#421 by GitHub stars
AlphaGBM/skills
Bring realtime market data and research workflows into Claude Code, Cursor & beyond — 29 open-source Skills for stocks, options and commodities.
History
Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.
View weekly changes
skills/commodities/alphagbm-chokepoint/SKILL.md
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skills/core/alphagbm-investment-review/SKILL.md
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skills/core/alphagbm-news-impact/SKILL.md
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skills/core/alphagbm-options-research/SKILL.md
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skills/core/alphagbm-report-breakdown/SKILL.md
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skills/core/alphagbm-stock-research/SKILL.md
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skills/digital-assets/alphagbm-polymarket/SKILL.md
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skills/options/alphagbm-bps-backtest/SKILL.md
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skills/options/alphagbm-earnings-crush/SKILL.md
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skills/options/alphagbm-greeks/SKILL.md
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skills/options/alphagbm-hedge-advisor/SKILL.md
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skills/options/alphagbm-iv-rank/SKILL.md
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skills/options/alphagbm-options-score/SKILL.md
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skills/options/alphagbm-options-strategy/SKILL.md
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skills/options/alphagbm-pnl-simulator/SKILL.md
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skills/options/alphagbm-unusual-activity/SKILL.md
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skills/options/alphagbm-vol-smile/SKILL.md
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skills/options/alphagbm-vol-surface/SKILL.md
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skills/stocks/alphagbm-dca-plan/SKILL.md
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skills/stocks/alphagbm-dividend-strategy/SKILL.md
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skills/stocks/alphagbm-etf-strategy/SKILL.md
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skills/stocks/alphagbm-grid-plan/SKILL.md
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skills/stocks/alphagbm-market-sentiment/SKILL.md
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skills/stocks/alphagbm-momentum-following/SKILL.md
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skills/stocks/alphagbm-research-reader/SKILL.md
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skills/stocks/alphagbm-smart-money/SKILL.md
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skills/stocks/alphagbm-stock-analysis/SKILL.md
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Heat
6.7- Star growth, last 7 daysAhead of 95% of tracked repositories
- Star growth, last 24 hoursAhead of 95% of tracked repositories
- Rank movement, last 7 daysAhead of 74% of tracked repositories
- Commits, last 30 daysAhead of 0% of tracked repositories
- Accounts asking about it through MCP, last 7 daysNot available
- Hot votes this weekAhead of 0% of tracked repositories
Heat never changes the order by stars, and sponsorship never touches it. How heat works
Stars over time
Individual skills in this repository
- alphagbm-chokepoint
skills/commodities/alphagbm-chokepoint - alphagbm-investment-review
skills/core/alphagbm-investment-review - alphagbm-news-impact
skills/core/alphagbm-news-impact - alphagbm-options-research
skills/core/alphagbm-options-research - alphagbm-report-breakdown
skills/core/alphagbm-report-breakdown - alphagbm-stock-research
skills/core/alphagbm-stock-research - alphagbm-polymarket
skills/digital-assets/alphagbm-polymarket - alphagbm-bps-backtest
skills/options/alphagbm-bps-backtest - alphagbm-earnings-crush
skills/options/alphagbm-earnings-crush - alphagbm-greeks
skills/options/alphagbm-greeks - alphagbm-hedge-advisor
skills/options/alphagbm-hedge-advisor - alphagbm-iv-rank
skills/options/alphagbm-iv-rank - alphagbm-options-score
skills/options/alphagbm-options-score - alphagbm-options-strategy
skills/options/alphagbm-options-strategy - alphagbm-pnl-simulator
skills/options/alphagbm-pnl-simulator - alphagbm-unusual-activity
skills/options/alphagbm-unusual-activity - alphagbm-vol-smile
skills/options/alphagbm-vol-smile - alphagbm-vol-surface
skills/options/alphagbm-vol-surface - alphagbm-dca-plan
skills/stocks/alphagbm-dca-plan - alphagbm-dividend-strategy
skills/stocks/alphagbm-dividend-strategy - alphagbm-etf-strategy
skills/stocks/alphagbm-etf-strategy - alphagbm-grid-plan
skills/stocks/alphagbm-grid-plan - alphagbm-market-sentiment
skills/stocks/alphagbm-market-sentiment - alphagbm-momentum-following
skills/stocks/alphagbm-momentum-following - alphagbm-research-reader
skills/stocks/alphagbm-research-reader - alphagbm-smart-money
skills/stocks/alphagbm-smart-money - alphagbm-stock-analysis
skills/stocks/alphagbm-stock-analysis
How it works
alphagbm-chokepoint
When your agent uses it
Supply-chain Bottlenecks reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
50 tokensAlways available to the agent
SKILL.md
248 tokensWhen the skill is activated
Reference documents
1,862 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,240 bytesreferences/legacy.mddoc7,569 bytes
Read SKILL.md
# Supply-chain Bottlenecks
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-investment-review
When your agent uses it
Compare research records you provide to see changes in facts, scores and judgments. Use when the user asks to investment review with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
54 tokensAlways available to the agent
SKILL.md
538 tokensWhen the skill is activated
Reference documents
1,207 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
- Investment review
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore2,699 bytesSKILL.mdcore2,699 bytesagents/openai.yamlasset350 bytesreferences/access.mddoc2,461 bytesreferences/investment-review.mddoc3,620 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Investment Review
Compare research records you provide to see changes in facts, scores and judgments.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Read references/investment-review.md. Ask the user for two authorized local JSON results and replace the file placeholders with quoted absolute paths. Compare matching identities, versions, observation dates and units only. This runs locally without a key, API calls, account-history access or a new allowance charge. Do not interpret an unverified file hash as proof of provenance, a changed price as a correct thesis, or the comparison as a trading-performance backtest. Obtaining new stock/options evidence separately still needs permission and account allowance.
## Run
View the complete source on GitHubalphagbm-news-impact
When your agent uses it
Separate reported claims from impact inferences, with related assets and next checkpoints. Use when the user asks to news impact with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
53 tokensAlways available to the agent
SKILL.md
597 tokensWhen the skill is activated
Reference documents
1,773 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore3,021 bytesSKILL.mdcore3,021 bytesagents/openai.yamlasset458 bytesreferences/access.mddoc2,461 bytesreferences/editorial-routing.mddoc2,566 bytesreferences/news-impact.mddoc3,753 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# News Impact
Separate reported claims from impact inferences, with related assets and next checkpoints.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Read references/news-impact.md. First list published news with `research --collection news --view news --lang en --limit 5` (or zh), then select the returned slug and revision. Replace the angle-bracket placeholders before running; never execute them literally. Separate reported claims, editorial inferences and verification gaps. This is not independent verification of an arbitrary web claim. The news-impact.v1 backend must be deployed; report unavailable without inventing an answer when unsupported. Read references/editorial-routing.md before selecting the next command. Check the returned workflow, language and published revision. A type mismatch needs the declared workflow, not a retry; a changed revision must be re-read. Never silently switch to paid analysis.
## Run
View the complete source on GitHubalphagbm-options-research
When your agent uses it
Compare option candidates and score components, with funding, expiry and assignment risks. Use when the user asks to options strategies with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
54 tokensAlways available to the agent
SKILL.md
648 tokensWhen the skill is activated
Reference documents
473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore3,292 bytesSKILL.mdcore3,292 bytesagents/openai.yamlasset270 bytesreferences/access.mddoc2,461 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Options Strategies
Compare option candidates and score components, with funding, expiry and assignment risks.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Ask for ticker, strategy preference and expiry if relevant. Use an explicit expiry when requested. Compare only returned candidates; a missing cash requirement or multiplier is unknown, not zero. Explain assignment, downside and event risk. Never infer a live executable fill or a complete multi-leg strategy from a single-leg candidate list. For the option-strategies.v1 rollout, add --workflow --lang zh (or en). A public, key-free preflight checks server support before the allowance-consuming request. Preserve resultId, scorerSourceRevision, partial status, missingData and riskFlags. Group candidates by strategy, never rank different strategy scores together or call them profit probabilities. Show reference capital, expiry breakeven and downside from economics when available; missing economics is unknown. The multiplier is a market-configuration assumption, not a verified deliverable. The USD $1-per-side fee is an illustrative assumption, not a broker quote or an all-in cost; fees and slippage are not included in the score. Short calls do not assume the user owns shares, and uncovered loss is unlimited. Do not present unknown quote times as live or replace them with generatedAt. Present data limitations in the user's language. This opt-in format requires the matching backend deployment; the default legacy command remains available.
## Run
View the complete source on GitHubalphagbm-report-breakdown
When your agent uses it
Extract report views, ratings, key assumptions and risks with sources and dates. Use when the user asks to research report breakdown with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
55 tokensAlways available to the agent
SKILL.md
634 tokensWhen the skill is activated
Reference documents
1,747 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore3,270 bytesSKILL.mdcore3,270 bytesagents/openai.yamlasset536 bytesreferences/access.mddoc2,461 bytesreferences/editorial-routing.mddoc2,566 bytesreferences/report-breakdown.mddoc3,624 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Research Report Breakdown
Extract report views, ratings, key assumptions and risks with sources and dates.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Read references/report-breakdown.md. Discover original reports with `research --collection research --lang en --limit 5` or institutional summaries with `research --collection news --view research --lang en --limit 5` (or zh). Select a returned slug and revision and replace the placeholders. Preserve institution, original rating, dates, currency, assumptions and limitations. Original research may include the full published reader; institutional reports return only the published summary, not private PDFs. The report-breakdown.v1 backend must be deployed; an unavailable response is not permission to fabricate a report. Read references/editorial-routing.md before selecting the next command. Check the returned workflow, language and published revision. A type mismatch needs the declared workflow, not a retry; a changed revision must be re-read. Never silently switch to paid analysis.
## Run
View the complete source on GitHubalphagbm-stock-research
When your agent uses it
Connect fundamentals, sentiment and risk to supporting and opposing evidence. Use when the user asks to stock opportunities with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
51 tokensAlways available to the agent
SKILL.md
583 tokensWhen the skill is activated
Reference documents
1,207 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
- Investment review
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore2,954 bytesSKILL.mdcore2,954 bytesagents/openai.yamlasset270 bytesreferences/access.mddoc2,461 bytesreferences/investment-review.mddoc3,620 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Stock Opportunities
Connect fundamentals, sentiment and risk to supporting and opposing evidence.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Identify the ticker and market suffix, and agree the research style. One explicit user-authorized research request is the starting point. Use only returned fundamentals, report and risk fields; no invented peer comparisons. The legacy risk.score is not the homepage opportunity score. For the stock-opportunities.v1 rollout, add --workflow --lang zh (or en). The runner first checks the public workflow contract without a key; unsupported servers receive no analysis request. Preserve partial status, resultId, scoring version and missingData. Do not retry a charged request automatically or claim the result was saved to user history. This opt-in format needs the matching backend deployment; the default legacy command remains available.
## Run
View the complete source on GitHubalphagbm-polymarket
When your agent uses it
Prediction Markets reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
46 tokensAlways available to the agent
SKILL.md
241 tokensWhen the skill is activated
Reference documents
781 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,228 bytesreferences/legacy.mddoc3,544 bytes
Read SKILL.md
# Prediction Markets
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-bps-backtest
When your agent uses it
Put-spread Backtest reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
50 tokensAlways available to the agent
SKILL.md
248 tokensWhen the skill is activated
Reference documents
1,656 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,232 bytesreferences/legacy.mddoc5,588 bytes
Read SKILL.md
# Put-spread Backtest
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-earnings-crush
When your agent uses it
Earnings IV Crush reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
50 tokensAlways available to the agent
SKILL.md
246 tokensWhen the skill is activated
Reference documents
1,142 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,230 bytesreferences/legacy.mddoc4,339 bytes
Read SKILL.md
# Earnings IV Crush
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-greeks
When your agent uses it
Greeks reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
46 tokensAlways available to the agent
SKILL.md
240 tokensWhen the skill is activated
Reference documents
1,723 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,200 bytesreferences/legacy.mddoc5,775 bytes
Read SKILL.md
# Greeks
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-hedge-advisor
When your agent uses it
Hedging Scenarios reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
51 tokensAlways available to the agent
SKILL.md
248 tokensWhen the skill is activated
Reference documents
1,462 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,229 bytesreferences/legacy.mddoc5,146 bytes
Read SKILL.md
# Hedging Scenarios
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-iv-rank
When your agent uses it
Volatility Snapshot via the published AlphaGBM interface. Use for this focused function, not as a promise that every website API accepts API keys.
What it loads into context
Frontmatter
42 tokensAlways available to the agent
SKILL.md
235 tokensWhen the skill is activated
Reference documents
473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore1,120 bytesSKILL.mdcore1,120 bytesagents/openai.yamlasset214 bytesreferences/access.mddoc2,461 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Volatility Snapshot
Read [access and evidence rules](references/access.md) first. This package is a focused function; full research workflows are listed in the repository catalogue. Its runner is self-contained and requires only Python 3.9+.
```bash
python3 "<skill-dir>/scripts/run.py" snapshot NVDA
```
Requires ALPHAGBM_API_KEY. If the command uses --confirm-usage, first obtain approval to use the shared account allowance. Snapshot reads do not consume analysis credits, but still require account access.
Return only the successful API response, with its original asset identity, dates, units, missing-data flags and score type. Stock risk scores are not opportunity scores. Volatility fields can be missing; do not turn a snapshot into a fabricated 252-day IV Rank. Option candidates are not guaranteed fills or trade instructions. Never fall back silently to demo data. Nonzero exit must be surfaced as an error.View the complete source on GitHubalphagbm-options-score
When your agent uses it
Options Score via the published AlphaGBM interface. Use for this focused function, not as a promise that every website API accepts API keys.
What it loads into context
Frontmatter
39 tokensAlways available to the agent
SKILL.md
239 tokensWhen the skill is activated
Reference documents
473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore1,139 bytesSKILL.mdcore1,139 bytesagents/openai.yamlasset208 bytesreferences/access.mddoc2,461 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Options Score
Read [access and evidence rules](references/access.md) first. This package is a focused function; full research workflows are listed in the repository catalogue. Its runner is self-contained and requires only Python 3.9+.
```bash
python3 "<skill-dir>/scripts/run.py" options NVDA --limit 3 --confirm-usage
```
Requires ALPHAGBM_API_KEY. If the command uses --confirm-usage, first obtain approval to use the shared account allowance. Snapshot reads do not consume analysis credits, but still require account access.
Return only the successful API response, with its original asset identity, dates, units, missing-data flags and score type. Stock risk scores are not opportunity scores. Volatility fields can be missing; do not turn a snapshot into a fabricated 252-day IV Rank. Option candidates are not guaranteed fills or trade instructions. Never fall back silently to demo data. Nonzero exit must be surfaced as an error.View the complete source on GitHubalphagbm-options-strategy
When your agent uses it
Strategy Builder reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
46 tokensAlways available to the agent
SKILL.md
241 tokensWhen the skill is activated
Reference documents
1,550 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,230 bytesreferences/legacy.mddoc5,898 bytes
Read SKILL.md
# Strategy Builder
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-pnl-simulator
When your agent uses it
Payoff Simulation reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
48 tokensAlways available to the agent
SKILL.md
244 tokensWhen the skill is activated
Reference documents
1,714 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,229 bytesreferences/legacy.mddoc5,900 bytes
Read SKILL.md
# Payoff Simulation
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-unusual-activity
When your agent uses it
Unusual Options Activity reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
49 tokensAlways available to the agent
SKILL.md
246 tokensWhen the skill is activated
Reference documents
771 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,246 bytesreferences/legacy.mddoc3,255 bytes
Read SKILL.md
# Unusual Options Activity
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-vol-smile
When your agent uses it
Volatility Smile reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
47 tokensAlways available to the agent
SKILL.md
243 tokensWhen the skill is activated
Reference documents
1,304 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,223 bytesreferences/legacy.mddoc4,621 bytes
Read SKILL.md
# Volatility Smile
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-vol-surface
When your agent uses it
Volatility Surface reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
47 tokensAlways available to the agent
SKILL.md
243 tokensWhen the skill is activated
Reference documents
1,496 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,229 bytesreferences/legacy.mddoc4,973 bytes
Read SKILL.md
# Volatility Surface
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-dca-plan
When your agent uses it
Build a contribution schedule and calculate a supplied price-path cost basis without predicting returns. Use when the user asks to dollar-cost averaging with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
56 tokensAlways available to the agent
SKILL.md
471 tokensWhen the skill is activated
Reference documents
473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore2,336 bytesSKILL.mdcore2,336 bytesagents/openai.yamlasset280 bytesreferences/access.mddoc2,461 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Dollar-Cost Averaging
Build a contribution schedule and calculate a supplied price-path cost basis without predicting returns.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Provide contribution amount, frequency and number of periods. An optional comma-separated price path is treated as supplied history only; the returned average cost is not a forecast. The result does not predict returns, choose a security or place recurring orders. Confirm the assumptions before using the shared allowance.
## Run
View the complete source on GitHubalphagbm-dividend-strategy
When your agent uses it
Score Hong Kong and China A-share dividend candidates across yield quality, durability, cash flow, valuation and momentum. Use when the user asks to dividend strategy with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
531 tokensWhen the skill is activated
Reference documents
473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore2,565 bytesSKILL.mdcore2,565 bytesagents/openai.yamlasset292 bytesreferences/access.mddoc2,461 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Dividend Strategy
Score Hong Kong and China A-share dividend candidates across yield quality, durability, cash flow, valuation and momentum.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Confirm the ticker and market suffix: use .HK for Hong Kong or .SS/.SZ for China A-shares. This account-backed request consumes the shared stock-research allowance. The dividend-opportunities.v1 contract scores yield quality, sustainability, cash-flow coverage, quality, growth, valuation and momentum; it is not a yield-only ranking or a return forecast. Preserve ready versus partial status, factor contributions, sources, quote timing and missingData. If dividend history, payout coverage or relative valuation is unavailable, report the gap and do not turn observedOnlyScore into a complete score. A high current yield may be one-off or unsustainable. Do not present the result as a trade instruction or promise of income.
## Run
View the complete source on GitHubalphagbm-etf-strategy
When your agent uses it
Score ETF trend, risk, liquidity, cost and available tracking and theme signals while preserving data gaps. Use when the user asks to etf strategy with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
61 tokensAlways available to the agent
SKILL.md
512 tokensWhen the skill is activated
Reference documents
473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore2,477 bytesSKILL.mdcore2,477 bytesagents/openai.yamlasset285 bytesreferences/access.mddoc2,461 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# ETF Strategy
Score ETF trend, risk, liquidity, cost and available tracking and theme signals while preserving data gaps.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Use a plain, non-leveraged ETF ticker. The workflow scores observed trend, risk, liquidity, cost, tracking, theme and event inputs only when supplied by the provider. Leveraged and inverse ETFs are rejected. Expense ratio may be available from the provider; tracking error, theme and event signals may remain missing. Preserve status and missingData, and never fill them from model memory or a demo. The strategy-workflows.v1 backend must be deployed; unsupported servers receive no paid request.
## Run
View the complete source on GitHubalphagbm-grid-plan
When your agent uses it
Build an auditable grid from a price range, current price and capital. Use when the user asks to grid plan with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
53 tokensAlways available to the agent
SKILL.md
497 tokensWhen the skill is activated
Reference documents
473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore2,294 bytesSKILL.mdcore2,294 bytesagents/openai.yamlasset264 bytesreferences/access.mddoc2,461 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Grid Plan
Build an auditable grid from a price range, current price and capital.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Provide a price range, current price, capital and grid count. The result is a transparent parameter plan with explicit levels and allocation, not a promise that orders will fill. It does not model slippage, fees, gap risk or future prices. Confirm the assumptions before using the shared allowance and review suitability separately.
## Run
View the complete source on GitHubalphagbm-market-sentiment
When your agent uses it
Market Sentiment reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
What it loads into context
Frontmatter
48 tokensAlways available to the agent
SKILL.md
244 tokensWhen the skill is activated
Reference documents
791 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore1,230 bytesreferences/legacy.mddoc3,384 bytes
Read SKILL.md
# Market Sentiment
This is a focused **reference package**, not a live API integration. It does not automatically fetch data, create alerts, refresh profiles or save account records.
1. Explain the method when the user explicitly asks about it. Treat [legacy notes](references/legacy.md) as historical implementation material, not a current access contract.
2. Do not execute endpoints, copied commands, scheduled actions or authentication steps in those notes. Do not request browser credentials to bypass a denied API key.
3. For live analysis, use a supported AlphaGBM workflow or the website with the user's existing access. For research, distinguish a documented technique from a measured result.
4. Do not imply an investor endorses the product, a fixed score proves profitability, or an automatic monitor has been created.
中文:这是单项研究方法参考,不代表对应的外部接口已开放。需要真实分析时,请使用已支持的工作流或网站功能。View the complete source on GitHubalphagbm-momentum-following
When your agent uses it
Score observed price trend, confirmation, risk and participation without forecasting a price. Use when the user asks to momentum following with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
55 tokensAlways available to the agent
SKILL.md
488 tokensWhen the skill is activated
Reference documents
473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore2,457 bytesSKILL.mdcore2,457 bytesagents/openai.yamlasset280 bytesreferences/access.mddoc2,461 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Momentum Following
Score observed price trend, confirmation, risk and participation without forecasting a price.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Use an explicit supported ticker. The workflow scores observed returns, moving-average confirmation, volatility/drawdown and volume participation from the configured market-data service. It is not a price forecast, a probability of profit or a trade instruction. Preserve quoteTime, sources, ready versus partial status and missingData. Compare the next result for the same ticker instead of treating one score as a promise. The strategy-workflows.v1 backend must be deployed; unsupported servers receive no paid request.
## Run
View the complete source on GitHubalphagbm-research-reader
When your agent uses it
Find published research and institutional views with sources, dates and related assets. Use when the user asks to research reports with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
53 tokensAlways available to the agent
SKILL.md
761 tokensWhen the skill is activated
Reference documents
3,243 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
- Investment review
- Report breakdown
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore3,770 bytesSKILL.mdcore3,770 bytesagents/openai.yamlasset518 bytesreferences/access.mddoc2,461 bytesreferences/editorial-routing.mddoc2,566 bytesreferences/investment-review.mddoc3,620 bytesreferences/news-impact.mddoc3,753 bytesreferences/report-breakdown.mddoc3,624 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Research Reports
Find published research and institutional views with sources, dates and related assets.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Start with a public catalogue list. `--collection research` lists original research; `--collection news --view research` selects institutional views; `--collection news --view news` selects news. Use `--query` for a title/ticker keyword and `--lang zh` for Chinese. Read a selected public article with `--slug <returned-slug>`. Attribute ratings and targets to the institution; a missing original rating stays missing. For news impact, read references/news-impact.md and run `news --slug <returned-news-slug> --revision <published-revision> --lang zh` (or en). This staged, key-free command reads existing public news only; it requires the news-impact.v1 backend and never starts paid analysis. On an unsupported server report unavailable rather than inventing a result. Read references/editorial-routing.md before selecting the next command. Check the returned workflow, language and published revision. A type mismatch needs the declared workflow, not a retry; a changed revision must be re-read. Never silently switch to paid analysis.
## Run
View the complete source on GitHubalphagbm-smart-money
When your agent uses it
Aggregate user-supplied disclosed buys and sells by source without automatically copying trades. Use when the user asks to smart money tracking with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
What it loads into context
Frontmatter
55 tokensAlways available to the agent
SKILL.md
551 tokensWhen the skill is activated
Reference documents
986 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Before running
- Run
- Deliver the result
- Example request
- Transaction input contract
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore2,732 bytesSKILL.mdcore2,732 bytesSKILL.mdcore2,732 bytesSKILL.mdcore2,732 bytesSKILL.mdcore2,732 bytesagents/openai.yamlasset291 bytesreferences/access.mddoc2,461 bytesreferences/transactions.mddoc2,133 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Smart Money Tracking
Aggregate user-supplied disclosed buys and sells by source without automatically copying trades.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
## Before running
Read [access and evidence rules](references/access.md). Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve `<skill-dir>` to the directory containing this file.
Provide a local JSON array of disclosed transactions. The workflow aggregates the records by side and source; it does not decide who is smart, infer undisclosed positions, or copy a trade. Check the disclosure date, source quality and reporting lag. The transaction file is sent in the authenticated request and may consume the shared allowance; do not include secrets or private personal data.
## Run
View the complete source on GitHubalphagbm-stock-analysis
When your agent uses it
Stock Analysis via the published AlphaGBM interface. Use for this focused function, not as a promise that every website API accepts API keys.
What it loads into context
Frontmatter
39 tokensAlways available to the agent
SKILL.md
235 tokensWhen the skill is activated
Reference documents
473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Scripts executed separately
scripts/review_engine.pyscripts/run.py
Files
SKILL.mdcore1,130 bytesSKILL.mdcore1,130 bytesagents/openai.yamlasset211 bytesreferences/access.mddoc2,461 bytesscripts/review_engine.pyruns16,028 bytesscripts/run.pyruns39,390 bytes
Read SKILL.md
# Stock Analysis
Read [access and evidence rules](references/access.md) first. This package is a focused function; full research workflows are listed in the repository catalogue. Its runner is self-contained and requires only Python 3.9+.
```bash
python3 "<skill-dir>/scripts/run.py" stock NVDA --confirm-usage
```
Requires ALPHAGBM_API_KEY. If the command uses --confirm-usage, first obtain approval to use the shared account allowance. Snapshot reads do not consume analysis credits, but still require account access.
Return only the successful API response, with its original asset identity, dates, units, missing-data flags and score type. Stock risk scores are not opportunity scores. Volatility fields can be missing; do not turn a snapshot into a fabricated 252-day IV Rank. Option candidates are not guaranteed fills or trade instructions. Never fall back silently to demo data. Nonzero exit must be surfaced as an error.View the complete source on GitHubSkills included
- alphagbm-chokepoint
- alphagbm-investment-review
- alphagbm-news-impact
- alphagbm-options-research
- alphagbm-report-breakdown
- alphagbm-stock-research
- alphagbm-polymarket
- alphagbm-bps-backtest
- alphagbm-earnings-crush
- alphagbm-greeks
- alphagbm-hedge-advisor
- alphagbm-iv-rank
- alphagbm-options-score
- alphagbm-options-strategy
- alphagbm-pnl-simulator
- alphagbm-unusual-activity
- alphagbm-vol-smile
- alphagbm-vol-surface
- alphagbm-dca-plan
- alphagbm-dividend-strategy
- alphagbm-etf-strategy
- alphagbm-grid-plan
- alphagbm-market-sentiment
- alphagbm-momentum-following
- alphagbm-research-reader
- alphagbm-smart-money
- alphagbm-stock-analysis
Repository facts
- License
- MIT
- Created
- Apr 6, 2026
- Last pushed
- Oct 6, 2026
- Forks
- 333
- Open issues
- 0
- Project activity
- Active
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- #402 on Oct 20
- #405 on Oct 21
- #403 on Oct 22
- #400 on Oct 23
- #396 on Oct 24
- #414 on Oct 25
- #418 on Oct 28
- #419 on Oct 29
- #421 from Oct 30 to Oct 31
Before you install
Local scriptsAutomated signals, not a guarantee
Checked Nov 2, 2026
Risk level changed on Oct 27, 2026
No longer found: Access to secrets
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 18 notes
skills/core/alphagbm-investment-review/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/core/alphagbm-news-impact/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/core/alphagbm-options-research/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/core/alphagbm-report-breakdown/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/core/alphagbm-stock-research/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/options/alphagbm-iv-rank/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/options/alphagbm-options-score/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-dca-plan/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-dividend-strategy/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-etf-strategy/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-grid-plan/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-momentum-following/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-research-reader/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-smart-money/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-smart-money/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-smart-money/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-smart-money/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/stocks/alphagbm-stock-analysis/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- No dangerous shell commands
- 41 network referencesin 21 files: alphagbm-investment-review/scripts/run.py:102 alphagbm-news-impact/scripts/run.py:102 alphagbm-options-research/scripts/run.py:102 and 18 more
- No hidden or obfuscated text
- No instruction overrides
- 30 executable scriptsin 30 files: alphagbm-investment-review/scripts/review_engine.py:1 alphagbm-investment-review/scripts/run.py:1 alphagbm-news-impact/scripts/review_engine.py:1 and 27 more
- 61 references to secretsin 31 files: alphagbm-investment-review/scripts/run.py:103 alphagbm-news-impact/scripts/run.py:103 alphagbm-options-research/scripts/run.py:103 and 28 more
- No requests to vote in Hot
Show all 132 signals by file
skills/commodities/alphagbm-chokepoint/references/legacy.md
- Network accessline 154
*Powered by [AlphaGBM](https://alphagbm.com) — Real-data options & research intelligence.*
skills/core/alphagbm-investment-review/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/core/alphagbm-investment-review/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/core/alphagbm-news-impact/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/core/alphagbm-news-impact/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/core/alphagbm-options-research/SKILL.md
- Access to secretsline 24
[Credential-related source omitted]
skills/core/alphagbm-options-research/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/core/alphagbm-options-research/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/core/alphagbm-report-breakdown/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/core/alphagbm-report-breakdown/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/core/alphagbm-stock-research/SKILL.md
- Access to secretsline 24
[Credential-related source omitted]
skills/core/alphagbm-stock-research/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/core/alphagbm-stock-research/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/options/alphagbm-greeks/references/legacy.md
skills/options/alphagbm-iv-rank/SKILL.md
- Access to secretsline 14
[Credential-related source omitted]
skills/options/alphagbm-iv-rank/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/options/alphagbm-iv-rank/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/options/alphagbm-options-score/SKILL.md
- Access to secretsline 14
[Credential-related source omitted]
skills/options/alphagbm-options-score/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/options/alphagbm-options-score/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/options/alphagbm-options-strategy/references/legacy.md
skills/options/alphagbm-pnl-simulator/references/legacy.md
skills/options/alphagbm-vol-smile/references/legacy.md
skills/options/alphagbm-vol-surface/references/legacy.md
skills/stocks/alphagbm-dca-plan/SKILL.md
- Access to secretsline 24
[Credential-related source omitted]
skills/stocks/alphagbm-dca-plan/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/stocks/alphagbm-dca-plan/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/stocks/alphagbm-dividend-strategy/SKILL.md
- Access to secretsline 24
[Credential-related source omitted]
skills/stocks/alphagbm-dividend-strategy/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/stocks/alphagbm-dividend-strategy/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/stocks/alphagbm-etf-strategy/SKILL.md
- Access to secretsline 24
[Credential-related source omitted]
skills/stocks/alphagbm-etf-strategy/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/stocks/alphagbm-etf-strategy/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/stocks/alphagbm-grid-plan/SKILL.md
- Access to secretsline 24
[Credential-related source omitted]
skills/stocks/alphagbm-grid-plan/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/stocks/alphagbm-grid-plan/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/stocks/alphagbm-momentum-following/SKILL.md
- Access to secretsline 24
[Credential-related source omitted]
skills/stocks/alphagbm-momentum-following/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/stocks/alphagbm-momentum-following/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/stocks/alphagbm-research-reader/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/stocks/alphagbm-research-reader/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/stocks/alphagbm-smart-money/SKILL.md
- Access to secretsline 24
[Credential-related source omitted]
skills/stocks/alphagbm-smart-money/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/stocks/alphagbm-smart-money/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
skills/stocks/alphagbm-stock-analysis/SKILL.md
- Access to secretsline 14
[Credential-related source omitted]
skills/stocks/alphagbm-stock-analysis/scripts/review_engine.py
- Executable scriptline 1
Executable file
skills/stocks/alphagbm-stock-analysis/scripts/run.py
- Executable scriptline 1
Executable file - Network accessline 102
def base_url(): value = os.environ.get("ALPHAGBM_BASE_URL", "https://www.alphagbm.com").rstrip("/") - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Access to secretsline 119
[Credential-related source omitted]
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Declared and executed: 15 facts
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-investment-review/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-news-impact/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-options-research/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-report-breakdown/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-stock-research/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-iv-rank/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-options-score/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-dca-plan/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-dividend-strategy/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-etf-strategy/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-grid-plan/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-momentum-following/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-research-reader/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-smart-money/scripts/run.py:103High confidence
- Contacts www.alphagbm.com, which SKILL.md, the README and the plugin manifest do not mentionalphagbm-stock-analysis/scripts/run.py:103High confidence
Signal confidence: 132 facts
- Network access in prose of documentationalphagbm-chokepoint/references/legacy.md:154Low confidence
- Executable script in prose of a file that runsalphagbm-investment-review/scripts/review_engine.py:1Low confidence
- Executable script in prose of a file that runsalphagbm-investment-review/scripts/run.py:1Low confidence
- Network access in code of a file that runsalphagbm-investment-review/scripts/run.py:102High confidence
- Network access in code of a file that runsalphagbm-investment-review/scripts/run.py:103High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-investment-review/scripts/run.py:103Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-investment-review/scripts/run.py:117Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-investment-review/scripts/run.py:119Low confidence
- Executable script in prose of a file that runsalphagbm-news-impact/scripts/review_engine.py:1Low confidence
- Executable script in prose of a file that runsalphagbm-news-impact/scripts/run.py:1Low confidence
- Network access in code of a file that runsalphagbm-news-impact/scripts/run.py:102High confidence
- Network access in code of a file that runsalphagbm-news-impact/scripts/run.py:103High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-news-impact/scripts/run.py:103Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-news-impact/scripts/run.py:117Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-news-impact/scripts/run.py:119Low confidence
- Access to secrets in prose of the SKILL.mdalphagbm-options-research/SKILL.md:24Low confidence
- Executable script in prose of a file that runsalphagbm-options-research/scripts/review_engine.py:1Low confidence
- Executable script in prose of a file that runsalphagbm-options-research/scripts/run.py:1Low confidence
- Network access in code of a file that runsalphagbm-options-research/scripts/run.py:102High confidence
- Network access in code of a file that runsalphagbm-options-research/scripts/run.py:103High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-options-research/scripts/run.py:103Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-options-research/scripts/run.py:117Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-options-research/scripts/run.py:119Low confidence
- Executable script in prose of a file that runsalphagbm-report-breakdown/scripts/review_engine.py:1Low confidence
- Executable script in prose of a file that runsalphagbm-report-breakdown/scripts/run.py:1Low confidence
- Network access in code of a file that runsalphagbm-report-breakdown/scripts/run.py:102High confidence
- Network access in code of a file that runsalphagbm-report-breakdown/scripts/run.py:103High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-report-breakdown/scripts/run.py:103Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-report-breakdown/scripts/run.py:117Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-report-breakdown/scripts/run.py:119Low confidence
- Access to secrets in prose of the SKILL.mdalphagbm-stock-research/SKILL.md:24Low confidence
- Executable script in prose of a file that runsalphagbm-stock-research/scripts/review_engine.py:1Low confidence
- Executable script in prose of a file that runsalphagbm-stock-research/scripts/run.py:1Low confidence
- Network access in code of a file that runsalphagbm-stock-research/scripts/run.py:102High confidence
- Network access in code of a file that runsalphagbm-stock-research/scripts/run.py:103High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-stock-research/scripts/run.py:103Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-stock-research/scripts/run.py:117Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-stock-research/scripts/run.py:119Low confidence
- Network access in prose of documentationalphagbm-greeks/references/legacy.md:19Low confidence
- Access to secrets in prose of documentationalphagbm-greeks/references/legacy.md:19Low confidence
- Network access in prose of documentationalphagbm-greeks/references/legacy.md:20Low confidence
- Access to secrets in prose of the SKILL.mdalphagbm-iv-rank/SKILL.md:14Low confidence
- Executable script in prose of a file that runsalphagbm-iv-rank/scripts/review_engine.py:1Low confidence
- Executable script in prose of a file that runsalphagbm-iv-rank/scripts/run.py:1Low confidence
- Network access in code of a file that runsalphagbm-iv-rank/scripts/run.py:102High confidence
- Network access in code of a file that runsalphagbm-iv-rank/scripts/run.py:103High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-iv-rank/scripts/run.py:103Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-iv-rank/scripts/run.py:117Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuealphagbm-iv-rank/scripts/run.py:119Low confidence
- Access to secrets in prose of the SKILL.mdalphagbm-options-score/SKILL.md:14Low confidence
and 82 more
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md skills/commodities/alphagbm-chokepoint/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md skills/commodities/alphagbm-chokepoint/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/commodities/alphagbm-chokepoint/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/commodities/alphagbm-chokepoint/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/commodities/alphagbm-chokepoint/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/commodities/alphagbm-chokepoint/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.
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