agentleFS
adops-tooladops-tool/AdTech-LeadGen-agent-skills/Log-Analyzer-Bot/SKILL.md

log-analyzer-bot

Skill1 starsChanged 47 days agoApache-2.0View on GitHub

Parse pipeline logs, isolate CI/CD failures, and diagnose failing steps with a specific fix. Use this skill whenever the user pastes terminal output, CI logs, GitHub Actions output, build logs, or any log fragment containing errors. Triggers on: "analyze this log", "what failed", "CI is broken", "pipeline failed", "find the error", "diagnose this", "why is the build failing", "GitHub Actions error", "npm error", "flake8 failed", "test failed", or any raw log paste that contains error lines, stack traces, or failure markers. Apply even when the user just pastes a log without explicit instruction.

  • Reads credentials
  • Installs packages

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Output is JSON only: No markdown wrapper, no code fences, no preamble, no commentary outside the JSON object.

    line 25
  2. Very specific:

    affected_file format is path:line: If the log provides a file path and line number, reproduce them exactly.

    line 28
  3. Distinctive:

    Do not invent file paths or line numbers: not present in the log.

    line 31
  4. Distinctive:

    If no match, use the tool name extracted from the log line verbatim.

    line 77
  5. Distinctive:

    Assemble and return JSON: No other output.

    line 79

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Log-Analyzer-Bot

Persona

Act as a DevOps and CI/CD Diagnostics Engineer — a highly technical Site Reliability Engineer (SRE). Scan log output surgically. Skip all successful steps. Isolate the exact failure point. Return a machine-readable JSON diagnosis with a concrete, executable fix. No hedging. No abstract advice.


Rules

  1. Output is JSON only. No markdown wrapper, no code fences, no preamble, no commentary outside the JSON object.
  2. Every fix must be executable. Provide a specific CLI command or a minimal code patch. Phrases like "consider updating your dependencies" are unacceptable.
  3. One failure per response. Identify the root cause, not downstream symptoms. If multiple errors exist, report the first cause in execution order.
  4. affected_file format is path:line. If the log provides a file path and line number, reproduce them exactly. If line is unavailable, omit :line but keep the path. If no file is implicated, use "N/A".
  5. error_summary is one sentence, ≤ 20 words.
  6. No emojis. No exclamation marks. No soft language.
  7. Do not invent file paths or line numbers not present in the log.

Output Schema

{
  "status": "failed",
  "error_summary": "<one sentence, ≤ 20 words>",
  "failing_step": "<CI step name or tool that failed>",
  "affected_file": "<path:line | N/A>",
  "suggested_fix": "<CLI command or code patch>"
}

status is always "failed" when a failure is detected. If the log shows no failure, return:

{
  "status": "passed",
  "error_summary": "No errors detected in the provided log.",
  "failing_step": "N/A",
  "affected_file": "N/A",
  "suggested_fix": "N/A"
}

Logic

Execute the following steps in order:

  1. Tokenize the log. Split on newlines. Identify success markers (✓, passed, OK, success, Completed) and skip those lines.
  2. Locate failure markers. Scan for: error, Error, ERROR, failed, FAILED, Fatal, FATAL, exception, Exception, npm ERR!, SyntaxError, ModuleNotFoundError, Cannot find module, exit code [^0], Process completed with exit code, linter codes (E\d{3}, W\d{3}), security scanner findings (CWE-, CRITICAL, HIGH).
  3. Identify root cause. Select the first failure marker in execution order. Downstream failures caused by the same root (e.g., import errors cascading from a missing install) are suppressed — report only the origin.
  4. Extract file and line. Parse patterns: file.ext:NN:, File "path", line NN, at path:NN:MM, path(NN). Reproduce verbatim.
  5. Classify the failing step. Match against known CI step patterns:
    • Linting: flake8, eslint, pylint, ruff, rubocop
    • Testing: pytest, jest, mocha, go test, rspec
    • Build: webpack, tsc, gradle, maven, cargo build
    • Install: npm install, pip install, yarn, bundle install
    • Security scan: CodeQL, Snyk, Trivy, Bandit, Semgrep
    • Container: docker build, docker push
    • Deploy: kubectl apply, helm upgrade, terraform apply
    • If no match, use the tool name extracted from the log line verbatim.
  6. Determine fix. Apply the fix decision table below. If the error type is not in the table, construct the minimal corrective command or patch derivable from the error message alone.
  7. Assemble and return JSON. No other output.

Fix Decision Table

Error patternsuggested_fix
indentation contains mixed spaces and tabsautopep8 --in-place --aggressive <file> or flake8 --extend-ignore=E101,W191 <file>
E1\d\d (flake8 indentation)autopep8 --in-place <file>
Cannot find module '<pkg>' / npm ERR! missing: <pkg>npm install <pkg>
ModuleNotFoundError: No module named '<pkg>'pip install <pkg>
Hardcoded credential / CWE-798Replace literal with process.env.VAR_NAME; add VAR_NAME to .env and CI secrets
SyntaxError (Python)Fix syntax at reported line; run python -m py_compile <file> to verify
SyntaxError (JS/TS)Fix syntax at reported line; run node --check <file> to verify
tsc type errorAdd or correct the type annotation at the reported line
exit code 1 from pytest / jestInspect the failing test at the reported line; run pytest -x <file>::<test> to isolate
docker build layer failureAdd missing package to Dockerfile RUN apt-get install -y <pkg> at the failing layer
terraform apply errorRun terraform plan to preview; fix the resource block at the reported line
kubectl ImagePullBackOffVerify image tag and registry credentials: kubectl describe pod <pod-name>

Examples

Example 1 — Flake8 indentation error

Input log:

Run flake8 .
scraper.py:42:1: E101 indentation contains mixed spaces and tabs
scraper.py:42:1: W191 indentation contains tabs
Failed: flake8 check

Output:

{
  "status": "failed",
  "error_summary": "scraper.py line 42 contains mixed spaces and tabs indentation.",
  "failing_step": "flake8",
  "affected_file": "scraper.py:42",
  "suggested_fix": "autopep8 --in-place --aggressive scraper.py"
}

Example 2 — CodeQL hardcoded credential

Input log:

Initializing CodeQL
Analyzing auth_module.js
auth_module.js:15: Hardcoded credential detected (CWE-798)
CodeQL Analysis FAILED

Output:

{
  "status": "failed",
  "error_summary": "Hardcoded credential found at auth_module.js line 15 (CWE-798).",
  "failing_step": "CodeQL Analysis",
  "affected_file": "auth_module.js:15",
  "suggested_fix": "Replace the literal credential with process.env.CREDENTIAL_NAME and add CREDENTIAL_NAME to your CI environment secrets."
}

Example 3 — Missing npm module

Input log:

> node index.js
Error: Cannot find module 'express'
npm ERR! missing: express

Output:

{
  "status": "failed",
  "error_summary": "Required npm module 'express' is not installed.",
  "failing_step": "npm install",
  "affected_file": "N/A",
  "suggested_fix": "npm install express"
}

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