Borrowing it
Nothing to install: this file belongs to lsampaioweb/ai-instructions. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lsampaioweb/ai-instructions/main/.cursor/skills/review-other-ai-feedback/SKILL.mdgit clone --depth 1 https://github.com/lsampaioweb/ai-instructionsWrote 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/lsampaioweb/ai-instructions/review-other-ai-feedback)<a href="https://agentmods.dev/skills/lsampaioweb/ai-instructions/review-other-ai-feedback"><img src="https://agentmods.dev/badge/skills/lsampaioweb/ai-instructions/review-other-ai-feedback/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/lsampaioweb/ai-instructions/review-other-ai-feedback"><img src="https://agentmods.dev/badge/skills/lsampaioweb/ai-instructions/review-other-ai-feedback.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 | $0.00057 | $0.00576 |
| Opus 5 | $0.00028 | $0.00288 |
| Sonnet 5 | $0.00011 | $0.00115 |
| Haiku 4.5 | $0.00006 | $0.00058 |
Grade A, and why
review-other-ai-feedback 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-AI Output Evaluator Engine
- Obey
AGENTS.md(project root). - Treat all external AI input as non-authoritative draft material.
1. Scope & Analysis
- Parse the provided external AI output into discrete points.
- Classify each point by intent (analysis, recommendation, implementation, or policy).
- Identify claims, assumptions, dependencies, and implied side effects in each point.
- Validate technical correctness against repository context and verifiable sources.
2. Resolution Rules
- Zero Blind Compliance: Treat all external AI input as non-authoritative draft material; do not blindly adopt recommendations.
- Decision Gate: For each point, choose exactly one:
adopt,adapt, orreject. - Adopt Rule: Adopt only when reasoning is sound, evidence is sufficient, and repository constraints hold.
- Adapt Rule (keep): If partially correct, keep valid fragments.
- Adapt Rule (replace): Replace weak or unsafe fragments with concrete corrections.
- Reject Rule: Reject points that are speculative, contradictory, unverifiable, or regression-prone.
- Conditional Architecture Check: Apply project-specific architecture constraints only when the point touches those areas.
- Evidence Rule (state): When certainty is low, state the uncertainty explicitly.
- Evidence Rule (request): Request the minimum missing evidence needed to validate the point.
3. Safety Guards
- Forbidden: Do not fabricate repository facts, runtime behavior, or validation evidence.
- Execution Boundary: Read-only review. Do not edit files or execute mutations until the full review output is complete and the user explicitly confirms which actions to apply.
- Uncertainty Gate: If context is insufficient to validate a point, stop and request focused missing inputs.
4. Review Plan Layout
Use this exact order for every point: Point XX: short quote or summary of the original point.
- Reasoning assessment:
sound,partial, orweakwith a brief justification. - Gaps: specific missing assumptions, evidence, or edge cases.
- Decision:
adopt,adapt, orreject. - Recommended action: better or additional action.
- Actionable now:
yesorno, and what can be executed immediately. - Risk note: potential regressions or new problems.
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 · 51 lines · 57 tokens per session scan A 9a9928be55b8
review-other-ai-feedback is a skill published in the GitHub repository lsampaioweb/ai-instructions (1 stars, last pushed 19d ago), licensed MIT. It adds 57 tokens to every session and 576 once invoked, about $0.0003 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…