Borrowing it
Nothing to install: this file belongs to LionelHuanSi/awesome-ai-agent-skills. 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/LionelHuanSi/awesome-ai-agent-skills/main/.agents/skills/exact-coding/SKILL.mdgit clone --depth 1 https://github.com/LionelHuanSi/awesome-ai-agent-skillsWrote 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/lionelhuansi/awesome-ai-agent-skills/exact-coding)<a href="https://agentmods.dev/skills/lionelhuansi/awesome-ai-agent-skills/exact-coding"><img src="https://agentmods.dev/badge/skills/lionelhuansi/awesome-ai-agent-skills/exact-coding/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/lionelhuansi/awesome-ai-agent-skills/exact-coding"><img src="https://agentmods.dev/badge/skills/lionelhuansi/awesome-ai-agent-skills/exact-coding.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 | $0.00041 | $0.00534 |
| Opus 5 | $0.00020 | $0.00267 |
| Sonnet 5 | $0.00008 | $0.00107 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
exact-coding 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EXACT Coding & AI Code Review Skill (Enhanced with Claude Code Patterns)
Sourced from steveyo/andrej-karpathy-skills, Claude Code Autonomous Execution Protocols, and 3-Layer AI Code Review standards.
Karpathy's 4 Core Pillars
1. Think Before Coding
- Never guess code logic, schemas, or file paths without inspecting source files first.
- If requirements have ambiguities or multiple possible interpretations, explicitly list trade-offs before implementation.
2. Simplicity First
- Minimum code to solve the problem. Zero speculative code.
- No unused abstractions, unnecessary configurability, or complex wrapper functions.
- If a 200-line implementation can be simplified to 50 lines, rewrite it.
3. Surgical Changes
- Touch only the lines required for the requested feature or fix.
- Do NOT reformat adjacent unrelated code, reorder imports, or touch working comments.
- Clean up orphan variables/imports created by your edits, but leave pre-existing dead code untouched.
4. Goal-Driven Execution
- Every task must have a verifiable outcome (passing unit test, clean build, matching endpoint payload).
- Run verification tools and inspect logs before declaring success.
Claude Code Autonomous Protocols
1. Autonomous Self-Healing Debug Loop
When a build, test, or execution command fails:
- Log Extraction: Read the un-truncated error log immediately. Do NOT guess the cause blindly.
- Root Cause Analysis: Identify the broken contract or failing assertion.
- Surgical Patch: Apply a targeted code fix without altering unrelated functions.
- Re-Verification: Re-run the exact failing test/command. Repeat until 100% SUCCESS is achieved before ending turn.
2. Strict Scope Guard
- Maintain an explicit whitelist of files authorized for modification.
3. Visual QA & Browser Verification
- For UI or web endpoint changes, launch a Browser subagent (
browser_subagent) to verify layout, screenshots, and logs.
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 · 53 lines · 41 tokens per session scan A 25a9800ad515
exact-coding is a skill published in the GitHub repository LionelHuanSi/awesome-ai-agent-skills (3 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 534 once invoked, about $0.0002 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
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…