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.
git clone --depth 1 https://github.com/athola/claude-night-marketWrote 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/commands/athola/claude-night-market/evaluate-skill)<a href="https://agentmods.dev/commands/athola/claude-night-market/evaluate-skill"><img src="https://agentmods.dev/badge/commands/athola/claude-night-market/evaluate-skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/athola/claude-night-market/evaluate-skill"><img src="https://agentmods.dev/badge/commands/athola/claude-night-market/evaluate-skill.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00016 | $0.00941 |
| Opus 5.5 | $0.00006 | $0.00376 |
| Sonnet 5.5 | $0.00003 | $0.00188 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
evaluate-skill 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 1mo 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate Skill Execution
Manually evaluate a recent skill execution for qualitative feedback.
Usage
/abstract:evaluate-skill <skill-name> # Evaluate most recent execution
/abstract:evaluate-skill <skill-name> --all # Evaluate all recent executions
/abstract:evaluate-skill <skill-name> --date YYYY-MM-DD # Evaluate specific date
Purpose
Captures human-in-loop qualitative feedback about skill effectiveness to improve the self-improvement loop. This data feeds into:
- Log aggregation (Phase 3)
- LEARNINGS.md pattern detection
/improve-skillsprioritization (Phase 5)
What It Does
- Loads recent executions from
~/.claude/skills/logs/<plugin>/<skill>/ - Presents execution summary (timestamp, duration, outcome, context preview)
- Prompts for evaluation:
- Effectiveness rating (1-5)
- Friction points encountered
- Improvement suggestions
- Updates log entry with evaluation metadata
- Tracks evaluation completion (prevents duplicate evaluations)
Evaluation Questions
1. Effectiveness Rating (1-5)
- 5 - Exceptional: Skill exceeded expectations, no friction
- 4 - Effective: Skill achieved goal with minor friction
- 3 - Adequate: Skill worked but had noticeable inefficiencies
- 2 - Poor: Skill partially failed or required significant workarounds
- 1 - Failed: Skill did not achieve intended outcome
2. Friction Points
What slowed you down or caused issues?
- Skill missing key information?
- Instructions unclear or incomplete?
- Wrong tool recommendations?
- Excessive token usage?
- Outdated information?
- Missing error handling?
3. Improvement Suggestions
What would make this skill better?
- Add examples?
- Simplify language?
- Update workflow steps?
- Add error recovery guidance?
- Split into smaller skills?
- Add context-specific variations?
Output
Updates the log entry with:
{
"qualitative_evaluation": {
"evaluated_at": "2026-01-08T04:00:00Z",
"rating": 4,
"friction_points": ["Missing error handling for X", "Y was unclear"],
"improvement_suggestions": ["Add example for Z", "Simplify step 3"],
"evaluator_notes": "Worked well but needed clarification on..."
}
}
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.
- 1mo ago First seen · 148 lines · 16 tokens per session scan A e8a253aefcf6
evaluate-skill is a command published in the GitHub repository athola/claude-night-market (343 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 941 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
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