ai-instructions: Skill for Cursor

.cursor/skills/review-code-against-instructions/SKILL.md

review-code-against-instructions is a skill for Cursor from lsampaioweb/ai-instructions. It costs 84 tokens per session (1,151 once invoked), scanned A, original, MIT.

A read-only audit that compares source code with the instruction files governing it, and also checks whether those instructions cover the codebase. It can inspect a specified file, folder, module, or pattern.

In plain words
What is it for?
Use it to review code against project instructions, measure instruction coverage, identify applicable rules, and propose initial Cursor rules when none exist.
Why use it?
It reveals both implementation violations and missing rules for recurring code patterns. This helps keep instructions aligned with the code instead of checking only one direction.

Skill for Cursor

Written for Cursor: installed under .cursor/.

This is lsampaioweb/ai-instructions's own configuration. It tells Cursor how to work on ai-instructions itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ai-instructions configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/lsampaioweb/ai-instructions/main/.cursor/skills/review-code-against-instructions/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/lsampaioweb/ai-instructions

Made for: Cursor.

Wrote 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.

agentmods badge for review-code-against-instructions

README.md
[![agentmods](https://agentmods.dev/badge/skills/lsampaioweb/ai-instructions/review-code-against-instructions/github.svg)](https://agentmods.dev/skills/lsampaioweb/ai-instructions/review-code-against-instructions)
Your own site
<a href="https://agentmods.dev/skills/lsampaioweb/ai-instructions/review-code-against-instructions"><img src="https://agentmods.dev/badge/skills/lsampaioweb/ai-instructions/review-code-against-instructions/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.

agentmods 80×15 button for review-code-against-instructions

Your own site · 80×15
<a href="https://agentmods.dev/skills/lsampaioweb/ai-instructions/review-code-against-instructions"><img src="https://agentmods.dev/badge/skills/lsampaioweb/ai-instructions/review-code-against-instructions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,151 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00084 $0.01151
Opus 5.5 $0.00034 $0.00460
Sonnet 5.5 $0.00017 $0.00230
Haiku 4.5 $0.00008 $0.00115

Measured 1mo ago against content hash 00373fe77df0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

review-code-against-instructions 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.

.cursor/skills/review-code-against-instructions/SKILL.md · 126 lines

How it starts

The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Instruction Audit

Arguments

  • Optional target path, module, file, or glob.
  • Omit the filter to audit the full workspace.

1. Define the Scope

  1. Load all instruction files applicable to the target scope from both:
    • the workspace directory .github/instructions/
    • the configured user-level directory ~/.agents/instructions/
  2. Check each instruction file's applyTo pattern against the target files.
  3. Treat both locations as active instruction sources. Do not ignore an applicable user-level instruction because it is outside the workspace.
  4. If applicable instructions conflict, report the conflict and identify both sources. Stop rather than choosing a winner silently.
  5. Resolve the target scope from the user-provided arguments when present. If missing, audit the full workspace.
  6. Enumerate all code files in scope, sorted by path ascending.
  7. Read each code file and check it against all applicable instructions.
  8. If scope exceeds available context, process files in path order until context is exhausted, then set Verdict to CONTINUE and record the next unprocessed path.

Project decision records

  1. Load project ADRs or equivalent decision records from the target workspace, typically under docs/adr/, when they exist.
  2. Treat an ADR as an approved, project-specific exception only when all of the following are true:
    • its status is Accepted or equivalent;
    • it explicitly names the behavior, endpoint, component, or rule being decided;
    • the observed code is within the ADR's stated scope;
    • the ADR does not merely describe the current implementation.
  3. An explicit accepted ADR may override a reusable instruction only for the narrowly described decision. Do not generalize the exception to unrelated code or rules.
  4. Continue validating every non-exempt instruction and every compensating control described by the ADR.
  5. Cite the ADR when suppressing a finding. If the ADR is ambiguous, obsolete, superseded, or broader than the observed decision, report the uncertainty and stop instead of silently accepting the exception.
  6. Do not treat README text, comments, deployment assumptions, or undocumented conventions as equivalent to an accepted ADR.

Read the full file on GitHub · 126 lines

Changes

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.

  1. 1mo ago Changed · +33 lines · +20 tokens per session 00373fe77df0
  2. 2mo ago First seen · 93 lines · 64 tokens per session scan A 012c0f5731b0

Subscribe to this mod's changes

review-code-against-instructions is a skill published in the GitHub repository lsampaioweb/ai-instructions (1 stars, last pushed 5d ago), licensed MIT. It adds 84 tokens to every session and 1,151 once invoked, about $0.0003 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 2026-08-31.

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