「first run」の検索結果

Restructure existing code without changing its behavior, through a test-gated refactoring loop: a named target, a green suite over that target before any edit, a planned sequence of small named refactorings, and the full suite re-run after every step. Use when the user wants to refactor, restructure, clean up, simplify, or improve the design of existing code, or to apply refactoring recommendations from a code-review or architectural-analysis report. This skill changes code; it does not review code (use code-review), assess architecture (use architectural-analysis), plan a multi-module restructure before executing it (use plan-a-change), map domain boundaries before restructuring along them (use ddd-analysis), or build new behavior test-first (use tdd). Do not use it on code inside an active tdd loop; the refactor step of tdd owns that cleanup. Runs its planned sequence to completion without pausing for review; to review each named refactoring as it lands, use pairing.

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Write code through a disciplined, BDD-framed Test-Driven Development loop: build a behavior test list, then drive each behavior through red-green-refactor with an enforced observed-failure gate. Use when the user wants to implement, build, or write code test-first, "do TDD", follow "red-green-refactor", drive code from tests, choose the next test by the Transformation Priority Premise (TPP) or ZOMBIES ordering, or grow a feature behavior-by-behavior with tests leading. This skill writes and changes code; it does not produce a test plan document (use automated-test-planning, or manual-test-planning for a plan a person runs by hand), review or audit existing code (use code-review), restructure existing code outside a TDD loop (use refactor, or plan-a-change to plan a multi-module restructure), specify what a feature should do (use plan-a-feature), or find the root cause of a bug (use investigate). Runs its loop to completion without pausing for review; to review each behavior as it lands, use pairing.

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Use database cache when external APIs aren't reliably idempotent. Use when: (1) External API claims idempotency but returns different values for same input, (2) Re-running a script creates duplicate resources, (3) Need stable identifiers across runs but external service generates new ones. Pattern: check database cache first, only call external API for genuinely new items, cache the result.

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Connect this agent to a running Guaardvark (self-hosted AI studio) and check what it can do right now. Use first when the user mentions Guaardvark, asks to generate images/video/music/voice locally, run a Film Crew, train a LoRA, launch a swarm, or when a guaardvark MCP tool is missing or a call fails with "not reachable" / "plugin offline".

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Analyzes test suites and suggests optimal test execution order based on code change impact and project requirements. Use when optimizing CI/CD pipelines, deciding which tests to run before committing, or selecting critical tests under time constraints. Prioritizes tests by analyzing recently modified files, test dependencies, and coverage overlap. Outputs detailed rankings with priority scores and reasoning to help teams run the most impactful tests first.

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Goal-driven E2E testing where a browser agent (Playwright MCP / computer-use) reads a natural-language goal and explores the app via the accessibility tree to assert outcomes — no pre-written script. Covers when intent-driven beats scripted, making agent runs deterministic (pinned model, temperature 0, seeded data, bounded steps, explicit success assertion, snapshot-not-pixel), cost/latency control, the accessibility-tree-first interaction model, CI gating, and graduating a stable run into a scripted Playwright test. Use when: "agentic browser test," "goal-driven browser test," "let an agent explore the app," "natural-language E2E," "browser agent smoke test," "Playwright MCP test." Not for: Writing/maintaining deterministic scripted Playwright tests — that is playwright-automation. Testing your product's OWN LLM features — that is ai-system-testing. Related: playwright-automation, ai-system-testing, exploratory-testing, test-reliability, qa-project-context.

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Vulnerability audit and dataflow analysis built on the neug CPG engine. file_audit (default, recommended): first builds a function-level summary graph (tier1) over the entire project, then incrementally builds detailed CPGs (tier2) on demand for chosen functions; both tiers coexist in a single neug database (unified schema, cross-layer refines edges; C/Java/Python/Rust). All capabilities are exposed as CLI tool subcommands (audit/overview/build/query/methods/paths), so an agent can drive the audit step by step via shell; the audit subcommand can also run the built-in LLM loop. The same tooling also supports a dataflow analysis mode (Workflow C): no vulnerability hunting, only variable-level dataflow tracing for functions of interest, producing dataflow_analysis.json. repo_audit: multi-round LLM/Cypher audit over a full-repo CPG. Use when the user asks to audit a codebase/project for vulnerabilities, scan a project with CPG/neug, run repo_audit/file_audit, find source-to-sink taint paths, or analyze dataflow inside a function. Supports C, Java, Python, and Rust.

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Interactively pick a quick-win contribution in any GitHub repo — either a labeled issue ("good first issue", "help wanted", docs) or a repo-scan candidate (typo, missing tests, i18n gap, actionable TODO). Runs a TDD dev-loop, opens a PR via gh, and returns the PR URL. Use when the user wants to auto-contribute to an open-source project end-to-end. Trigger words: auto-contribute, open a PR for me, find a good first issue, contribute to <repo>.

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Suede AI CI and branch-protection wiring for any repo and any stack: path-aware jobs, a single aggregator required check that cannot deadlock, lockfile hygiene, runtime pinning from the repo, and the exact branch-protection settings. Use when asked to set up CI, protect main, make CI block a bad merge, fix a required check that hangs pending forever, or repair duplicate or misfiring pipelines. Detects the repo's real apps, package managers, and runtime versions first; emits workflow files and settings, never pushes or flips protection itself. NOT FOR: reviewing or grading the change the gate is failing on (use suede-code); designing the AI eval cases to wire in (use suede-ai-eval); branch and worktree hygiene (a private Suede Labs companion, not in this pack).

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Use when writing or running Godot tests - gdUnit4 or GUT (whichever the repo has), unit tests on typed GDScript classes, scene runners for frames, physics, input and signals, headless CLI with --import first, reports and exit codes, and C# tests via gdUnit4Net or plain dotnet test.

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Use when a backend or worker service needs a cloud deploy - container-first GitHub Actions deploys to Google Cloud Run (WIF) or AWS ECS/App Runner (OIDC), with migrate-before-deploy and a health-check gate

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The definition of done. Run before calling any change done, fixed, verified, ready or shippable, for features and bug fixes alike. Checks the pre-mortem was answered, proves the tests fail on the old code, gets a fresh breaker review, runs CI's own checks in a clean checkout, checks the user-facing words, and writes the evidence report. Also use when asked "is it done", "is it ready", "did you verify it" or "can we merge".

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Learns the words a user habitually writes to their coding agent that make its answers worse ("be 100% sure", "don't assume", "full review", "are you sure?", "all fine, right?"), from what they actually typed in their recent Claude Code sessions. Shows what each one does, what went wrong after it, and what to say instead, then writes the habit words block that maps each word to the checks it should trigger, so the checks run without the words. Use when asked to learn, read, refresh or review the user's habit words or prompts, "which of my words make you worse", from setup-first-pass's profile step, or when the start-of-session check says the habit words are due.

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Scaffold the test framework and CI/CD pipeline for the project's engine. Creates the tests/ directory structure, engine-specific test runner configuration, and GitHub Actions workflow. Run once during Technical Setup phase before the first sprint begins.

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Author and run a durable AI chat agent with chat.agent from @trigger.dev/sdk/ai: the per-turn run loop, why you MUST spread ...chat.toStreamTextOptions() first, returning a StreamTextResult vs calling chat.pipe(), the two server actions (chat.createStartSessionAction + auth.createPublicToken), and wiring useChat to useTriggerChatTransport. Load this when building, modifying, or debugging a chat backend (the agent task or its lifecycle hooks) or its React transport, when declaring typed tools or custom data parts, or when migrating a plain AI SDK streamText route to chat.agent.

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Run the complete 5-step development workflow: focus problem → prevent over-development → test-first (TDD) → document → smart commit. Use when starting a new feature, or when the user runs /workflow or asks for the full development flow.

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Set up concise, guidance-first Claude/Codex/Grok project context after inspecting the repository. Use on first setup, after cloning, or when the user runs /project-init.

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First step of deploying a new warp route (steps 1–9). Reads a Linear ticket to extract token details and chain configuration, looks up mailbox addresses from the local registry, generates the deploy.yaml, runs the deploy, and optionally runs a send test. Follow up with /warp-deploy-update-owners.

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Validate and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

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Strategic owner of Horizon, the VNX future-state layer (roadmap -> tracks -> deliverables) and the plan-first gate. USE THIS when the user wants to plan the next VNX feature, decide what to build next, add something to the roadmap, prioritize or schedule (inplannen) work into now/next/later horizons, break a feature into deliverables, set the routing FLOOR, or run the plan-gate on a feature. The tracks DB (`vnx horizon`, alias `vnx objective`) is the source of truth; the repo ROADMAP.yaml is a generic example, not the SSOT. Plans and gates only: never dispatches, never closes open-items. The heavy multi-model plan-gate panel runs only on an explicit plan-gate step. (Renamed from `pm` 2026-07-05 — `/pm`/`@pm` still resolve via the backward-compat alias in `.claude/skills/pm/SKILL.md`.)

日本語の概要は準備中です。原文の説明を表示しています。

Strategic owner of Horizon, the VNX future-state layer (roadmap -> tracks -> deliverables) and the plan-first gate. USE THIS when the user wants to plan the next VNX feature, decide what to build next, add something to the roadmap, prioritize or schedule (inplannen) work into now/next/later horizons, break a feature into deliverables, set the routing FLOOR, or run the plan-gate on a feature. The tracks DB (`vnx horizon`, alias `vnx objective`) is the source of truth; the repo ROADMAP.yaml is a generic example, not the SSOT. Plans and gates only: never dispatches, never closes open-items. The plan-gate panel is proportional (0-5 seats, by governance weight) and runs only on an explicit plan-gate step. (Renamed from `pm` 2026-07-05 — `/pm`/`@pm` still resolve via the backward-compat alias in `.claude/skills/pm/SKILL.md`.)

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Use when the user wants to run the optimization loop, improve agent performance, evolve the agent, or iterate on quality. Requires .evolver.json to exist (run harness:setup first).

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Bruno is an open-source, offline-first API client that stores collections as plain-text .bru files on your filesystem. It serves as a privacy-focused, git-friendly alternative to Postman and Insomnia with no cloud sync and no account required.

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One command from "plugin installed" to "running brief on your idea." Combines /mycelium:setup (project-state initialization) and /mycelium:interview (10-minute discovery brief) into a single flow. Run this immediately after installing the Mycelium plugin — it's the recommended entry point for first-time users.

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