「fact」の検索結果

Generate developer behavioral profile and create Claude-discoverable artifacts

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。

Post-mortem investigation for failed GSD workflows — analyzes git history, artifacts, and state to diagnose what went wrong

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。

Generate a comprehensive project summary from milestone artifacts for team onboarding and review

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。

Extract decisions, lessons, patterns, and surprises from completed phase artifacts

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。

Implement bounded features and fixes in existing or new mini-program codebases while preserving repository rules, user-owned changes, approved behavior, assets, framework conventions, and evidence boundaries. Use when users ask to write or modify mini-program code, implement a confirmed specification or architecture, add a scoped feature, remove behavior semantically, update internal documentation required by a change, or carry out a well-defined small fix that does not require root-cause investigation. Establishes a baseline and change boundary, applies test-driven small steps, distinguishes source from generators and build artifacts, verifies the affected contract, and never reports source completion as device validation, formal acceptance, upload, or release.

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。

Orchestrate evidence-first engineering for WeChat and other mini programs from a vague idea or existing repository through project discovery, product specification, architecture, implementation, debugging, device adaptation, verification, and release readiness. Use when users ask to build a mini program from zero to one, take over an existing mini-program project, deliver a cross-stage feature, diagnose project status, coordinate development and testing, or judge whether a mini program is ready to upload or release. Enforces fact discovery, change boundaries, stage gates, evidence-calibrated status, sensitive-information redaction, and continuity after side questions.

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。

Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.

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。

Govern mini-program release readiness and evidence across source branches, semantic versions, build artifacts, test flags, debug paths, sensitive data, permissions, privacy declarations, platform uploads, review submissions, staged versions, production releases, and rollback plans. Use when users ask for a release checklist, packaging, export, version bump, upload readiness, submission readiness, launch status, rollback preparation, or a precise judgment of what has and has not shipped. Defaults to read-only preflight, requires separate explicit authorization for every external state change, and never treats a Git push, successful build, platform upload, review submission, approval, or production publication as interchangeable.

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。

Convert a vague mini-program idea, feature request, or partially documented product into an evidence-calibrated product specification with target users, core problem, version-one scope, main and exception flows, page responsibilities, state matrices, and testable acceptance criteria. Use when users ask to define an MVP, clarify requirements, organize product flows, specify page behavior, resolve ambiguous product states, or prepare a stable handoff before architecture or implementation. Separates facts, user decisions, assumptions, unknowns, and future ideas; never invents product logic merely to complete a screen or technical plan.

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。

Use this when summarizing analysis results, writing reports, documenting experiment outcomes, or presenting model evaluation — including structuring findings in Japanese with conclusions, facts, assumptions, interpretations, and caveats.

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。

Seven-role depersonalised panel review of framework artefacts (KUD, criterion bank, LT definition, crosswalk, scope-and-sequence) in sequential-isolation mode. Gate rule mean>=88 AND no role<70.

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。

Map a complex topic by placing factors on hexagonal tiles where adjacency signals a claimed relationship. Use when students need to surface hidden connections in a system before analysis or action.

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。

Design the deeper patterns, structures, artefacts, and mental models needed to grow a desired event. Use when a class imagines a better future.

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。

A harness that makes Opus (or any Claude model) behave like Fable — it enforces seeing a task through to the end, with evidence and verification, as procedure. Use when starting a multi-step task (2+ sequential stories), long autonomous work, debugging or root-cause investigation, building render/executable artifacts (HTML, SVG, games, charts), or when the user says "fablize", "see it through", "verify as you go", "split into goals".

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。

Simplify recently changed code for clarity, reuse, quality, and efficiency while preserving behavior. Use for tidy/refactor passes; use ce-debug for bugs.

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。

Refresh docs/solutions learnings against the current codebase. Use when auditing stale, overlapping, superseded, or drifted learnings; avoid general refactor, debugging, or code review unless docs/solutions is explicit.

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。

Simplifies code for clarity. Use when refactoring code for clarity without changing behavior. Use when code works but is harder to read, maintain, or extend than it should be. Use when reviewing code that has accumulated unnecessary complexity.

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。

Drives development with tests using the red-green-refactor loop. Use when implementing any logic, fixing any bug, or changing any behavior. Use when you need to prove that code works, when a bug report arrives, or when you're about to modify existing functionality.

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。

Runs one or more installed review lenses — adversarial critique, edge cases, verification gaps, structure, prose — and reports triaged findings. Use when, and only when, the user asks you to review a diff, a pull request, or an artifact — code or documents, one or many — and actually says "review"; an explicit skill:bmad-review directive from another skill counts as that ask. A request to act on feedback from an earlier review is a change, not a review. Never invoke this uninvited, including on edits you just made.

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。

Build, revise, audit, or rebut a research manuscript from Harness Research evidence. Use for paper intake, related-work research, claim and citation control, section architecture, LaTeX patching, figure planning, reviewer response, or final manuscript hardening when the workspace contains Harness research artifacts.

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。

Convert run artifacts into calibrated interpretation and next decisions.

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。

Generate accurate, runnable quantitative strategy code for the JoinQuant (聚宽) platform. Use this skill when the user asks to write a stock/ETF/futures backtest, paper-trading, or live-trading strategy on JoinQuant — the skill provides progressive-disclosure access to the complete JoinQuant API knowledge base (14 categories, 294KB official docs), 5 production-ready strategy templates (single-stock MA, multi-factor, ETF rotation, momentum, mean-reversion), and a lint tool that catches hallucinated APIs, future-function violations, and unset price-mode/slippage/commission. Chinese-friendly. Code generated by this skill should paste directly into the JoinQuant online editor without modification.

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。

Find architectural friction in a codebase and propose deepening opportunities. Use when user wants to improve architecture, find refactor candidates, consolidate shallow modules, or make a codebase more testable. Outputs scored proposals (depth/leverage/locality) that surgeon and review can consume.

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。

Use when designing, reviewing, or refactoring a CLI that must serve AI agents alongside humans, or when converting an API or SDK into an agent-usable CLI interface.

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。