classify-ci-failure

classify-ci-failure is a skill for Codex from OutlineDriven/outline-driven-development. It costs 64 tokens per session (1,573 once invoked), scanned A, original, Apache-2.0.

A read-only way to label a CI problem, such as a failed, missing, delayed, unstable, or unexpected check.

In plain words
What is it for?
Use it to examine check status and logs, classify the failure, and produce a reviewable repair plan when the required pull-request details are available.
Why use it?
It gives the issue a consistent owner and next step without changing code before there is evidence for a fix.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to examine check status and logs, classify the failure, and produce a reviewable repair plan when the required pull-request details are available.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/outlinedriven/outline-driven-development/classify-ci-failure
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.

Any agent
npx skills add OutlineDriven/outline-driven-development --skill classify-ci-failure
Clone the repo
git clone --depth 1 https://github.com/OutlineDriven/outline-driven-development

Made for: Codex.

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 classify-ci-failure

README.md
[![agentmods](https://agentmods.dev/badge/skills/outlinedriven/outline-driven-development/classify-ci-failure.svg)](https://agentmods.dev/skills/outlinedriven/outline-driven-development/classify-ci-failure)
Your own site
<a href="https://agentmods.dev/skills/outlinedriven/outline-driven-development/classify-ci-failure"><img src="https://agentmods.dev/badge/skills/outlinedriven/outline-driven-development/classify-ci-failure.svg" alt="Measured on agentmods" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,573 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00064 $0.01573
Opus 5 $0.00032 $0.00787
Sonnet 5 $0.00013 $0.00315
Haiku 4.5 $0.00006 $0.00157

Measured 4d ago against content hash 66c1ebb3ff82, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

classify-ci-failure 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 4d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.devin/skills/classify-ci-failure/SKILL.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Classify CI failure

Contract

Field Bound contract
Trigger A CI check is failed, absent, pending too long, unstable, or reported unexpectedly.
Authority Read-only. No file, VCS, credential, paid, published, deployed, or remote mutation. No patching during classification.
Side effect Chat output: one deterministic failure class, the next owner, and a reviewable fix plan.
Done The class is supported by observed evidence, nothing was mutated, the next action is explicit, no-CI and blocked states are surfaced rather than skipped, and the fix plan is emitted when a PR and logs are available.

Inputs

Required: the CI check name, its status (failed, absent, pending, unstable, or unexpected), and the run identifier or URL that produced the report.

Optional: the failing job log, the changed files or commit range under test, any prior classification of the same check, the current git branch with an open PR, and an authenticated GitHub CLI (gh) for log extraction. The fix plan is produced when a PR and gh are available; without them, classification stands alone.

Procedure

  1. Record the check name, status, and run identifier before reading anything else. If the status is absent or the check never ran, treat that as a distinct input, not a missing one. Done when: the check name, status, and run identifier are recorded.
  2. Read the failing job log and any error, exit code, or annotation the run produced. Preserve the exact failure line, signal, or message. Done when: the failure signal is read and preserved exactly.
  3. Compare the failure against the commit range and changed files under test. Determine whether the failing code path was touched by the change or predates it. Done when: the failing path is determined as in-diff or pre-existing.
  4. Classify the failure into exactly one class:
    • regression: the change introduced or exposed the failure; the failing path is in the diff.
    • flake/watch: the failure is timing-, order-, or environment-dependent; it passes on retry or across runs without a code change.
    • infrastructure: the failure is caused by the runner, network, quota, service outage, or resource exhaustion, not by the code under test.
    • configuration: the failure stems from build, config, dependency, or environment setup, not from product logic.
    • policy/absent-CI: the check is absent, skipped, not configured, or blocked by a branch-protection or policy rule.
    • human escalation: the evidence is insufficient, contradictory, or outside the five classes above; a human must decide. Done when: the failure is classified into exactly one class.
  5. Assign the next owner from the class: regression and configuration go to the change author; flake/watch goes to the test or platform owner; infrastructure goes to the platform or runner owner; policy/absent-CI goes to the repository or CI-config owner; human escalation goes to a human reviewer. Done when: the next owner is assigned from the class.
  6. State the next action the owner must take, concretely and in one sentence. Done when: the next action is stated in one sentence.
  7. If the same check was classified before and the new evidence matches the prior class, note the repeat; if it contradicts, re-classify from the new evidence. Done when: the repeat or contradiction is noted, or no prior classification exists.
  8. Emit a reviewable fix plan as the post-classification artifact. When a PR and an authenticated gh are available, gather the evidence first: set GH_PAGER=cat on every gh invocation (the GitHub CLI has no global --no-pager option and blocks on a pager in non-interactive contexts without it); view the PR with GH_PAGER=cat gh pr view <branch> --json number,title,url,state; fetch the check rollup with GH_PAGER=cat gh pr view <branch> --json statusCheckRollup; and extract each failed check's logs with GH_PAGER=cat gh run view <run-id> --log-failed, collecting error messages with file paths and line numbers, compilation errors, lint names, test failure messages and stack traces, and build root causes. If any check is still in progress, report the partial state and stop; do not diagnose incomplete checks. Before flagging a test failure as a regression, check whether the same test previously passed in CI and note environment-specific or flaky cases rather than treating them as CI regressions. Then write the fix plan with: a problem statement summarizing the failing checks, current state listing each error and its location, proposed changes grouped by error category (formatting, linting, compilation, test failures, platform-specific), and validation steps (fmt, lint, tests, presubmit). Fix one category at a time. Apply no code change. Done when: the fix plan carries the problem statement, current state, per-category proposed changes, and validation steps, with no code change applied, or the plan is skipped because no PR or gh is available.

Read the full file on GitHub · 55 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 4d ago First seen · 55 lines · 64 tokens per session scan A 66c1ebb3ff82

Subscribe to this mod's changes

classify-ci-failure is a skill published in the GitHub repository OutlineDriven/outline-driven-development (52 stars, last pushed 2d ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,573 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 2026-09-03.

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