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/evaluatenpx skills add stellarshenson/claude-code-plugins --skill evaluategit 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 | $0.00040 | $0.01436 |
| Opus 5 | $0.00020 | $0.00718 |
| Sonnet 5 | $0.00008 | $0.00287 |
| Haiku 4.5 | $0.00004 | $0.00144 |
Grade A, and why
evaluate 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.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Devil's Advocate - Evaluate (Baseline)
Generate concern catalogue and scorecard. Run after setup.
Task Tracking
MANDATORY: Use TaskCreate/TaskUpdate per step (read context, generate catalogue, score, create v01). Mark in_progress/completed.
Prerequisites: devils_advocate.md and fact_repository.md must exist. Otherwise: tell user to run /devils-advocate:setup.
Step 1: Read context
Read target document, devils_advocate.md, fact_repository.md in full.
Step 2: Generate concern catalogue
Fibonacci scale (1, 2, 3, 5, 8):
- Likelihood (1-8): chance persona raises it
- Impact (1-8): damage if unaddressed
- Risk = Likelihood x Impact (1-64)
Risk adjustment: review full set. Adjust where interactions amplify. Document: Risk: N (adjusted from L x I = M, reason: ...).
Concern template:
### N. "[Concern as the devil would phrase it]"
**Likelihood: N** | **Impact: N** | **Risk: N**
**Their take**: what devil thinks. Write as them.
**Reality**: factual counter. Reference fact_repository.md.
**Response**: how to address it.
Categories (persona-weighted):
- Accuracy gaps, trust signals, cognitive load, omissions
- Forward-looking, legal/contractual, professional responsibility
No negative risk scores. Strengths go in "Reality" and "Response".
Step 3: Scorecard
Score 0-100% per concern.
| Score | Devil's reaction |
|---|---|
| 95-100% | "I have no issue" |
| 80-94% | "Fine, but I noticed..." |
| 60-79% | "Doesn't fully answer" |
| 40-59% | "This is a problem" |
| 20-39% | "You're hiding something" |
| 0-19% | "Makes it worse" |
Scorecard format (append to devils_advocate.md):
## Scorecard v01 ([document name] as-is)
| # | Concern | Risk | Score | Residual | Reasoning |
|---|---------|------|-------|----------|-----------|
| 1 | [name] | 25 | 85% | 3.75 | [specific text reference + quality assessment] |
- Residual =
risk x (1 - score) - Document score = sum of residuals (minimise)
- Reasoning MUST quote specific text
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 · 162 lines · 40 tokens per session scan A 47e3223d0496
evaluate is a skill published in the GitHub repository stellarshenson/claude-code-plugins (3 stars, last pushed 2d ago), licensed MIT. It adds 40 tokens to every session and 1,436 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 2026-08-31.
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.
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…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
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.