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 strikersam/autonomous-ai-agency --skill auto-fixgit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/strikersam/autonomous-ai-agency/auto-fix)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/auto-fix"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/auto-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/strikersam/autonomous-ai-agency/auto-fix"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/auto-fix.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.00646 |
| Opus 5 | $0.00000 | $0.00323 |
| Sonnet 5 | $0.00000 | $0.00129 |
| Haiku 4.5 | $0.00000 | $0.00065 |
Grade A, and why
auto-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 12d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: auto-fix
Purpose
Automatically detect and fix linting, formatting, and type errors across the codebase. Acts as a one-shot "clean up everything" command before committing or opening a PR.
Trigger
Use when:
- Pre-commit cleanup is needed
- CI is failing due to lint/type errors
- Code was written quickly and needs polish
- You want to ensure the codebase is green before review
Process
Step 1: Discover Fix Commands
Read CLAUDE.md, package.json, pyproject.toml, Makefile, .pre-commit-config.yaml to find:
- Lint fix commands (eslint --fix, ruff check --fix, black, prettier)
- Type check commands (mypy, tsc --noEmit, pyright)
- Format commands (isort, autopep8, gofmt)
Step 2: Run Fixers (Auto-fixable)
Execute all auto-fixable tools in order:
- Formatters first (black, prettier, gofmt)
- Import sorters (isort, organize-imports)
- Lint fixers (eslint --fix, ruff --fix)
Capture output for each. Note what was changed.
Step 3: Run Checkers (Non-auto-fixable)
Run tools that report but cannot auto-fix:
- Type checkers (mypy, tsc, pyright)
- Strict lint rules that require manual intervention
Collect all errors with file:line references.
Step 4: Manual Fix Loop
For each remaining error:
- Read the file at the error location
- Understand the error message
- Apply the minimal correct fix
- Re-run the checker to confirm resolved
- Never suppress errors with
# type: ignoreor// eslint-disableunless it's genuinely unfixable and documented why
Step 5: Final Verification
Run the full check suite one more time. All checks must pass before declaring done.
Step 6: Report
Summarize:
- Files auto-formatted: N
- Lint issues auto-fixed: N
- Type errors manually fixed: N
- Remaining issues (if any): list them with explanations
Rules
- Never introduce
anytypes to silence TypeScript errors - Never use
# noqaor# type: ignorewithout a comment explaining why - Prefer fixing root causes over suppressing symptoms
- If a fix requires understanding business logic, flag it rather than guess
- Run tests after fixing to ensure no regressions
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.
- 12d ago First seen · 80 lines · 0 tokens per session scan A 8fa8e2f3a43e
auto-fix is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 646 tokens. 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
langsmith-tracing
LangSmith tracing and debugging setup for LLM applications. Configure observability, capture traces, and enable debugging for LangChain/LangGraph agents.
behavior-contract
Bug condition/postcondition formalization as testable Behavior Contracts. Defines invariants that must be preserved across fixes.
quality-hooks
Language-specific auto-lint/format/typecheck pipeline. Supports Python (ruff+pyright), TypeScript (prettier+eslint+tsc), Go (gofmt+golangci-lint). Auto-fix and convergence loops.
systematic-debugging
Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.
debugging-patterns
Root cause analysis frameworks including log-first investigation, git bisect correlation, and pattern-based diagnosis with confidence scoring.
hotfix-triage
Urgent issue classification, root cause analysis, and fast-path routing for production hotfixes.