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/matteocervelli/llms/pr-creatornpx skills add matteocervelli/llms --skill pr-creatorgit clone --depth 1 https://github.com/matteocervelli/llmsWrote 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/matteocervelli/llms/pr-creator)<a href="https://agentmods.dev/skills/matteocervelli/llms/pr-creator"><img src="https://agentmods.dev/badge/skills/matteocervelli/llms/pr-creator.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 | $0.00032 | $0.05248 |
| Opus 5 | $0.00016 | $0.02624 |
| Sonnet 5 | $0.00006 | $0.01050 |
| Haiku 4.5 | $0.00003 | $0.00525 |
Grade A, and why
pr-creator 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.
How it starts
The opening of the file, as written. The whole thing — 991 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pull Request Creator Skill
Purpose
This skill provides systematic guidance for creating high-quality pull requests with comprehensive descriptions, proper commit messages, and complete test plans following conventional commits and project standards.
When to Use
- After implementation, validation, documentation, and CHANGELOG updates
- Ready to create pull request for code review
- Need comprehensive PR description
- Following git workflow best practices
- Ensuring PR quality standards
PR Creation Workflow
1. Pre-Flight Checks
Objective: Verify everything is ready for PR creation.
Checklist:
# 1. All changes committed
git status
# Should show: "nothing to commit, working tree clean"
# 2. All tests passing
pytest
# All tests should be green
# 3. Code quality checks passed
black src/ tests/ --check
mypy src/
# Should have no errors
# 4. Documentation updated
# Verify docs/ has implementation docs
ls docs/implementation/
# 5. CHANGELOG updated
# Verify CHANGELOG.md has new entry
head -50 CHANGELOG.md
# 6. Version numbers synced
grep -r "version.*=.*[0-9]" pyproject.toml src/__init__.py
# Should all show same version
# 7. No merge conflicts with main
git fetch origin main
git merge-base --is-ancestor origin/main HEAD
# Exit code 0 means no conflicts
If any check fails:
- Fix the issue before proceeding
- Re-run checks until all pass
- Don't create PR with failing checks
Deliverable: All pre-flight checks passed
2. Review Changes
Objective: Understand full scope of changes for PR description.
Review commit history:
# View all commits in feature branch
git log --oneline main..HEAD
# View detailed commit messages
git log main..HEAD
# View files changed
git diff --stat main..HEAD
# View actual changes
git diff main..HEAD
Analyze changes:
- What was implemented?
- What files were modified/added?
- Are there breaking changes?
- What tests were added?
- What documentation was updated?
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 · 991 lines · 32 tokens per session scan A b74cc2c28f88
pr-creator is a skill published in the GitHub repository matteocervelli/llms (25 stars, last pushed 3mo ago), licensed MIT. It adds 32 tokens to every session and 5,248 once invoked, about $0.0002 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 2026-09-01.
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…