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 hatch3r/hatch3r --skill hatch3r-ci-pipelinegit clone --depth 1 https://github.com/hatch3r/hatch3rWrote 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/hatch3r/hatch3r/hatch3r-ci-pipeline)<a href="https://agentmods.dev/skills/hatch3r/hatch3r/hatch3r-ci-pipeline"><img src="https://agentmods.dev/badge/skills/hatch3r/hatch3r/hatch3r-ci-pipeline/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/hatch3r/hatch3r/hatch3r-ci-pipeline"><img src="https://agentmods.dev/badge/skills/hatch3r/hatch3r/hatch3r-ci-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.01449 |
| Opus 5 | $0.00016 | $0.00724 |
| Sonnet 5 | $0.00006 | $0.00290 |
| Haiku 4.5 | $0.00003 | $0.00145 |
Grade A, and why
hatch3r-ci-pipeline 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CI Pipeline Workflow
Quick Start
Task Progress:
- [ ] Step 0: Detect ambiguity (P8 B1)
- [ ] Step 1: Audit existing pipeline
- [ ] Step 2: Design stage structure
- [ ] Step 3: Optimize test parallelization
- [ ] Step 4: Configure artifact management
- [ ] Step 5: Implement and validate
Step 0 — Detect Ambiguity (P8 B1)
Before any work, scan the invocation for unresolved questions in scope, intent, acceptance criteria, target environment, or irreversibility. If any are found, ask the user via the platform-native question tool per agents/shared/user-question-protocol.md. Do not proceed under silent assumption. Default path, not an exception. Triggers for THIS skill: CI platform (GitHub Actions vs GitLab vs CircleCI vs Azure Pipelines), pipeline duration target, runner sizing budget, deploy gate (auto vs manual approval for prod), and artifact retention policy.
Fan-out Discipline (P8 B2)
Fan-out scales with task size; token cost never justifies serializing independent work (rules/hatch3r-fan-out-discipline.md P8 B2; agents/shared/efficiency-patterns.md). Emit sub_agents_spawned: { count, rationale, task_structure } in your output.
Step 1: Audit Existing Pipeline
- Map the current pipeline stages, their dependencies, and execution times.
- Identify bottlenecks: which stages take the longest? Which block others unnecessarily?
- Check for flaky tests that cause unnecessary reruns.
- Review resource usage: are runners appropriately sized? Are caches effective?
- Measure total pipeline duration from push to deployable artifact.
Step 2: Design Stage Structure
- Organize into logical stages: install, lint, typecheck, unit test, integration test, build, deploy.
- Maximize parallelism: lint, typecheck, and unit tests can run in parallel after install.
- Use fail-fast: if lint fails, skip tests. If unit tests fail, skip integration tests.
- Gate deployments behind all quality checks.
- Separate environment-specific deployment stages (staging, production) with manual approval gates for production.
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.
- 7d ago First seen · 111 lines · 32 tokens per session scan A c06eef1231e6
hatch3r-ci-pipeline is a skill published in the GitHub repository hatch3r/hatch3r (26 stars, last pushed 6d ago), licensed MIT. It adds 32 tokens to every session and 1,449 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 2026-09-03.
Other skills, from other repositories
e2e-testing
Guide for running end-to-end tests of the Qwen Code CLI, including headless mode, MCP server testing, and API traffic inspection. Use this skill whenever you need to verify CLI behavior with real model calls, reproduce user-reported bugs end-to-end, test MCP tool integrations, or inspect raw API request/response…
terminal-capture
Automates terminal UI screenshot testing for CLI commands. Applies when reviewing PRs that affect CLI output, testing slash commands (/about, /context, /auth, /export), generating visual documentation, or when 'terminal screenshot', 'CLI test', 'visual test', or 'terminal-capture' is mentioned.
agent-reproduce-align
Use after a Codex or Claude Code feature has been implemented in Qwen Code to run the selected reference agent and Qwen Code under the same scenario, capture HTTP and terminal traces, compare request bodies, tool/function schemas, outputs, and iterate until the reproduced behavior is close enough.
issue-resolve
Use when an assessed gflow-cli issue (verdict CONFIRMED-BUG or LIKELY-BUG) has localized, verifiable scope and should be driven to a fix. Mutating and gated: it works in an isolated worktree, fixes test-first, and opens a DRAFT PR for human review. Built to run autonomously (hermes-ops) within a strict action envelope…
test-microflows
Write and run MDL-based microflow tests with mxcli test — annotations, file formats, and the warm local test loop. Use when testing microflow logic rather than the UI: return values, entity changes, control flow.
verify-with-oql
Verify microflow side effects and data changes with OQL against a running app. Use after executing changes to confirm data was created, updated or deleted as expected, or to back a browser test with a data assertion.