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/mckruz/claude-code-sdlc/pr-writernpx skills add MCKRUZ/claude-code-sdlc --skill pr-writergit clone --depth 1 https://github.com/MCKRUZ/claude-code-sdlcWrote 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/mckruz/claude-code-sdlc/pr-writer)<a href="https://agentmods.dev/skills/mckruz/claude-code-sdlc/pr-writer"><img src="https://agentmods.dev/badge/skills/mckruz/claude-code-sdlc/pr-writer.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 | $0.00058 | $0.00606 |
| Opus 5 | $0.00029 | $0.00303 |
| Sonnet 5 | $0.00012 | $0.00121 |
| Haiku 4.5 | $0.00006 | $0.00061 |
Grade A, and why
pr-writer 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR writer
A PR is where intent and implementation are reviewed together, so it must carry both. This skill produces the PR the merge bar and the grader expect.
Preconditions (the harness enforces these — don't fight them)
- You push only to the branch you created (
spec/NNNN-name), never to the default branch. - The
review-gatehook blocks the push until/code-reviewand/simplifyhave run for HEAD — run those first (see the kit's built-in dependencies). - Tests and build are green (the Stop hook won't let you finish otherwise).
Procedure
- Branch: confirm you're on
spec/NNNN-name(create it from the default branch if not). - Commit: stage the specific files (not
git add -A). Title is a conventional commit —type: description, imperative, lowercase, under 72 chars (feat:,fix:,refactor:,docs:,test:,chore:,perf:,ci:). Every agent commit is co-authored for provenance. - Confirm the spec is in the diff —
specs/NNNN-name.mdmust be part of the PR so the reviewer and grader see intent + implementation in one view. - PR body:
- Summary — what changed and why (one short paragraph).
- Acceptance checks → evidence — list each check from the spec and the test/line that satisfies it.
- Test plan — what was run and the result (
dotnet testetc.), not "should pass." - Risk tier + gated paths touched — so the right gates and reviewers are triggered; if HIGH,
add the
risk:highlabel (GitHub) or PR label via--labels risk:high(Azure DevOps).
- Open the PR with that title and body —
gh pr createon GitHub,az repos pr createon Azure DevOps (both are gated by the review-gate hook until the receipts exist). Do not approve or merge it — a non-author owns the verdict.
Done when
- Branch, conventional-commit title, and co-authorship are correct.
- The spec file is in the diff and every acceptance check is mapped to evidence.
- The test plan reports real results; the risk tier/label is set.
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.
- 4d ago First seen · 43 lines · 58 tokens per session scan A 7559164bea80
pr-writer is a skill published in the GitHub repository MCKRUZ/claude-code-sdlc (4 stars, last pushed 7d ago), licensed MIT. It adds 58 tokens to every session and 606 once invoked, about $0.0003 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 2026-08-31.
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…