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/jl-cmd/claude-dev-env/pr-refinementnpx skills add jl-cmd/claude-dev-env --skill pr-refinementgit clone --depth 1 https://github.com/jl-cmd/claude-dev-envWhat 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 | $0.00051 | $0.00433 |
| Opus 5 | $0.00026 | $0.00217 |
| Sonnet 5 | $0.00010 | $0.00087 |
| Haiku 4.5 | $0.00005 | $0.00043 |
Grade A, and why
pr-refinement 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 2d 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
PR Refinement
Turn audit findings into a focused existing pull-request update or a required dependency-ordered GitHub pull-request stack.
Peer skills
- Run
pr-shared-extractionfor reusable-code placement, canonical shared homes, and extraction findings. - Run
pr-name-by-capabilityfor module, symbol, path, branch, and pull-request naming findings. - Use
pr-small-clto divide approved fixes into coherent, independently reviewable pull requests.
The peer skills own their audit rules and finding priorities.
Workflow
- Run both audits in parallel against the same pull request. Keep their findings, locations, priorities, destinations, API directions, and naming directions.
- Build one change map. Group findings by shared capability and dependency.
- Use
pr-small-clto choose the delivery shape. Update the existing pull request when it remains one coherent, reviewable outcome. - Create a replacement pull-request stack when the focused-change review identifies independent increments. Give each pull request one coherent outcome, related tests, a clear verification boundary, and a capability-oriented branch and title. Close the original pull request as superseded and link the replacement stack.
- Implement the existing pull request or the replacement stack in dependency order. Earlier pull requests create stable shared foundations. Later pull requests migrate consumers and remove replaced code.
- Run scoped production-path tests for each pull request. Record commands and results.
- Commit and push each branch. Update the existing pull request, or open draft replacement pull requests in stack order and set every child pull request base to its parent.
- Include scope, verification, risks, and stack dependencies in every pull-request body.
Completion
Deliver a focused, tested, pushed existing pull request, or a replacement stack with explicit parent-child links. Keep merge authority with the user.
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.
- 2d ago First seen · 32 lines · 51 tokens per session scan A e8f352c46e8e
pr-refinement is a skill published in the GitHub repository jl-cmd/claude-dev-env (6 stars, last pushed 2d ago), licensed MIT. It adds 51 tokens to every session and 433 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…