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 skills add ANcpLua/ancplua-claude-plugins --skill pipeline-investigationgit clone --depth 1 https://github.com/ANcpLua/ancplua-claude-pluginsWrote 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/skills/ancplua/ancplua-claude-plugins/pipeline-investigation)<a href="https://agentmods.dev/skills/ancplua/ancplua-claude-plugins/pipeline-investigation"><img src="https://agentmods.dev/badge/skills/ancplua/ancplua-claude-plugins/pipeline-investigation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ancplua/ancplua-claude-plugins/pipeline-investigation"><img src="https://agentmods.dev/badge/skills/ancplua/ancplua-claude-plugins/pipeline-investigation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00159 | $0.04334 |
| Opus 5 | $0.00079 | $0.02167 |
| Sonnet 5 | $0.00032 | $0.00867 |
| Haiku 4.5 | $0.00016 | $0.00433 |
Grade A, and why
pipeline-investigation scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- `curl` for downloading task logs and build artifacts How it starts
The opening of the file, as written. The whole thing — 317 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pipeline Investigation
Investigate AzDO pipeline failures that aren't Helix test failures — build errors, infrastructure tooling crashes, validation test flakiness, and artifact cascade failures. Complements helix-investigation by covering everything else in the pipeline.
When to Use This Skill
- User has an AzDO build URL with a non-Helix failure (build step, validation, infra task)
- User asks "why did the pipeline fail" and the failure is in a build/scan/validation task
- User wants to know how often a specific failure occurs (frequency/trend analysis)
- User sees
exit code null, 1ES PT errors, or MSBuild failures - User wants to understand artifact cascade failures ("missing artifacts from prior build")
- User asks about SourcelinkTests, Binary Analysis Scan, or installer validation failures
Output Formats
This skill produces two distinct report types. Match the format to the request:
Health Assessment ("pipeline health", "are builds passing", "pipeline status")
Follow references/health-assessment-format.md. Output MUST include these two tables:
-
Failed Builds Table — every failed build, classified and investigated: | Build | Type | Source | Failure Detail |
- Type: Rolling, Forward Flow, or Other PR (classify via
gh pr view) - Source: Rolling → branch name. Forward Flow →
target ← source-repo. Other PR → short description.
- Type: Rolling, Forward Flow, or Other PR (classify via
-
Summary Table — pass/fail breakdown by build type: | Type | Completed | ✅ Pass | ❌ Fail | Pass Rate |
-
Failure Trends Table (conditional — include when 3+ builds in scope and at least one pattern recurs; cap at top 5): | Pattern | Hits | Window | Status |
- Status: ❌ No issue filed, ✅ Fix merged, 🔄 Known issue (link), ⏳ Fix in progress
See the reference for build classification rules, branch filtering, and codeflow analysis methodology.
Save the report: After presenting the health assessment, save it to reviews/pipeline-health-<slug>-YYYY-MM-DD-HHMM.md in the repo, where <slug> identifies the pipeline or scope (e.g., unified-build, runtime-ci). Include the timestamp to ensure uniqueness across multiple investigations per day. Include a Methodology section at the end documenting data sources and classification approach.
What ships with it
2 files 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.
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.
- 12d ago First seen · 317 lines · 159 tokens per session scan A f8b808a0ab68
pipeline-investigation is a skill published in the GitHub repository ANcpLua/ancplua-claude-plugins (6 stars, last pushed 26d ago), licensed MIT. It adds 159 tokens to every session and 4,334 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
Verification & Quality Assurance
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
mcore-create-issue
Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.
debug-task
Diagnose and fix moon tasks that are broken, misconfigured, or behaving unexpectedly. Use this skill when a moon task is failing, not running, skipped, hanging, producing stale or wrong output, cached when it shouldn't be, re-running every time when it should be cached, or when outputs are empty or missing after a…
operating-github-ci-fixer
Use when the user asks OpenSRE to fix failing GitHub CI, GitHub Actions checks, failing pull request checks, a broken PR branch, or CI on a named branch such as main.
meta-long-running-build-watchdog
Watches a long-running command via tmux, lets sub-agent diagnose failures and propose a fix, and records the diagnosis to memory. Designed for overnight model fine-tunes, CI image builds, or repeated regression suites that may fail intermittently.
tracely
Instrument AI agents with Tracely and turn their production traces into CI gates. Use when the user mentions Tracely, tracely-ai, tracelysdk, the tracely CLI, or asks to trace/observe an AI agent, add LLM evaluators or LLM-as-a-judge columns, debug why a trace or conversation isn't showing up, wire agent regression…