diagnose-ci-failures

A troubleshooting workflow for failed continuous integration, linting, type checks, builds, or tests. Continuous integration means automated checks that run for code changes.

In plain words
What is it for?
It helps capture the failing command and error, determine the failure type, reproduce it locally, identify the root cause, make the smallest fix, and rerun the relevant check.
Why use it?
It separates real code problems from flaky or environment-related failures and keeps fixes focused on the first actionable cause.

Skill for Claude CodeCodex

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 skills/pymodel/pythinker-cli/diagnose-ci-failures
Any agent
npx skills add PyModel/pythinker-cli --skill diagnose-ci-failures
Clone the repo
git clone --depth 1 https://github.com/PyModel/pythinker-cli

Made for: Claude Code, Codex.

Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 236 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 $0.00031 $0.00236
Opus 5 $0.00015 $0.00118
Sonnet 5 $0.00006 $0.00047
Haiku 4.5 $0.00003 $0.00024

Measured 2d ago against content hash 5773c47e03ae, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

diagnose-ci-failures 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 2d 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.

src/pythinker_code/skills/diagnose-ci-failures/SKILL.md · 37 lines

What it actually says

Diagnose CI Failures

Use when a CI job, local test, lint, typecheck, or build gate fails.

Workflow

  1. Capture the failing command, exit code, and the first actionable error.
  2. Identify whether the failure is deterministic, flaky, environmental, or caused by recent code.
  3. Reproduce with the narrowest local command when practical.
  4. Name the root cause before editing.
  5. Make the smallest fix that addresses the root cause.
  6. Rerun the focused failing gate and report the result.

Rules

  • Do not skip hooks or weaken tests to make CI pass.
  • Do not hide unrelated failures; separate them from the fixed failure.
  • Prefer targeted tests over full-suite runs until the focused failure is fixed.
  • If a gate cannot run because of missing tools or credentials, report that clearly.

Output

Return:

SUMMARY
ROOT CAUSE
CHANGES
VERIFICATION
REMAINING RISKS
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. 2d ago First seen · 37 lines · 31 tokens per session scan A 5773c47e03ae

Subscribe to this mod's changes

diagnose-ci-failures is a skill published in the GitHub repository PyModel/pythinker-cli (20 stars, last pushed 5d ago), licensed Apache-2.0. It adds 31 tokens to every session and 236 once invoked, about $0.0002 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 2026-08-30.