megalinter-watcher

megalinter-watcher is an agent for coding agents from ulises-jeremias/agent-toolkit. It costs 70 tokens per session (880 once invoked), scanned A, original, MIT.

An agent that watches MegaLinter, a collection of code-quality checkers, while it runs in a CI pipeline.

In plain words
What is it for?
Use it with GitHub Actions, GitLab CI, Azure Pipelines, or Bitbucket Pipelines to wait for a MegaLinter job and summarize its failures.
Why use it?
It keeps large CI logs out of the main conversation and returns only the lint errors and related tips.

Agent

Install

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.

agentmods
npx agentmods add agents/ulises-jeremias/agent-toolkit/megalinter-watcher
Clone the repo
git clone --depth 1 https://github.com/ulises-jeremias/agent-toolkit

Wrote 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.

agentmods badge for megalinter-watcher

README.md
[![agentmods](https://agentmods.dev/badge/agents/ulises-jeremias/agent-toolkit/megalinter-watcher.svg)](https://agentmods.dev/agents/ulises-jeremias/agent-toolkit/megalinter-watcher)
Your own site
<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>
Per session 70 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 880 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured yesterday against content hash 3c62222f9b20, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

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.

skills/quality/megalinter-setup/agents/megalinter-watcher.md · 54 lines

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

  1. 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 via az pipelines runs artifact or the logs REST endpoint
    • Bitbucket: REST API GET /2.0/repositories/{workspace}/{repo}/pipelines/ and .../steps/{step}/log with $BITBUCKET_TOKEN or app password
  2. If the job is still running, poll its status (wait 30-60 seconds between polls) until it completes.
  3. Download the logs of the MegaLinter step only.
  4. Parse the MegaLinter summary table and per-linter sections from the logs.
  5. 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 with grep -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", "..."]
    }
  ]
}
  • linters contains 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 time column 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.

Read the full file on GitHub · 54 lines

Changes

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.

  1. yesterday First seen · 54 lines · 70 tokens per session scan A 3c62222f9b20

Subscribe to this mod's changes

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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