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limingrui679-design/high-stakes-analytics-decision-lab
Build or review source-backed descriptive, diagnostic, predictive, and prescriptive analysis for consequential decisions. Use when an agent must profile and safely prepare uploaded data, turn a real dataset or research question into a reproducible study, investigate drivers without overstating causa
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Individual skills in this repository
- high-stakes-analytics-decision-lab
skills/high-stakes-analytics-decision-lab
How it works
high-stakes-analytics-decision-lab
When your agent uses it
Build or review source-backed descriptive, diagnostic, predictive, and prescriptive analysis for consequential decisions. Use when an agent must profile and safely prepare uploaded data, turn a real dataset or research question into a reproducible study, investigate drivers without overstating causality, validate a model, compare feasible actions under dependent uncertainty and tail risk, trace every parameter to evidence and approval, or produce an answer-first analytical report across health, business, finance, policy, engineering, operations, behavioral science, AI, or planning.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
1,841 tokensWhen the skill is activated
Reference documents
17,084 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Start here
- Route before choosing a method
- Evidence-gated workflow
- Start from a real dataset
- Select an executable module
- Decision layer
- Output contract
- Worked precedents
- Non-negotiable guardrails
Scripts executed separately
scripts/allocation_optimizer.pyscripts/analytics_router.pyscripts/data_quality.pyscripts/decision_engine.pyscripts/doctor.pyscripts/evidence_analysis.pyscripts/hsadl.pyscripts/init_case.pyscripts/prediction_validation.pyscripts/prepare_dataset.pyscripts/profile_dataset.pyscripts/quickstart_demo.pyscripts/report_visuals.pyscripts/route_question.pyscripts/run_case.pyscripts/validate_case.pyscripts/visual_system.py
Files
LICENSE.txtdoc1,106 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesSKILL.mdcore8,971 bytesagents/openai.yamlasset282 bytesassets/case-template.jsonasset7,834 bytesassets/data-contract-template.jsonasset945 bytesbundle-manifest.jsonasset6,404 bytesreferences/advanced-method-boundaries.mddoc2,935 bytesreferences/analytics-triad.mddoc2,643 bytesreferences/case-precedents.mddoc15,098 bytesreferences/case-schema.mddoc8,541 bytesreferences/data-quality-gate.mddoc8,197 bytesreferences/domain-playbooks.mddoc3,073 bytesreferences/editorial-visual-system.mddoc7,642 bytesreferences/method-domain-map.jsonasset1,883 bytesreferences/method-modules.mddoc5,242 bytesreferences/method-routing.mddoc4,927 bytesreferences/methodology.mddoc8,818 bytesreferences/provenance-contract.mddoc2,468 bytesreferences/real-evidence-workflow.mddoc3,083 bytesreferences/reporting-standard.mddoc2,372 bytesreferences/reproducibility-contract.mddoc2,358 bytesreferences/visual-report-system.mddoc5,168 bytesscripts/allocation_optimizer.pyruns15,634 bytesscripts/analytics_router.pyruns25,335 bytesscripts/data_quality.pyruns86,035 bytesscripts/decision_engine.pyruns115,391 bytesscripts/doctor.pyruns5,248 bytesscripts/evidence_analysis.pyruns16,652 bytesscripts/hsadl.pyruns2,517 bytesscripts/init_case.pyruns12,408 bytesscripts/prediction_validation.pyruns25,374 bytesscripts/prepare_dataset.pyruns1,596 bytesscripts/profile_dataset.pyruns1,252 bytesscripts/quickstart_demo.pyruns5,490 bytesscripts/report_visuals.pyruns43,522 bytesscripts/route_question.pyruns1,826 bytesscripts/run_case.pyruns1,752 bytesscripts/validate_case.pyruns995 bytesscripts/visual_system.pyruns12,056 bytes
Read SKILL.md
# High-Stakes Analytics & Decision Lab
Turn a real question into a defensible path from source evidence to analysis and,
only when justified, bounded action. Keep observation, diagnosis, prediction,
causal evidence, value judgments, and recommendation visibly separate.
## Start here
Run the environment audit before an executable workflow:
```bash
python3 scripts/hsadl.py doctor
```
Skills included
- high-stakes-analytics-decision-lab
Repository facts
- License
- MIT
- Created
- Jul 29, 2026
- Last pushed
- Oct 26, 2026
- Forks
- 121
- Open issues
- 0
- Project activity
- Active
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Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 4, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 11 notes
skills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/high-stakes-analytics-decision-lab/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- No dangerous shell commands
- No hidden or obfuscated text
- No instruction overrides
- 17 executable scriptsin 17 files: high-stakes-analytics-decision-lab/scripts/allocation_optimizer.py:1 high-stakes-analytics-decision-lab/scripts/analytics_router.py:1 high-stakes-analytics-decision-lab/scripts/data_quality.py:1 and 14 more
- No access to secrets
- No requests to vote in Hot
Show all 80 signals by file
skills/high-stakes-analytics-decision-lab/references/case-precedents.md
- Network accessline 5
| # | Case | Route | Capability path | Valid endpoint | |---:|---|---|---|---| | 01 | [Jersey City Bike Demand and Rebalancing Evidence](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data- - Network accessline 6
|---:|---|---|---|---| | 01 | [Jersey City Bike Demand and Rebalancing Evidence](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/01-bike-demand-operations.md) | descriptive - Network accessline 7
| 01 | [Jersey City Bike Demand and Rebalancing Evidence](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/01-bike-demand-operations.md) | descriptive / predictive / prescrip - Network accessline 8
| 02 | [Cross-City 311 Distribution Shift and Transfer Gate](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/02-cross-city-311-shift.md) | descriptive / diagnostic / decisio - Network accessline 9
| 03 | [Capacity-Constrained Marketing Pilot](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/03-bank-marketing-response.md) | descriptive / predictive / prescriptive | Anal - Network accessline 10
| 04 | [ACS Employment AI Temporal Transport and Audit](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/04-census-income-ai.md) | descriptive / predictive | AI and model val - Network accessline 11
| 05 | [Treasury Curve and Tail-Risk Decision Engine](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/05-treasury-risk-engineering.md) | descriptive / predictive / prescript - Network accessline 12
| 06 | [Human-in-the-Loop Complaint Triage Information System](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/06-cfpb-fintech-complaint-operations.md) | descriptive / predi - Network accessline 13
| 07 | [Commercial Real Estate Diligence Decision Product](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/07-commercial-real-estate-risk.md) | descriptive / diagnostic / pr - Network accessline 14
| 08 | [Wildfire Mitigation Evidence Allocation Under Uncertainty](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/08-wildfire-mitigation-under-uncertainty.md) | descriptive - Network accessline 15
| 09 | [SEC N-PORT Liquidity and Crowding Filing Review](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/09-sec-nport-filing-review.md) | descriptive / diagnostic / predicti - Network accessline 16
| 10 | [Social-Norm Field Experiment with Household-Clustered Inference](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/10-social-norm-field-experiment.md) | descriptive / - Network accessline 17
| 11 | [Population Health Risk Transport Across NHIS Cohorts](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/11-population-health-survival.md) | descriptive / predictive / - Network accessline 18
| 12 | [Opportunity Zone One-Year Policy Evidence Screen](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/12-opportunity-zone-policy-evaluation.md) | descriptive / diagnosti - Network accessline 19
| 13 | [Small-Sample Repeated-Measures Inference](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/13-behavioral-reading-experiment.md) | descriptive / inferential | Statisti - Network accessline 20
| 14 | [NHANES Mortality Transportability and Population Inequality](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/14-nhanes-population-transportability.md) | descriptive - Network accessline 21
| 15 | [Spatial Equity Planning with Transit and Site-Evidence Gates](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/main/examples/real-data-cases/cases/15-spatial-equity-planning.md) | descriptive / prescrip - Network accessline 28
- Reviewer signals: temporal holdout; simple baseline; fixed capacity; modeled pilot boundary. - Boundary: Rebalancing outcomes are modeled; no stockout, routing, labor, or achieved-service claim. - Full reproducible project: [bike-demand-o - Network accessline 30
- Full reproducible project: [bike-demand-operations](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/bike-demand-operations) - Network accessline 35
- Reviewer signals: ontology audit; distribution shift; transfer refusal; administrative-data boundary. - Boundary: Administrative shift audit only; requests are not latent need or service quality. - Full reproducible project: [cross-city-3 - Network accessline 37
- Full reproducible project: [cross-city-311-shift](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/cross-city-311-shift) - Network accessline 42
- Reviewer signals: leakage-safe timing; untouched test; capacity capture; randomized pilot requirement. - Boundary: Observed response concentration is not incremental lift, causal treatment effect, profit, or return on outreach. - Full rep - Network accessline 44
- Full reproducible project: [bank-marketing-response](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/bank-marketing-response) - Network accessline 49
- Reviewer signals: temporal transport; survey weights; protected-attribute audit; no consequential use. - Boundary: No eligibility, hiring, credit, benefits, or other consequential action. - Full reproducible project: [census-income-ai](ht - Network accessline 51
- Full reproducible project: [census-income-ai](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/census-income-ai) - Network accessline 56
- Reviewer signals: tail loss; coverage backtest; regime comparison; dependent market shocks. - Boundary: First-order duration omits convexity, security selection, costs, financing, taxes, liquidity, and future-regime uncertainty. - Full re - Network accessline 58
- Full reproducible project: [treasury-risk-engineering](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/treasury-risk-engineering) - Network accessline 63
- Reviewer signals: privacy minimization; calendar holdout; human review; do not deploy. - Boundary: The timely flag is not complaint merit, harm, resolution quality, company quality, or compliance; the 2022 model may not transport. - Full - Network accessline 65
- Full reproducible project: [cfpb-fintech-complaint-operations](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/cfpb-fintech-complaint-operations) - Network accessline 70
- Reviewer signals: transaction screening; financing sensitivity; diligence workflow; missing property evidence. - Boundary: The source does not establish arm's-length status, NOI, expenses, occupancy, condition, appraisal value, debt terms - Network accessline 72
- Full reproducible project: [commercial-real-estate-risk](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/commercial-real-estate-risk) - Network accessline 77
- Reviewer signals: scenario regret; tail exposure; feasibility gap; evidence request. - Boundary: No fires-prevented or acres-prevented estimate; mitigation action blocked pending effectiveness and feasibility evidence. - Full reproducible - Network accessline 79
- Full reproducible project: [wildfire-mitigation-under-uncertainty](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/wildfire-mitigation-under-uncertainty) - Network accessline 84
- Reviewer signals: transparent indicators; review capacity; filing lineage; fiduciary boundary. - Boundary: Filing review only; no expected-return, suitability, fund-quality, or investment recommendation. - Full reproducible project: [sec- - Network accessline 86
- Full reproducible project: [sec-nport-filing-review](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/sec-nport-filing-review) - Network accessline 91
- Reviewer signals: randomized assignment; household clustering; intent-to-treat; no new campaign authorization. - Boundary: Causal scope is the historical randomized experiment; no new campaign authorization. - Full reproducible project: [ - Network accessline 93
- Full reproducible project: [social-norm-field-experiment](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/social-norm-field-experiment) - Network accessline 98
- Reviewer signals: linked cohorts; temporal transport; workload screen; no individual clinical action. - Boundary: Population-risk validation only; no individual diagnosis, treatment, or clinical deployment. - Full reproducible project: [p - Network accessline 100
- Full reproducible project: [population-health-survival](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/population-health-survival) - Network accessline 105
- Reviewer signals: matched change; support diagnostics; control reuse; associational policy screen. - Boundary: Associational one-year screen; no causal effect because parallel trends are unavailable. - Full reproducible project: [opportun - Network accessline 107
- Full reproducible project: [opportunity-zone-policy-evaluation](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/opportunity-zone-policy-evaluation) - Network accessline 112
- Reviewer signals: within-participant contrast; small sample; clustered inference; generalization boundary. - Boundary: The public sample is small and does not establish downstream educational outcomes or an intervention effect. - Full rep - Network accessline 114
- Full reproducible project: [behavioral-reading-experiment](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/behavioral-reading-experiment) - Network accessline 119
- Reviewer signals: survey weights; external cohort check; calibration; population inequality. - Boundary: Population research only; no individual diagnosis or treatment. - Full reproducible project: [nhanes-population-transportability](htt - Network accessline 121
- Full reproducible project: [nhanes-population-transportability](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/nhanes-population-transportability) - Network accessline 126
- Reviewer signals: complete-case route; missingness sensitivity; observed transit proximity; site evidence gate. - Boundary: No site recommendation until parcel, zoning, network, cost, and community evidence is supplied. - Full reproducibl - Network accessline 128
- Full reproducible project: [spatial-equity-planning](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases/projects/spatial-equity-planning)
skills/high-stakes-analytics-decision-lab/references/editorial-visual-system.md
- Network accessline 20
This system draws general lessons from public research published by [McKinsey Global Publishing](https://www.mckinsey.com/featured-insights/year-in-review/year-in-charts), - Network accessline 21
[McKinsey Global Publishing](https://www.mckinsey.com/featured-insights/year-in-review/year-in-charts), - Network accessline 22
[McKinsey Global Institute](https://www.mckinsey.com/mgi/our-research/mckinsey-global-institute-2025-in-charts), - Network accessline 23
[BCG](https://www.bcg.com/publications/2026/global-wealth-growth-in-an-era-of-reordering), - Network accessline 24
[IBM Institute for Business Value](https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/business-trends-2025), - Network accessline 25
[Accenture Research](https://www.accenture.com/us-en/insights/pulse-of-change-january-2025), - Network accessline 26
and [Deloitte CFO Insights](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/finance/us-cfo-insights-data-visualization.pdf). It does not reproduce their brand assets, layouts, or proprietary artwork. - Network accessline 27
[Deloitte CFO Insights](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/finance/us-cfo-insights-data-visualization.pdf).
skills/high-stakes-analytics-decision-lab/references/method-routing.md
- Network accessline 69
For complete worked implementations, use the [real-data case index](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases). Select the case - Network accessline 70
[real-data case index](https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/tree/main/examples/real-data-cases). Select the case
skills/high-stakes-analytics-decision-lab/scripts/allocation_optimizer.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/analytics_router.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 509
"estimates and prescriptive decisions, with an optional diagnostic bridge." ) return f"""<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}" role="img" aria-labelledby="title desc - Network accessline 510
) return f"""<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}" role="img" aria-labelledby="title desc"> <title id="title">Question-to-analysis routing blueprint</title> - Network accessline 511
return f"""<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}" role="img" aria-labelledby="title desc">
skills/high-stakes-analytics-decision-lab/scripts/data_quality.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/decision_engine.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/doctor.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/evidence_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/hsadl.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/init_case.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/prediction_validation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/prepare_dataset.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/profile_dataset.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/quickstart_demo.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/report_visuals.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/route_question.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/run_case.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/validate_case.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/high-stakes-analytics-decision-lab/scripts/visual_system.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 288
note: str = "Synthetic demonstration · values shown directly", ) -> str: return f"""<svg xmlns="http://www.w3.org/2000/svg" width="{WIDTH}" height="{height}" viewBox="0 0 {WIDTH} {height}" role="img" aria-labelledby="title desc"> - Network accessline 289
) -> str: return f"""<svg xmlns="http://www.w3.org/2000/svg" width="{WIDTH}" height="{height}" viewBox="0 0 {WIDTH} {height}" role="img" aria-labelledby="title desc"> <title id="title">{escape(title_value)}</title> - Network accessline 290
return f"""<svg xmlns="http://www.w3.org/2000/svg" width="{WIDTH}" height="{height}" viewBox="0 0 {WIDTH} {height}" role="img" aria-labelledby="title desc">
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.
No combined signals at this commit.
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md skills/high-stakes-analytics-decision-lab/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md skills/high-stakes-analytics-decision-lab/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/high-stakes-analytics-decision-lab/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/high-stakes-analytics-decision-lab/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/high-stakes-analytics-decision-lab/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/high-stakes-analytics-decision-lab/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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