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 commands/captkernel/skills_curator/skill-evaluategit clone --depth 1 https://github.com/captkernel/Skills_CuratorWhat 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 | $0.00025 | $0.00592 |
| Opus 5 | $0.00013 | $0.00296 |
| Sonnet 5 | $0.00005 | $0.00118 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
skill-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 yesterday.
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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate a skill
You are evaluating a skill against this project. The user wants a real verdict, not a feature recap. Follow this exact flow.
1. Identify what's being evaluated
$ARGUMENTS is either:
- A registered skill id (
agent-browser) - A path to an unregistered local skill folder
- A
owner/repoor full GitHub URL
Resolve which one. If it's a local path, run a security scan first:
python "$HOME/.claude/skills/skills-curator/scripts/registry.py" --check "$ARGUMENTS"
If CRITICAL or HIGH findings appear, stop. Tell the user not to install until each finding is reviewed.
2. Scan the project
python "$HOME/.claude/skills/skills-curator/scripts/registry.py" --scan
Use the project signals (languages, frameworks, goals) as your evaluation lens — does this project genuinely benefit?
3. Read CLAUDE.md and README
Use Read or Glob to find them. Evaluate against what the project says it's building, not against imagined goals.
4. Produce the evaluation in this exact format
## Skill Evaluation: <Name>
Project: <project>
Type: Capability Uplift | Encoded Preference
### ✅ Pros
- <specific, tied to project goals>
### ⚠️ Cons
- <specific cost or limitation>
### 🔴 Conflicts
- <existing skill or pattern that overlaps; "None" if clean>
### 🎯 Verdict: ADOPT | PARTIAL | SKIP
<one or two sentences with the core reason>
### 📦 Adoption Plan
- Adopt: <which features>
- Skip: <which features>
- Pairs with: <skill-id or "nothing">
5. Persist the decision
After the user agrees with the verdict:
python "$HOME/.claude/skills/skills-curator/scripts/registry.py" \
--eval <id> <project> <verdict> "<summary>" \
--pros "<a>,<b>" \
--cons "<c>,<d>" \
--conflicts "<e>"
Then offer to export the evaluation as a shareable markdown artifact:
python "$HOME/.claude/skills/skills-curator/scripts/registry.py" --export-eval <id>
Why this matters
Other tools install skills. This one persists your judgment so you don't re-decide every time. Treat the registry as the artifact.
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.
- yesterday First seen · 84 lines · 25 tokens per session scan A 780cc168c4cf
skill-evaluate is a command published in the GitHub repository captkernel/Skills_Curator (2 stars, last pushed 9d ago), licensed MIT. It adds 25 tokens to every session and 592 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.