Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
This is the Pocock-style seam discipline. test-driven-development is AOS's general TDD skill; use this one when the question is where to test and what a good test is.
TDD is the red → green loop. This skill is the reference that makes that loop produce tests worth keeping: what a good test is, where tests go, the anti-patterns, and the rules of the loop. Every section applies on every cycle: consult them before and during the loop, not after.
When exploring the codebase, read CONTEXT.md (if it exists; see the domain-modeling skill) so test names and interface vocabulary match the project's domain language, and respect ADRs in the area you're touching.
Tests verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't. A good test reads like a specification: "user can checkout with valid cart" tells you exactly what capability exists, and it survives refactors because it doesn't care about internal structure.
See tests.md for examples and mocking.md for mocking guidelines.
A seam is the public boundary you test at: the interface where you observe behavior without reaching inside. Tests live at seams, never against internals.
Test only at pre-agreed seams. Before writing any test, write down the seams under test and confirm them with the user. No test is written at an unconfirmed seam. You can't test everything, so agreeing the seams up front is how testing effort lands on the critical paths and complex logic instead of every edge case.
Ask: "What's the public interface, and which seams should we test?" Give each proposed seam a one-line note on what it catches and what it misses.
When the shape of that interface is itself in question (how deep the module is, where the seam belongs, what the interface should expose), call the Skill tool with "software-architecture" for the module and interface vocabulary. It is a reference to consult, not a session to run.
expect(add(a, b)).toBe(a + b), a snapshot derived by hand the same way, a constant asserted equal to itself), so it passes by construction and can never disagree with the code. Expected values must come from an independent source of truth: a known-good literal, a worked example, the spec.two-axis-code-review skill), not the red → green implementation cycle.还没有评价。欢迎分享你的使用感受。
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Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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