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/stellarshenson/claude-code-plugins/fix-projectnpx skills add stellarshenson/claude-code-plugins --skill fix-projectgit clone --depth 1 https://github.com/stellarshenson/claude-code-pluginsWhat 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 | $0.00019 | $0.00908 |
| Opus 5 | $0.00010 | $0.00454 |
| Sonnet 5 | $0.00004 | $0.00182 |
| Haiku 4.5 | $0.00002 | $0.00091 |
Grade A, and why
fix-project 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fix Project Structure
Port an existing data science project to the copier-data-science template standards, or update an already-templated project to the latest version.
Step 1: Detect project state
Check the project root for:
.copier-answers.yml-> project was created with copier (can update)Makefilewith known targets -> partial copier structure- Neither -> legacy/manual project (needs full port)
Report the detected state and ASK user to confirm.
Mode A: Update existing copier project
If .copier-answers.yml exists:
copier update --trust
This pulls the latest template changes while preserving user modifications. Review the diff and resolve any conflicts.
If copier is not installed: pip install copier or uv tool install copier.
Mode B: Port legacy project to copier
If no .copier-answers.yml:
-
Audit current structure:
- List all directories and their contents
- Identify: data directories, notebooks, source modules, model artifacts, configs
- Map existing structure to copier-data-science conventions
-
ASK user:
- Project name (for copier)
- Author name and email
- What to preserve vs what to reorganize
- Any directories that should NOT be moved
-
Create copier scaffold alongside:
copier copy https://github.com/stellarshenson/copier-data-science .copier-temp -
Port existing files:
- Move
*.pynotebooks tonotebooks/(if not already there) - Move data files to
data/{raw,processed,interim}/based on content - Move model artifacts to
models/ - Move source modules to
src/<project_name>/ - Merge Makefiles (keep existing targets, add missing copier targets)
- Merge
.gitignore(union of both) - Merge
pyproject.toml/setup.py(keep deps, adopt structure)
- Move
-
Install copier answers:
- Move
.copier-answers.ymlfrom temp scaffold to project root - Remove temp scaffold
- Now
copier updateworks for future template updates
- Move
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 · 96 lines · 19 tokens per session scan A 3d30ccea473c
fix-project is a skill published in the GitHub repository stellarshenson/claude-code-plugins (3 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 908 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 2026-08-31.
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…