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概要と使いどころ
Protocol for auditing API surface coherence and type safety. Trigger when: - Evaluating API designs, interface type safety, or design elegance. - Prompt contains: /api-audit, API surface, API coherence, type safety.
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。
Capture a POC's signed-off behaviour into its spec ledger (`<poc>/spec.json`) — the structured, walkable behaviour contract graduation freezes as authoritative. Run at the moment a behaviour gets signed off ("ok, this works"), while it's fresh — the same trigger as the HANDOFF.md update, but capturing the *behaviour as a testable entry* rather than the *decision as prose*. Use when a POC behaviour is approved, or asked to "capture this spec", "write the spec entry", "log this to the ledger".
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。
The case-law chronicle of this repo's major failures — symptom, root cause, evidence, status, and the rule each battle minted — so nobody re-fights a settled battle or re-proposes a rejected fix. Use when a bug/incident looks familiar ("didn't this happen before?"), when you're about to propose an approach that may have been tried and rejected (e.g. shipping a migration from a package), when someone asks "why does rule X exist" / "why is done-by-lgtm a thing" / "what was incident 988", or when a bug-archaeology run hits the shallow-clone wall and needs the pre-June-2026 history. Trigger phrases: "has this broken before", "why can't packages ship migrations", "why did graduation fail", "why is the green-build lesson a rule", "what did we learn from incident NN". (For a live "build is green but the app is broken" symptom, start at crouton-diagnostics-index. NB keep this description free of any space-then-hash sequence — an unquoted YAML scalar treats space-hash as a comment start and silently truncates.)
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。
AI-powered design review for Figma components with weighted dual-scoring system. Evaluates Style Guide Implementation (70%) and LLM Metadata Accessibility (30%). For export, hands off to atomic-design skill.
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。
Automatic quality check on significant outputs via isolated sub-agent. Spawns reflection-agent for objective evaluation without conversation context bias.
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。
Solves complex, open-ended problems using an evolutionary search mechanism inspired by genetic algorithms. Generates a diverse population of candidate answers, scores them against a rubric, then iteratively applies selection, crossover, and mutation to breed progressively better solutions. Use this skill whenever the user wants to explore a solution space deeply, asks for the "best" answer to a subjective or multi-dimensional problem, wants to evaluate competing approaches and distill the strongest, or uses any of these triggers: "evolutionary search", "genetic algorithm", "evolve an answer", "breed solutions", "population-based search", or phrases like "give me multiple approaches and refine the best one". Also trigger when the problem is complex, open-ended, or has no obvious single correct answer and the user wants a high-quality, well-explored result, not just a quick response.
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。
exact-13 staging planを独立評価したいとき、4条件とdigestに束縛したplan-findings.jsonを生成したいときに使う。
確認点で利用者が見る深さを選んだあと打ち合わせ資料を作り手とは別の目で確かめたいとき、正確さ・言葉・見た目・シンプルさの指摘を資料の編集なしで受け取りたいときに使う。
生成された仕様書ドキュメントセットの完成度を独立 context で評価したいとき、上位概念trace・意思決定・マトリクス網羅性・設計知識反映・最新ドキュメント出典・prompt品質の 6 観点で合否判定したいときに使う。
生成済み plan を4条件と決定論ゲートで評価したいとき、context:fork で独立評価結果 plan-findings.json を取得したいときに使う。
生成済みプロンプトを C1-C4 と 4 パスで評価したいとき、context:fork で独立評価結果 findings.json を取得したいときに使う。
Notion ページを描画する直前に粒度を検証したいとき、info-collector-agent ページと同等の section 充足度を section_canonical_map 基準で機械検証したいときに使う。
run-extract-blueprint が生成した章別ブループリントの忠実性を独立 context で評価したいとき、事実/推測区別と粒度と被覆を検証し draft_hash に束縛した PASS/FAIL verdict をローカル品質ゲート (C01 の周回内の受入判定・差し戻し) へ渡したいときに使う。
生成済みSKILL.mdを評価したいとき、rubric準拠を確認したいときに使う。
生成した handout 資料が初心者に伝わるか読みやすさレビューを依頼したいとき、独立 context のレビュアーから指摘と根拠つきの verdict を回収したいときに使う。
36章 PKG-002〜008 / PKG-014 sub-check を実行したいとき、plugin package の静的検査結果を findings JSON で得たいときに使う。
The Eterdis Strategy Diagnostic — an AI-guided conversation that helps leadership teams understand their strategic situation before committing to a direction. This is the entry point skill. It runs an open-ended discovery conversation, detects which strategic frameworks are most relevant, and produces a strategy-ready brief. Use this skill when someone asks for a general strategic assessment, when they don't know which framework they need, when they want to "figure out where we stand," or when they're preparing for a strategy engagement. This skill orchestrates other Eterdis skills — it does not replace them. Triggers on: "help me think through our strategy," "where should we start," "we need to figure out what's going on," "strategic health check," "prepare for a strategy review," or any open-ended request for strategic thinking. Based on 20 years of consulting practice by Eterdis (eterdis.com).
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。
Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations. Triggers: "AIProjectClient java", "azure ai projects java", "Foundry project java", "ConnectionsClient", "DatasetsClient", "IndexesClient".
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 AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This i...
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。
Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility.
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。
Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes. Use for AI Foundry project management, versioned agents, and orchestration. Triggers: "AI Projects", "AIProjectClient", "Foundry project"...
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 AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.
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。
Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code spe...
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。
Ask which skill or flow fits your situation. A router over the user-invoked skills in this repo.
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。