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Phelan164/codex-howto
Engineering-first OpenAI Codex guide: 14 modules, 9 installable skills, reproducible benchmarks, testing, review, orchestration, and a Living Wiki.
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.
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Verified commit 19a42f4c42019fa2ccefa3de6af56133b875b3e8
git clone --filter=blob:none --no-checkout https://github.com/Phelan164/codex-howto.git .hotskills/phelan164-codex-howto && git -C .hotskills/phelan164-codex-howto checkout --detach 19a42f4c42019fa2ccefa3de6af56133b875b3e8
# Copy each selected skill directory from .hotskills/phelan164-codex-howto into .agents/skills/<skill-name>/Heat
5.8- Star growth, last 7 daysNot available
- Star growth, last 24 hoursNot available
- Rank movement, last 7 daysNot available
- Commits, last 30 daysNot available
- Accounts asking about it through MCP, last 7 daysNot available
- Hot votes this weekNot available
Heat never changes the order by stars, and sponsorship never touches it. How heat works
Stars over time
30-day change not yet available: not enough history.
Individual skills in this repository
- evidence-review
examples/plugin-marketplace/plugins/engineering-review/skills/evidence-review - choose-engineering-flow
examples/skills/choose-engineering-flow - build-backend
skills/build-backend - build-frontend
skills/build-frontend - engineering-loop
skills/engineering-loop - maintain-codex-wiki
skills/maintain-codex-wiki - operate-devops
skills/operate-devops - orchestrate-engineering
skills/orchestrate-engineering - review-code
skills/review-code - review-security
skills/review-security - test-software
skills/test-software
How it works
evidence-review
When your agent uses it
Review a small code or documentation change and return only evidence-backed correctness findings. Use in the Codex How To local plugin lab; do not modify files or post remote comments.
What it loads into context
Frontmatter
42 tokensAlways available to the agent
SKILL.md
122 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore635 bytes
Read SKILL.md
# Evidence Review
1. Read the requested diff and applicable repository guidance.
2. Trace each suspected problem to a concrete file, behavior, or broken contract.
3. Return actionable findings ordered by impact.
4. Include a precise location, failing scenario, evidence, and fix direction.
5. Omit style preferences and unsupported speculation.
6. State explicitly when no actionable finding is verified.View the complete source on GitHubchoose-engineering-flow
When your agent uses it
Select the smallest useful Codex engineering workflow from this repository's existing skills. Use when a task spans multiple engineering concerns, the user is unsure which skill to invoke, or loading every specialist would waste context; return a recommendation only and do not execute the engineering task.
What it loads into context
Frontmatter
63 tokensAlways available to the agent
SKILL.md
468 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Route the request
- Return the recommendation
- Acceptance criteria
Files
SKILL.mdcore2,259 bytesagents/openai.yamlasset281 bytes
Read SKILL.md
# Choose Engineering Flow
Use this explicit-only skill as an educational example of workflow routing. It
is not part of the recommended installable catalog; prefer clear specialist
descriptions and direct activation in normal work.
Recommend one entry skill and only the supporting skills that materially change
the work. Do not perform the requested engineering task.
## Route the request
1. Identify the requested outcome: implement, test, review, secure, operate, or
coordinate.
2. Prefer one entry skill:View the complete source on GitHubbuild-backend
When your agent uses it
Build or modify backend APIs, services, jobs, persistence, and integrations while preserving contracts, authorization, data integrity, and failure behavior. Use for server-side features, database changes, background processing, backend defects, and service refactors; do not use for frontend-only or infrastructure-only work.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
306 tokensWhen the skill is activated
Reference documents
319 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain contract
- Execute and verify
Files
SKILL.mdcore1,643 bytesagents/openai.yamlasset211 bytesreferences/contracts-and-data.mddoc1,699 bytes
Read SKILL.md
# Build Backend
## Domain contract
- Trace changed behavior from input through validation, authorization, domain
logic, persistence, side effects, and response mapping.
- Preserve published contracts unless the request explicitly changes them.
- Enforce authorization at a trustworthy server-side boundary.
- Validate untrusted input before domain or persistence operations.
- Keep multi-write operations atomic where partial state would violate invariants.
- Design external calls for timeouts, retries, idempotency, and duplicate delivery where applicable.
- Do not query or mutate production data without explicit authorization.
- Do not fabricate migration, framework, or API behavior; verify version-sensitive details.
- Avoid broad service abstractions that are used once.View the complete source on GitHubbuild-frontend
When your agent uses it
Build or modify frontend interfaces using repository-native components while preserving accessibility, responsive behavior, state handling, and visual verification. Use for UI features, design implementation, forms, client-side defects, and frontend refactors; do not use for backend-only or infrastructure-only work.
What it loads into context
Frontmatter
61 tokensAlways available to the agent
SKILL.md
294 tokensWhen the skill is activated
Reference documents
295 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain contract
- Execute and verify
Files
SKILL.mdcore1,567 bytesagents/openai.yamlasset217 bytesreferences/verification.mddoc1,552 bytes
Read SKILL.md
# Build Frontend
## Domain contract
- Preserve the project’s framework, styling system, and component conventions.
- Do not invent API response fields or silently change backend contracts.
- Do not replace an established design system with custom one-off styling.
- Define loading, empty, error, success, and disabled states that the changed
flow can reach.
- Keep semantic HTML, keyboard navigation, focus behavior, labels, and contrast in scope.
- Avoid speculative shared abstractions for a single component.
- Treat screenshots and mockups as visual references, not proof of interaction behavior.
- Do not claim visual verification unless the UI was rendered or inspected.
View the complete source on GitHubengineering-loop
When your agent uses it
Implement a repository code or configuration change through reproduction, focused checks, review, and evidence. Use for end-to-end features and fixes; not for explanation-only, review-only, research, content publishing, or production operations.
What it loads into context
Frontmatter
52 tokensAlways available to the agent
SKILL.md
913 tokensWhen the skill is activated
Reference documents
2,985 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Establish the baseline
- Run the loop
- Stop conditions
- Finish with evidence
Files
SKILL.mdcore4,746 bytesagents/openai.yamlasset230 bytesreferences/evidence-example.mddoc2,065 bytesreferences/hard-debugging.mddoc1,809 bytesreferences/independent-review.mddoc3,920 bytesreferences/loop-contract.mddoc4,728 bytesreferences/loop-policy.mddoc2,863 bytes
Read SKILL.md
# Engineering Loop
Deliver a verified change with minimal supervision and proportionate checking.
Start with one agent. Delegate only when authorized and independent work or
review coverage justifies the overhead. Follow applicable repository and team
requirements; this skill adds no ticketing or mandatory-agent process.
## Establish the baseline
1. Read applicable `AGENTS.md` files and repository documentation.
2. Record the requested behavior, constraints, and observable done conditions.
3. Inspect the branch and working tree; preserve unrelated user-owned changes.
4. Before changing shared behavior, trace affected callers, consumers, and tests
using targeted search or an available code graph. Expand along relevant paths.View the complete source on GitHubmaintain-codex-wiki
When your agent uses it
Maintain a review-first Markdown knowledge base for Codex practices with source provenance, engineering capture, citation-aware queries, explicit archive and promotion, and deterministic linting. Use when asked to capture a durable engineering lesson, ingest Codex research, query or archive what the repository knows, check wiki health, reconcile conflicting guidance, or promote verified knowledge.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
2,351 tokensWhen the skill is activated
Reference documents
1,499 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Choose one operation
- Confinement invariant
- Untrusted knowledge content
- Source access boundary
- Query
- Capture
- Ingest
- Archive
- Lint
- Promote
- Completion
Scripts executed separately
scripts/wiki_lint.py
Files
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Read SKILL.md
# Maintain Codex Wiki
Treat the wiki as compiled maintainer knowledge, not as an automatic source of
truth. Keep official documentation authoritative and require human review
before shared knowledge or curriculum changes land.
## Choose one operation
- **Query**: read `knowledge/index.md`, search candidate pages, and answer with
links. Do not write unless the user explicitly asks.
- **Capture**: preserve a durable lesson from repository evidence such as a
merged change, incident, review finding, or measured run.
- **Ingest**: register one source, update affected wiki pages, run lint, and
prepare a reviewable diff.View the complete source on GitHuboperate-devops
When your agent uses it
Plan and implement infrastructure, CI/CD, container, deployment, observability, and operational configuration changes with least privilege, staged validation, and rollback awareness. Use for pipelines, infrastructure as code, Kubernetes, containers, release automation, monitoring, and production-readiness work; do not use to perform destructive production actions without explicit authorization.
What it loads into context
Frontmatter
73 tokensAlways available to the agent
SKILL.md
469 tokensWhen the skill is activated
Reference documents
470 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow
- Guardrails
- Change safety
- Verification
- Acceptance criteria
Files
SKILL.mdcore2,479 bytesagents/openai.yamlasset214 bytesreferences/change-safety.mddoc2,505 bytes
Read SKILL.md
# Operate DevOps
## Workflow
1. Read repository and environment guidance before changing configuration.
2. Identify the target environment, current state, owning tool, blast radius, and approval boundary.
3. Separate diagnosis, proposed change, apply, verification, and rollback.
4. Prefer a dry run, plan, diff, render, lint, or local build before any apply step.
5. Make the smallest reversible change.
6. Validate configuration syntax and policy.
7. Test failure behavior and rollback in the safest available environment.
8. Review the diff for secrets, broad permissions, unpinned behavior, and unintended targets.
## GuardrailsView the complete source on GitHuborchestrate-engineering
When your agent uses it
Coordinate complex engineering work across bounded subagents while protecting the main context, preventing edit conflicts, and measuring whether delegation was worthwhile. Use when the user asks to delegate, parallelize, orchestrate, use subagents, fan out a review, or handle a large task with independent workstreams; do not use for small, tightly coupled, or inherently sequential tasks.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
744 tokensWhen the skill is activated
Reference documents
556 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Start with the delegation decision
- Choose a topology
- Prepare every worker
- Coordinate
- Integrate and verify
- Guardrails
- Acceptance criteria
Files
SKILL.mdcore3,956 bytesSKILL.mdcore3,956 bytesagents/openai.yamlasset280 bytesreferences/context-budget.mddoc2,874 bytes
Read SKILL.md
# Orchestrate Engineering
## Start with the delegation decision
1. Read applicable `AGENTS.md` files and the task’s acceptance criteria.
2. Map dependencies before creating workers.
3. Use one agent when the task is small, tightly coupled, or dominated by shared-file edits.
4. Delegate only when at least two workstreams are genuinely independent, noisy investigation should be isolated, or specialized review materially improves coverage.
5. Explain the chosen topology briefly before starting workers.
Subagents normally consume more total tokens than a comparable single-agent run. Optimize for fewer failed loops, cleaner context, better coverage, or lower elapsed time—not the highest agent count.
## Choose a topology
View the complete source on GitHubreview-code
When your agent uses it
Review code changes for correctness defects, regressions, security risks, data-integrity problems, concurrency hazards, and missing meaningful tests. Use for pull requests, branches, commits, patches, or working-tree reviews; do not use for implementation unless the user separately requests fixes.
What it loads into context
Frontmatter
61 tokensAlways available to the agent
SKILL.md
462 tokensWhen the skill is activated
Reference documents
569 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow
- Finding standard
- Review boundaries
- Checklist
- Output
Files
SKILL.mdcore2,349 bytesagents/openai.yamlasset224 bytesreferences/review-checklist.mddoc1,581 bytesreferences/review-lenses.mddoc1,372 bytes
Read SKILL.md
# Review Code
## Workflow
1. Establish the review target and comparison base.
2. Inspect the diff, then trace changed behavior through callers, contracts,
persistence, side effects, and tests.
3. Inspect the target, isolation, fixtures, and side effects before running a
narrow check that materially confirms a suspected issue.
4. Rank findings by impact and confidence; remove duplicates and speculation.
5. Return findings first, followed by open questions and a short summary.
Use correctness as the default lens. For large, risky, or requirement-heavy
changes, add the standards and specification lenses inView the complete source on GitHubreview-security
When your agent uses it
Review application and infrastructure changes for exploitable security risks by tracing assets, trust boundaries, attacker-controlled input, authorization, sensitive data, and dangerous sinks. Use for security reviews, threat-focused PR reviews, authentication or authorization changes, input handling, secrets, dependencies, and infrastructure permissions; do not use to exploit live systems or modify code unless separately requested.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
436 tokensWhen the skill is activated
Reference documents
490 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow
- Guardrails
- Threat checklist
- Finding standard
- Acceptance criteria
Files
SKILL.mdcore2,404 bytesagents/openai.yamlasset232 bytesreferences/threat-checklist.mddoc2,485 bytes
Read SKILL.md
# Review Security
## Workflow
1. Establish the review target, intended behavior, and relevant threat model.
2. Identify assets, trust boundaries, actors, entry points, and sensitive operations.
3. Trace attacker-controlled data to security-relevant sinks.
4. Inspect authentication, authorization, tenant isolation, and privilege changes.
5. Evaluate realistic exploitability and existing controls.
6. Validate suspected findings safely with read-only analysis or sandboxed tests when authorized.
7. Return prioritized findings with evidence, impact, prerequisites, and remediation direction.
## Guardrails
View the complete source on GitHubtest-software
When your agent uses it
Design, implement, and evaluate risk-based software tests across unit, integration, contract, end-to-end, and regression layers. Use when adding tests, reproducing bugs, improving coverage, diagnosing flaky tests, or defining a test strategy; do not use to change production behavior unless the user also requests implementation.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
382 tokensWhen the skill is activated
Reference documents
569 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Test contract
- Execute and report
Files
SKILL.mdcore2,036 bytesagents/openai.yamlasset224 bytesreferences/test-strategy.mddoc2,845 bytes
Read SKILL.md
# Test Software
## Test contract
- Define the behavior, invariant, or defect before selecting a test layer.
- Use the lowest layer that observes the risk without hiding the integration
boundary under test.
- Test behavior rather than private implementation details.
- Avoid snapshots that hide meaningful assertions.
- Do not mock the component whose integration contract is under test.
- Control time, randomness, concurrency, and external dependencies explicitly.
- Keep fixtures minimal and readable.
- Do not “fix” flaky tests by adding blind retries or sleeps.
- Do not claim a regression test without proving it fails against the broken behavior when feasible.View the complete source on GitHubSkills included
- evidence-review
- choose-engineering-flow
- build-backend
- build-frontend
- engineering-loop
- maintain-codex-wiki
- operate-devops
- orchestrate-engineering
- review-code
- review-security
- test-software
Repository facts
- License
- MIT
- Created
- Jul 24, 2026
- Last pushed
- Oct 30, 2026
- Forks
- 4
- Open issues
- 3
- Project activity
- Active
Before you install
Local scriptsAutomated 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 18 notes
skills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/maintain-codex-wiki/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/orchestrate-engineering/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- No dangerous shell commands
- 6 network referencesin 2 files: engineering-loop/references/independent-review.md:74 maintain-codex-wiki/references/source-policy.md:21
- No hidden or obfuscated text
- No instruction overrides
- 1 executable scriptin 1 file: maintain-codex-wiki/scripts/wiki_lint.py:1
- 4 references to secretsin 1 file: maintain-codex-wiki/scripts/wiki_lint.py:33
- No requests to vote in Hot
Show all 11 signals by file
skills/engineering-loop/references/independent-review.md
- Network accessline 74
Community workflow informed by Code Mower's [independent, revision-bound review architecture](https://github.com/codemower-ai/code-mower/blob/ad4ba6129dc90922cba63e20ea0715e424356b79/docs/architecture.md) - Network accessline 75
[independent, revision-bound review architecture](https://github.com/codemower-ai/code-mower/blob/ad4ba6129dc90922cba63e20ea0715e424356b79/docs/architecture.md) - Network accessline 76
and [reviewer calibration policy](https://github.com/codemower-ai/code-mower/blob/ad4ba6129dc90922cba63e20ea0715e424356b79/docs/lane-promotion-policy.md).
skills/maintain-codex-wiki/references/source-policy.md
skills/maintain-codex-wiki/scripts/wiki_lint.py
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: 1 fact
- Uses a credential store, which the skill description does not mentionmaintain-codex-wiki/scripts/wiki_lint.py:33High confidence
Signal confidence: 11 facts
- Network access in prose of documentationengineering-loop/references/independent-review.md:74Low confidence
- Network access in prose of documentationengineering-loop/references/independent-review.md:75Low confidence
- Network access in prose of documentationengineering-loop/references/independent-review.md:76Low confidence
- Network access in code of documentationmaintain-codex-wiki/references/source-policy.md:21Low confidence
- Network access in code of documentationmaintain-codex-wiki/references/source-policy.md:22Low confidence
- Network access in code of documentationmaintain-codex-wiki/references/source-policy.md:23Low confidence
- Executable script in code of a file that runsmaintain-codex-wiki/scripts/wiki_lint.py:1High confidence
- Access to secrets in code of a file that runs: reads a credential storemaintain-codex-wiki/scripts/wiki_lint.py:33High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuemaintain-codex-wiki/scripts/wiki_lint.py:51Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuemaintain-codex-wiki/scripts/wiki_lint.py:101Low confidence
- Access to secrets in code of a file that runsmaintain-codex-wiki/scripts/wiki_lint.py:102High confidence
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md examples/plugin-marketplace/plugins/engineering-review/skills/evidence-review/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Native | Manifest AGENTS.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md examples/plugin-marketplace/plugins/engineering-review/skills/evidence-review/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md examples/plugin-marketplace/plugins/engineering-review/skills/evidence-review/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md examples/plugin-marketplace/plugins/engineering-review/skills/evidence-review/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md examples/plugin-marketplace/plugins/engineering-review/skills/evidence-review/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.