「fact」の検索結果

Create a staged refactor plan for a module. Use when a mid-level developer needs a safe refactor path.

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 statistics and claims in blog posts by fetching cited source URLs and checking if the claimed data actually appears on the page. Extracts all statistical claims (numbers, percentages, named sources), fetches each cited URL via WebFetch, and scores match confidence (exact match 1.0, paraphrase 0.7-0.9, not found 0.0). Flags uncited claims as UNVERIFIED. Use when user says "fact check", "verify statistics", "check sources", "validate claims", "factcheck", "source verification".

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。

Suede Labs Android app factory: plan, build, test, and release a production-grade native Android app from a product idea through Google Play. Use for requests to create, ship, submit, monetize, or modernize an Android app. Covers Kotlin and Jetpack Compose architecture, API-level policy verification, accessibility, privacy and Data Safety, Play Billing, Play Integrity, account deletion, testing, performance, store assets, signing, staged rollout, and a release evidence gate. NOT FOR: iOS work (private Suede Labs companion, not in this pack: ios-app-factory), a review-only pass on an existing app (use suede-code-review), or live Play listing and keyword audits on a shipped app (use suede-aso).

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Implements behavior changes with a test-first red-green-refactor loop: write one meaningful failing test, watch it fail for the intended reason, make the smallest change, then refactor while green. Use for new behavior, bug fixes, refactors with preserved contracts, and regression tests. Not required for disposable prototypes, generated files, or documentation-only edits.

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 a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.

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。

Perform a read-only, evidence-first intake of an existing mini-program repository before planning or modifying code. Use when users ask to take over, understand, audit, resume, scope, or continue a WeChat or other mini-program project; when project facts may conflict with historical documents; or when an agent needs the framework, rules, Git state, build path, risks, unknowns, protected behavior, and change boundary. Produces a project fact map and handoff without changing code, installing dependencies, building artifacts, or claiming runtime, device, cloud, upload, acceptance, or release status.

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Applies fixes from a prior review-llm-artifacts run, with safe/risky classification. Respects verify-llm-artifacts output when present to skip false positives.

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 auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

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Systematic process for analyzing and modernizing legacy applications to comply with the 12-Factor App methodology. Covers code analysis, configuration externalization, dependency management, and container/Kubernetes best practices.

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Reference guide for the Twelve-Factor App methodology — 15 principles (12 original + 3 modern extensions) for building portable, resilient, cloud-native applications. Use when evaluating application architecture, designing cloud-native services, reviewing codebases for methodology compliance, advising on configuration, scaling, observability, security, and deployment patterns. Incorporates the 2025 open-source community evolution and cloud-native reinterpretations of each factor.

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。

Start or complete a specific refactoring task from a task file. Use when a sub-agent needs to pick up a refactoring task, update its status, implement acceptance criteria, and run verification steps.

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Validate a completed plugin refactor against its task evidence, baseline assessment, documentation, and current validators. Use after implement-refactor or when deciding whether refactoring is complete.

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。

Analysis criteria, transformation patterns, output format, and validation checklist for refactoring Claude Code agent prompt files. Load this skill when preparing to run the subagent-refactorer agent or when reviewing agent prompt files for structural, model optimization, or instruction quality improvements.

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。

Refactor Code: Use when user wants to "refactor" or "change" the code base.

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。

Reconcile one set of artifact kinds (the target) to reflect the current state of another set of artifact kinds (the source), while optionally honoring a filtering hint. Use when the user wants to "reconcile", "sync", "align", or "update" artifacts like SPEC, CODE, DOCS, TASK, INFR, or OTHR against each other.

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Comprehensive UI design guidance based on Refactoring UI principles covering design personality, color theory, layout strategy, visual hierarchy, typography, spacing, mobile-first responsive design, and writing copy that doesn't read like AI wrote it. Use this skill whenever the user is creating new pages, building components, making design additions or changes, reviewing UI code, asking about design decisions (colors, fonts, spacing, layout), writing or editing interface copy, or discussing visual improvements. Trigger for any design-related conversation including "how should this look", "what color", "font size", "spacing", "layout", "responsive", "mobile", "this copy", "headline", "button text", "microcopy", or when reviewing/critiquing existing interfaces.

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What this project has already learned the hard way, and where any new such fact belongs. Two jobs. (1) Routing: this project keeps NO per-project Claude memories — read this before writing anything down, and pick a durable home from the list here. (2) An index, by area, of non-obvious facts worth knowing before you guess: debugging rigs and measurements that lie, iOS/WebKit and the tethered iPhone, the Windows app, production and deployment, and general codebase traps. Use before setting up a browser- or server-driven debugging session, when a bug reproduces only on iPhone, in Safari or in the Windows app, before changing deployment configuration or reading store/crash data, when something behaves unexpectedly and you are about to guess why, and whenever you are tempted to record a fact for later.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for CI/CD, registry, dependency drift, artifact provenance, image build, release pipeline, and runtime consumer challenges. Use when the user asks to trace dependency drift, registry pulls, malicious packages, build or release tampering, CI execution, artifact signing, or which shipped artifact the runtime actually consumes. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for DFIR chronology, cross-artifact correlation, persistence chains, and incident timeline reconstruction. Use when the user asks to build a forensic timeline, correlate EVTX, PCAP, registry, disk, memory, mailbox, or browser artifacts, explain the order of attacker actions, or pinpoint the stage where the decisive artifact appears. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Render a completed diamond's work as a shareable, factual one-page "work receipt" (problem framed, assumptions tested, what was killed vs kept) carrying an origin line + a volitional onward-handoff data flow. Read-only. Consults the attribution registry per `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` consent + privacy gate. Follows the render fleet's shared conventions + consent gate (Check 43 applies via the `-render` name); a standalone shareable-artifact generator, NOT a `/render` dispatcher target. Audience fixed `external` (a hand-it-over artifact).

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Unified entry point for creating or updating OpenCode tools (skills, agents, commands) with dependency awareness. Orchestrates the creation of multiple related artifacts in a single session. Use when user says "create opencode tool", "new opencode tool", "oc-tool-dev", or wants to create/update an OpenCode artifact and may need supporting artifacts.

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Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.

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Refactor an over-long or poorly structured SKILL.md by extracting examples to references/EXAMPLES.md, splitting compound procedures, and reorganizing sections for progressive disclosure. Use when a skill exceeds the 500-line CI limit, when code blocks dominate the skill body, when a procedure step contains multiple unrelated operations, or after a content update pushed the skill over the line limit.

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Four-stage alchemical code transmutation — nigredo (decomposition), albedo (purification), citrinitas (illumination), rubedo (synthesis) — with meditate and heal checkpoints between stages. Transforms tangled or legacy code into optimized, well-structured output through systematic material analysis. Use when transforming legacy code into modern equivalents, refactoring deeply tangled modules where incremental fixes keep failing, converting a codebase between paradigms, or when simpler refactoring approaches have stalled and a full-cycle transformation is needed.

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。