Borrowing it
Nothing to install: this file belongs to arnaudgelas/K-Ops. 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/arnaudgelas/K-Ops/main/.claude/agents/lint-healer.mdgit clone --depth 1 https://github.com/arnaudgelas/K-OpsWrote 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/arnaudgelas/k-ops/lint-healer)<a href="https://agentmods.dev/agents/arnaudgelas/k-ops/lint-healer"><img src="https://agentmods.dev/badge/agents/arnaudgelas/k-ops/lint-healer.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.00089 |
| Opus 5 | $0.00014 | $0.00044 |
| Sonnet 5 | $0.00005 | $0.00018 |
| Haiku 4.5 | $0.00003 | $0.00009 |
Grade A, and why
lint-healer 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 8d 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.
What it actually says
You are the Lint + Heal agent.
Read the full vault and make focused quality improvements.
Priority order:
- unsupported claims
- contradictions
- missing backlinks
- duplicate concept names
- sparse pages and missing overview pages
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.
- 8d ago First seen · 17 lines · 27 tokens per session scan A c7233221fe04
lint-healer is an agent published in the GitHub repository arnaudgelas/K-Ops (1 stars, last pushed 19d ago), licensed MIT. It adds 27 tokens to every session and 89 once invoked, about $0.0001 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 agents, from other repositories
code-mapper
Use when quick reconnaissance is complete and an unfamiliar or risky bounded scope needs a read-only trace or thorough map of ownership, contracts, consumers, and unknowns.
inbox-processor
Process inbox items using GTD principles. Categorize, clarify, and organize captured notes into actionable items. Use for inbox zero and capture processing.
note-organizer
Organize and restructure vault notes. Fix broken links, consolidate duplicates, suggest connections, and maintain vault hygiene. Use when managing vault organization or cleaning up notes.
bug-investigator
Use when a bug, regression, or unexpected behaviour needs its root cause traced to specific code before any fix is designed. Investigates only — never edits files. Returns a structured report citing file:line evidence.
worker-lint
Runs lint.py against the wiki, parses tiered output, returns severity summary. Use before quarterly reviews or when user asks for wiki health check.
deep-reasoner
Fasi ad alto ragionamento - architettura, debug complesso, design di algoritmi. Pensa a fondo, restituisci una conclusione concisa su cui l'orchestratore possa agire.