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 skills add existential-birds/beagle --skill review-skill-improvergit clone --depth 1 https://github.com/existential-birds/beagleWrote 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/existential-birds/beagle/review-skill-improver)<a href="https://agentmods.dev/skills/existential-birds/beagle/review-skill-improver"><img src="https://agentmods.dev/badge/skills/existential-birds/beagle/review-skill-improver/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/skills/existential-birds/beagle/review-skill-improver"><img src="https://agentmods.dev/badge/skills/existential-birds/beagle/review-skill-improver.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 165 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00035 | $0.01486 |
| Opus 5.5 | $0.00014 | $0.00594 |
| Sonnet 5.5 | $0.00007 | $0.00297 |
| Haiku 4.5 | $0.00003 | $0.00149 |
Grade A, and why
review-skill-improver 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Skill Improver
Purpose
Analyzes structured feedback logs to:
- Identify rules that produce false positives (high REJECT rate)
- Identify missing rules (issues that should have been caught)
- Suggest specific skill modifications
Input
Feedback log in enhanced schema format (see the review-feedback-schema skill).
Hard gates
Run in order; do not emit the final Review Skill Improvement Report until each gate passes.
- Input on record — The log is loaded from a stated path in the repo or from an attached artifact, not from memory or paraphrase. Pass: the report header or Summary names that path or states “attached feedback blob” with byte/line count.
- Schema / shape — Entries match the enhanced schema (
rule_source,verdict,rationale, etc. per the review-feedback-schema skill). Pass: either all rows parse, or skipped malformed rows are counted and listed by row index (not silently dropped). - Aggregation before thresholds — Complete Step 1 (per–
rule_sourcetotals, ACCEPT vs REJECT, rejection rate, rejection rationales) for the full parsed set before labeling any rule “high-rejection” or writing recommendations. Pass: Summary includes “Unique rules triggered” consistent with the aggregation table. - Evidence-bound recommendations — Every recommendation includes at least one concrete evidence pointer (log row(s), or file:line + short quote) before Proposed Fix. Pass: Evidence is non-empty for each recommendation.
Analysis Process
Step 1: Aggregate by Rule Source
For each unique rule_source:
- Count total issues flagged
- Count ACCEPT vs REJECT
- Calculate rejection rate
- Extract rejection rationales
Step 2: Identify High-Rejection Rules
Rules with >30% rejection rate warrant investigation:
- Read the rejection rationales
- Identify common themes
- Determine if rule needs refinement or exception
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 · 200 lines · 35 tokens per session scan A d2cdfc550d23
review-skill-improver is a skill published in the GitHub repository existential-birds/beagle (84 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 1,486 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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