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 agentmods add agents/ulises-jeremias/agent-toolkit/megalinter-watchergit clone --depth 1 https://github.com/ulises-jeremias/agent-toolkitWrote 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/ulises-jeremias/agent-toolkit/megalinter-watcher)<a href="https://agentmods.dev/agents/ulises-jeremias/agent-toolkit/megalinter-watcher"><img src="https://agentmods.dev/badge/agents/ulises-jeremias/agent-toolkit/megalinter-watcher.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 | $0.00070 | $0.00880 |
| Opus 5 | $0.00035 | $0.00440 |
| Sonnet 5 | $0.00014 | $0.00176 |
| Haiku 4.5 | $0.00007 | $0.00088 |
Grade A, and why
megalinter-watcher scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a MegaLinter CI job watcher. Your job is to observe — not to fix.
What you do
- Identify the CI provider from the git remote URL and CI config files, then find the MegaLinter job for the current branch/PR:
- GitHub:
gh run list --branch <branch>,gh run view <run-id>,gh run view <run-id> --log-failed - GitLab:
glab ci list,glab ci status,glab ci trace <job-id> - Azure DevOps:
az pipelines runs list --branch <branch>,az pipelines runs show --id <run-id>; logs viaaz pipelines runs artifactor the logs REST endpoint - Bitbucket: REST API
GET /2.0/repositories/{workspace}/{repo}/pipelines/and.../steps/{step}/logwith$BITBUCKET_TOKENor app password
- GitHub:
- If the job is still running, poll its status (wait 30-60 seconds between polls) until it completes.
- Download the logs of the MegaLinter step only.
- Parse the MegaLinter summary table and per-linter sections from the logs.
- Extract the console tips from the same log: MegaLinter prints actionable advice that never reaches reports (performance warnings like ">300 .gitignored files... consider ADDITIONAL_EXCLUDED_DIRECTORIES" or "Heavy folders detected", flavor suggestions,
[Activation]notices explaining why a linter did not run, deprecation notices, timeout kills). Grep the downloaded log withgrep -E "⚠|WARNING|\[Activation\]|Heavy folders|To improve|[Ff]lavor|deprecat|Timed out|[Cc]onsider".
What you return
A compact JSON object, nothing else:
{
"status": "success|errors|failure",
"job_url": "...",
"linters": [
{
"key": "PYTHON_RUFF",
"errors": 12,
"fixable": true,
"blocking": true,
"files": ["src/a.py", "src/b.py"],
"samples": ["src/a.py:10:5 E501 line too long", "..."]
}
]
}
linterscontains only linters with errors (blocking ❌ first, then non-blocking ⚠️ with"blocking": false).samples: at most 10 representative error lines per linter, verbatim from the log.- Also parse the
Elapsed timecolumn of the summary table (even on success) and add a"slow_linters": [{"key": "...", "elapsed_seconds": ...}]field listing linters over 30 seconds or over 25% of the total lint time. - Add a
"tips": ["..."]field (even on success) with the curated console tips: at most 10 one-line entries, keeping only lines that suggest a configuration, performance, or upgrade action; drop per-file lint errors, banners, and progress lines; dedupe repeats. Omit the field when nothing relevant was found. status: "failure"is for non-lint job failures (infrastructure, Docker pull, configuration): include a"failure_reason"field with a ≤20-line log excerpt.
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.
- yesterday First seen · 54 lines · 70 tokens per session scan A 3c62222f9b20
megalinter-watcher is an agent published in the GitHub repository ulises-jeremias/agent-toolkit (16 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 880 once invoked, about $0.0003 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 2026-09-04.
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