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 ivy00johns/Skill-Madness --skill skill-deep-reviewgit clone --depth 1 https://github.com/ivy00johns/Skill-MadnessWrote 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/ivy00johns/skill-madness/skill-deep-review)<a href="https://agentmods.dev/skills/ivy00johns/skill-madness/skill-deep-review"><img src="https://agentmods.dev/badge/skills/ivy00johns/skill-madness/skill-deep-review/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/ivy00johns/skill-madness/skill-deep-review"><img src="https://agentmods.dev/badge/skills/ivy00johns/skill-madness/skill-deep-review.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 | $0.00124 | $0.01200 |
| Opus 5 | $0.00062 | $0.00600 |
| Sonnet 5 | $0.00025 | $0.00240 |
| Haiku 4.5 | $0.00012 | $0.00120 |
Grade A, and why
skill-deep-review 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 11d 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Deep Review
Perform a comprehensive quality review of a single skill, combining static analysis with live trigger testing via /skill-creator.
When to Use
- Reviewing a single skill in depth before publishing
- Investigating why a skill isn't triggering or producing poor results
- Validating a skill after major edits
- Quality-gating a skill before it enters the ecosystem
Inputs
- Skill path — path to the skill directory (must contain
SKILL.md) - Context (optional) — what prompted the review (e.g., "it never triggers", "outputs are wrong")
Process
Phase 1: Structural Analysis
Read the skill's SKILL.md and all files in its directory tree. Evaluate against the rubric in references/deep-review-rubric.md. Score each dimension 1–5 and note specific issues.
Dimensions:
- Frontmatter compliance — required fields present, types correct, version valid semver, description follows patterns
- Description quality — action verb, trigger contexts, keyword variants, appropriate length, "pushiness"
- Progressive disclosure — body under 500 lines, references used appropriately, clear pointers to reference files
- Instruction clarity — imperative voice, logical flow, no ambiguity, explains "why" not just "what"
- Coordination — ownership declarations, composes_with accuracy, no overlaps with existing skills
- Completeness — all referenced files exist, no dead links, validation checklists present where needed
- Anti-patterns — no hardcoded project details, no excessive MUSTs/NEVERs without rationale, no duplicate content between body and references
Phase 2: Live Trigger Testing
Use /skill-creator's eval infrastructure to test whether the skill actually works:
- Generate 3–5 realistic test prompts that should trigger this skill
- Generate 2–3 near-miss prompts that should NOT trigger it
- Run trigger evaluation using skill-creator's description optimization tooling
- Report trigger accuracy (hit rate on should-trigger, false-positive rate on should-not)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 130 lines · 124 tokens per session scan A 7cf3aceb287e
skill-deep-review is a skill published in the GitHub repository ivy00johns/Skill-Madness (11 stars, last pushed today), licensed MIT. It adds 124 tokens to every session and 1,200 once invoked, about $0.0006 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 2026-08-30.
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