Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/stevegjones/ai-first-sdlc-practices/commitnpx skills add SteveGJones/ai-first-sdlc-practices --skill commitgit clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/stevegjones/ai-first-sdlc-practices/commit)<a href="https://agentmods.dev/skills/stevegjones/ai-first-sdlc-practices/commit"><img src="https://agentmods.dev/badge/skills/stevegjones/ai-first-sdlc-practices/commit.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00018 | $0.00401 |
| Opus 5 | $0.00009 | $0.00200 |
| Sonnet 5 | $0.00004 | $0.00080 |
| Haiku 4.5 | $0.00002 | $0.00040 |
Grade A, and why
commit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Validated Commit
Run quick validation, then commit if clean.
Steps
- Run quick validation
/sdlc-core:validate --quick
- Run tests and smoke test (if application code was changed)
# Run test suite if configured
pytest --tb=short -q 2>/dev/null || echo "No pytest configured"
# Smoke test: verify the app's main module imports cleanly
python -c "import app" 2>/dev/null || python -c "import main" 2>/dev/null || echo "No app module found"
If tests fail or the app cannot import, stop. Fix the issue before committing. Static analysis passing does not mean the code works.
-
If validation or tests fail, report the issues and stop. Do NOT commit.
-
If all checks pass, proceed:
- Review all changed files with
git statusandgit diff - Stage the relevant files (prefer specific files over
git add -A) - If
$ARGUMENTSis provided, use it as the commit message - If no message provided, draft a concise commit message based on the changes
- Use conventional commit format:
feat:,fix:,docs:,refactor:,test:,chore:
- Review all changed files with
-
Commit using a heredoc for proper formatting:
git commit -m "$(cat <<'EOF'
<type>: <description>
<optional body>
Co-Authored-By: Claude <[email protected]>
EOF
)"
- Run git status after commit to verify success.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 55 lines · 18 tokens per session scan A 11db000b9fd7
commit is a skill published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 25d ago), licensed MIT. It adds 18 tokens to every session and 401 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…