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 Wondermonger-daydreaming/claude-skills-library --skill cross-model-critiquegit clone --depth 1 https://github.com/Wondermonger-daydreaming/claude-skills-libraryWrote 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/wondermonger-daydreaming/claude-skills-library/cross-model-critique)<a href="https://agentmods.dev/skills/wondermonger-daydreaming/claude-skills-library/cross-model-critique"><img src="https://agentmods.dev/badge/skills/wondermonger-daydreaming/claude-skills-library/cross-model-critique/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/wondermonger-daydreaming/claude-skills-library/cross-model-critique"><img src="https://agentmods.dev/badge/skills/wondermonger-daydreaming/claude-skills-library/cross-model-critique.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 | $0.00000 | $0.01218 |
| Opus 5 | $0.00000 | $0.00609 |
| Sonnet 5 | $0.00000 | $0.00244 |
| Haiku 4.5 | $0.00000 | $0.00122 |
Grade A, and why
cross-model-critique 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 12d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Model Critique — Integrating External AI Feedback
What This Skill Does
Takes critique generated by another AI model, translates its diagnostic vocabulary into actionable revision targets, and performs precise surgery based on the translated diagnosis.
Why This Matters
Different models have different blindnesses and different strengths. In the forge session that generated this skill, GPT's critique of Claude's lyrics consistently identified:
- Words wearing costume that Claude's self-review missed ("clique," "stout-man's-college")
- Register shifts that Claude's internal consistency-checking didn't flag
- The strongest lines — confirming which load-bearing walls to protect during revision
- Structural observations that reframed the work's own architecture in ways Claude hadn't articulated
The external model functions as Reviewer 2 — the peer with a different training distribution who catches what your own distribution normalizes.
The Integration Method
1. Receive Without Defensiveness
The first instinct is to defend the work or explain the choices. Resist this. Read the full critique before reacting. The external model doesn't share your context, so some observations will be off-base — but the OFF-BASE observations are often the most revealing, because they identify assumptions you didn't know you were making.
2. Sort Into Categories
Every piece of critique falls into one of these:
- PROTECT: Lines or elements the critique identifies as strongest. These are the skeleton — they don't get touched during revision. Mark them explicitly.
- DIAGNOSE: Problems the critique identifies correctly. Map each diagnosis to a specific word, line, or passage. If the critique is vague ("verse 3 flirts with over-compression"), locate the exact lines it's referring to.
- TRANSLATE: Observations in the external model's vocabulary that need translation into Claude's revision vocabulary. GPT might say "a touch more comic than the rest of the stanza" — translate to: "this line is in the wrong register; it's patter where the verse wants baroque."
- REJECT (with reason): Suggestions that would damage the work. Name WHY you're rejecting — "this would remove the agency inversion in the bridge" is a reason; "I prefer it my way" is not.
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.
- 12d ago First seen · 78 lines · 0 tokens per session scan A 1d7336e53ac6
cross-model-critique is a skill published in the GitHub repository Wondermonger-daydreaming/claude-skills-library (6 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,218 tokens. 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.
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