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 hamzaPixl/pixl-ai --skill self-review-fix-loopgit clone --depth 1 https://github.com/hamzaPixl/pixl-aiWrote 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/hamzapixl/pixl-ai/self-review-fix-loop)<a href="https://agentmods.dev/skills/hamzapixl/pixl-ai/self-review-fix-loop"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/self-review-fix-loop/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/hamzapixl/pixl-ai/self-review-fix-loop"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/self-review-fix-loop.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 | $0.00074 | $0.01042 |
| Opus 5.5 | $0.00030 | $0.00417 |
| Sonnet 5.5 | $0.00015 | $0.00208 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade A, and why
self-review-fix-loop 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.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
This skill runs an iterative review-and-fix loop over code changes. Each iteration has two waves of parallel agents: 3 reviewers that find issues, then 3 fixers that resolve them. The coordinator consolidates findings between waves and partitions work across fixers to avoid conflicts.
The loop continues until reviewers find no meaningful issues or the iteration cap (10) is reached.
Why self-review works: Automated self-review catches 80%+ of issues before human review because it applies multiple specialized lenses (correctness, security, maintainability) in parallel — something a single reviewer rarely does consistently. This reduces review cycles and unblocks PRs faster.
Step 1: Scope
Determine the review scope:
- If there is a
git diff(staged + unstaged), that is the default scope - Otherwise, use the provided feature scope or file list
- Identify the changed files, their purpose, and the intent of the changes
Step 2: Review (Parallel — 3 Agents)
Spawn 3 reviewer agents (Explore type, read-only) in parallel:
- Reviewer A: Requirement coverage and behavioral correctness
- Reviewer B: Regressions, edge cases, error handling, security/privacy, performance
- Reviewer C: Tests, maintainability, instruction/policy compliance
Each reviewer returns findings with severity (P0-P3), evidence, and fix direction.
Step 3: Consolidate
The coordinator merges, deduplicates, and normalizes severity across all reviewer outputs:
- Merge findings from all reviewers
- Deduplicate (same file + same issue = one finding)
- Normalize severity across reviewers
- Produce a single prioritized backlog
- If the backlog is empty, the loop ends
Findings Packet Format
Write consolidated findings to .context/review-findings.json using the context packet standard (see references/orchestration/context-packet.md):
{
"type": "review",
"version": "1.0",
"metadata": {
"skill": "self-review-fix-loop",
"project": "PROJECT",
"created_at": "TIMESTAMP"
},
"payload": {
"findings": [
{
"severity": "P0",
"file": "src/api/users.ts",
"description": "Missing auth check on DELETE endpoint",
"fix_direction": "Add requirePermission guard before handler",
"evidence": "Line 42: router.delete('/users/:id', handler) has no guard"
}
]
}
}
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.
- 1mo ago First seen · 111 lines · 74 tokens per session scan A 4dd5b9f85403
self-review-fix-loop is a skill published in the GitHub repository hamzaPixl/pixl-ai (2 stars, last pushed 6mo ago), licensed MIT. It adds 74 tokens to every session and 1,042 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 2026-09-03.
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