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 AKSoftCode/aicrew --skill aicrew-fixgit clone --depth 1 https://github.com/AKSoftCode/aicrewWrote 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/aksoftcode/aicrew/aicrew-fix)<a href="https://agentmods.dev/skills/aksoftcode/aicrew/aicrew-fix"><img src="https://agentmods.dev/badge/skills/aksoftcode/aicrew/aicrew-fix/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/aksoftcode/aicrew/aicrew-fix"><img src="https://agentmods.dev/badge/skills/aksoftcode/aicrew/aicrew-fix.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 | $0.00032 | $0.00560 |
| Opus 5.5 | $0.00013 | $0.00224 |
| Sonnet 5.5 | $0.00006 | $0.00112 |
| Haiku 4.5 | $0.00003 | $0.00056 |
Grade A, and why
aicrew-fix 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 — 38 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aicrew /fix (Codex)
Use for fast bug fixes. This mirrors the /fix command but runs inline in Codex.
Token foundation (mandatory)
/dev, /fix, and /quick all share the same 11-capability token-saving stack — only pipeline depth differs. Full reference: ~/Agents/docs/token-foundation.md. Stack: graph-first (codebase-memory-mcp), speculative Scout → verify (SCOUT schema, two-model routing), Karpathy guardrails, layered guardrails (guardrails-taxonomy.md), context-economy read policy, security-guard.py hooks, .ai/state checkpoints, /compact between phases, /handoff on tool switch, optional context-mode + token-optimizer-mcp, caveman default output. For /fix, Scout opens Phase 1 Bug Analysis before the bug-analyst deep dive.
Default output
Caveman/lean style by default. See ~/Agents/agents/caveman.md and ~/Agents/agents/context-economy.md. /normal or /lean off restores verbose.
Source of truth:
~/Agents/commands/fix.md- Project overrides in
.ai/skills/and repoAGENTS.md(if present)
Token foundation (mandatory — all phases):
- Graph-first: codebase-memory-mcp (search_graph → trace_path → get_code_snippet) before any file read
- Speculative context: Scout pass at start of Phase 1 Bug Analysis; emit SCOUT: schema; verify before bug-analyst deep dive
- Layered guardrails: security-guard.py (input) → karpathy-guardrails (Phase 2 implement) → security-reviewer (Phase 4)
- Context economy: always on; slice reads only during Scout
- Two-model routing: Scout on haiku/mini; fix on sonnet
- See: ~/Agents/docs/token-foundation.md
Workflow summary:
- Ask the 3 clarifying questions (symptom, expected behavior, repro).
- Scout pass (graph-first) → emit SCOUT: schema → verify → bug-analyst deep trace → confirm root cause.
- Load karpathy-guardrails; write the smallest failing test or reproducible check.
- Implement the minimal fix to make it pass.
- Run targeted tests + smoke path.
- Security review on changed files.
- Conclude with summary, tests run, and any risks.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 38 lines · 32 tokens per session scan A ade384b5306b
aicrew-fix is a skill published in the GitHub repository AKSoftCode/aicrew (3 stars, last pushed 3mo ago), licensed MIT. It adds 32 tokens to every session and 560 once invoked, about $0.0001 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.
Other skills, from other repositories
08-debug
Reproduce and fix a known bug, or find an unknown root cause by hypothesis validation. Use when the user wants to fix a bug, find why something breaks, or reopen a stuck investigation. Not for building a feature or reviewing a diff.
polya-fix
Phase 5 of the Pólya Heuristic Coder: First-Principles Bug Remediation, Seam Tracing, Bisection Search (O(log n)), Failing Reproduction Tests, and Incubation Circuit-Breaker Gate (docs/bug-reports/{slug}-bugfix.md).
bugfix-quick
Fast bug fixes with root cause investigation + TDD. Enforces 'no fix without root cause' discipline and verification protocol. Without this skill, fixes are applied at symptoms instead of sources, and bugs return.
hotfix
Fixes an observed defect with reproducible evidence in one call: writes a short trace doc before touching code, implements the fix, and backs it with a regression test written before the fix. Production incidents are the motivating case, not a gate. When blocked, it halts by name and saves the doc for a later call to…
mushi-integration
Full end-to-end Mushi Mushi integration smoke test: bug capture → AI triage → story mapping → TDD test generation → approval → execution → PDCA cycle. Use when "test mushi integration", "verify full pipeline", "mushi e2e check", "does mushi work end-to-end", "smoke test mushi", or after deploying changes.
tdd-bug-report
Workflow for analyzing bug reports, tracing root causes, and generating structured bug-fix implementation plans enforcing the Prove-It pattern (failing regression test first) with rollback strategies.