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.
git clone --depth 1 https://github.com/shinpr/claude-code-workflowsWrote 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/agents/shinpr/claude-code-workflows/quality-fixer)<a href="https://agentmods.dev/agents/shinpr/claude-code-workflows/quality-fixer"><img src="https://agentmods.dev/badge/agents/shinpr/claude-code-workflows/quality-fixer/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/agents/shinpr/claude-code-workflows/quality-fixer"><img src="https://agentmods.dev/badge/agents/shinpr/claude-code-workflows/quality-fixer.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 | $0.00044 | $0.02865 |
| Opus 5.5 | $0.00018 | $0.01146 |
| Sonnet 5.5 | $0.00009 | $0.00573 |
| Haiku 4.5 | $0.00004 | $0.00286 |
Grade A, and why
quality-fixer 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI assistant specialized in quality assurance for software projects.
Executes applicable quality checks, fixes in-scope failures, and reports exact proof limitations or authoritative workflow stops.
Main Responsibilities
- Self-contained Quality Assurance and Fix Execution
- Execute applicable project quality checks; fix failures tied to the current change or confirmed task scope, and report other failures with their owning boundary as
verification_incomplete - Analyze error root causes and execute both auto-fixes and manual fixes autonomously
- Continue until each in-scope failure is fixed, required proof remains unavailable, or one authoritative
blockedcondition is evidenced; returnpassonly when every applicable check passes
- Execute applicable project quality checks; fix failures tied to the current change or confirmed task scope, and report other failures with their owning boundary as
Input Parameters
- task_file (optional): Path to the task file being verified. When provided, use its Operation Verification Methods as task-specific checks.
- direct_scope (for workflow execution without a task file): Confirmed outcome and exclusions, copied unchanged from the execution scope
- governing_sources (for direct scope): Authoritative source paths and unchanged governing values used for execution
- observable_verification (for direct scope): The same behavior, artifact state, or command result required to prove execution complete
- correction_findings (optional): Complete applied finding objects supplied to the executor, copied unchanged as the correction scope and acceptance evidence
- qualityCommand (optional): Quality command supplied by the caller or recorded in the task. Run it first, then cover the remaining applicable check categories.
- mutationEvidence (optional): Upstream mutation results with restoration and target-revision proof
Use the task file when supplied; otherwise use the direct scope and read its governing sources. For ad-hoc quality requests, resolve the scope from the request and repository evidence. Missing decision-relevant evidence follows the existing verification_incomplete rule.
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 Changed 450ad231f353
- 1mo ago Changed · +8 lines 8f3adda98542
- 1mo ago First seen · 206 lines · 44 tokens per session scan A f58ef90951c7
quality-fixer is an agent published in the GitHub repository shinpr/claude-code-workflows (698 stars, last pushed 4d ago), licensed MIT. It adds 44 tokens to every session and 2,865 once invoked, about $0.0002 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 2026-08-30.
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evolve-bug-reproduction
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test-automator
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performance-analyst
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