「ast」の検索結果

Durable categorized record-keeping for any predicate-enabled walk. Trigger when: - Classifying a step's output for recording (decision, finding, debt, feedback). - Promoting a scratch artifact to the ledger, to repo/docs, or to a category home (.ledger/tech-debt/, .ledger/process-feedback/). - Auditing whether a draft is structurally ready for its destination. - Running a zero-context sensibility read before committing to promotion. - Recording a known limitation as a tech-debt entry (not prose in the log). - Recording a friction, miss, improvement, or process amendment. - Prompt contains: /record, record-keeping, promote, ledger note, zettelkasten, scratch-to-ledger, zero-context read, over-strip, classification trigger, tech-debt, process-feedback, amendment.

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Generate a daily "where are we?" digest from this repo's GitHub epics — a render-only HTML + plain-text email summarising the last 24h of activity plus a progress snapshot of every open epic. Use when asked for a "daily digest", "epic report", "status rapport", "what moved this week", or "where are we on the epics".

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。

When a bug or regression is reported, the FIRST step — before fixing — is to research how and when it was introduced (git archaeology), then record that finding on the tracking issue/PR. Use the moment a bug, error, broken build, or "this used to work" is reported, before writing a fix. Produces a first-bad-commit (or "not a code regression") note you paste onto the issue.

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At the close of an epic (or a difficult issue), post a postmortem roundup comment — what went well, what was hard (backed by evidence), and 1–3 concrete proposals to improve the skills/flows — then offer to turn accepted proposals into their own `workflow`-labeled tasks, and end with a `🔭 Next` handoff (a paste-ready next-session prompt + the next epic to start). Use at epic close, after the "verify the whole thing" rollup, or when asked to "do a retro", "postmortem this epic", "what did we learn", "how do we improve the workflow".

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。

Code Review Assistant. Reviews uncommitted changes or recent commits for pattern violations, security issues, and bugs. Optimized for solo dev moving fast. Use when reviewing code before or after committing.

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。

Before building new infrastructure or a non-trivial capability, check whether the Nuxt ecosystem, UnJS, Vite, or other OSS already solves it — within our constraints (Nuxt, OSS, self-hostable, no mandatory SaaS). Use when weighing build-vs-adopt or starting new infra/tooling.

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。

Pre-commit i18n check. Scans changed files for untranslated strings, missing locale keys, and locale sync issues. Fast and focused — only checks what changed. Use before committing or as a review add-on.

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 monthly digest of the skills in this repo's flow system — a render-only HTML + plain-text email listing every skill grouped by job, plus a "what was added / updated / removed since last digest" band computed from git history. Use when asked for a "skills digest", "what skills do we have", "skills report", "what skills changed", or to keep the skill surface from sprawling.

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AID Phase 3 - Implementation Planning with consolidation-first approach. Resolves contradictions between PRD and Tech Spec, creates consolidated master document, then breaks down into actionable tasks and populates Jira. Includes sprint planning and risk assessment.

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Senior System Architect for SaaS specifications. Security-first design with ISO 27001 compliance. Use for system architecture, tech specs, API design, technology choices, distributed systems, cloud infrastructure.

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End-of-session wrap-up. Save what was learned this session to auto-memory, verify the working tree is committed/pushed, and emit a brief resume prompt the user can paste next time. Invoke as /done to close a work session. Wrap-up is auto-memory ONLY — never writes a handoff.md or any handoff file.

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。

Scan code, dependencies, containers, IaC and SBOMs with Snyk via its first-party MCP server: authenticate, trust a folder, run the right scanner, read the findings, fix and re-scan. Includes package-health checks before adding a dependency.

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Solves complex, open-ended problems using a knockout tournament mechanism. Generates an initial population of diverse candidate answers, pairs them into head-to-head matches, produces a hybrid "child" solution from each pair, and advances the best of the three through a bracket until a winner emerges. Use this skill whenever the user wants to competitively select the best answer from a field of alternatives, asks for a "tournament" or "bracket" style search, wants a faster alternative to full evolutionary search, or uses phrases like: "run a tournament on this", "bracket-style search", "let solutions compete", "knockout search", "find the best answer by elimination", or "which solution wins?". Also trigger when the problem is open-ended and the user wants a clear winner quickly rather than a deep multi-generation refinement.

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。

Roblox and Luau game code standards. Writes, reviews, and fixes Script, LocalScript, and ModuleScript code and game systems such as shops, DataStore saves, RemoteEvents, combat, NPCs, and UI. Use when a request involves Luau code or a Roblox Studio project, even when Roblox is never named, when a game bug is reported with no file named, and when the user asks to keep these standards active. Also covers Rojo, Argon, Azul, Script Sync, Studio MCP, and community libraries such as ProfileStore, Knit, Trove, and Fusion. Applies to requests written in any language. Not for plain Lua outside Roblox (Neovim, LOVE, Defold, other engines) or for game design with no Luau to write or review.

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Build an evidence-backed working model of an existing codebase. Use when you must orient in a large or unfamiliar repository; explain architecture or a subsystem; trace a request, runtime, control, or data flow for a concrete question or bug; analyze a branch, commit, diff, or pull request for behavioral impact and blast radius; or assess AI-generated or changed code against requirements, contracts, invariants, tests, and runtime evidence. Default to read-only investigation. Do not trigger only because a coding task touches an existing repository; use when understanding or verification is the requested outcome or a substantial prerequisite.

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Get structured data from catalogued providers instead of scraping pages. Firecrawl Alexandria is a library of official APIs, licensed publishers, and Firecrawl indexes (financial series, company data, government contracts and law, podcasts, listings, research). Use this skill when the user needs records, listings, prices, filings, transcripts, or a dataset, says "is there an API for", "find a data source for", "pull the records", "get the dataset", or when assembling the answer by scraping would take many pages that one provider call returns directly. Discover with search, inspect with find_tools, execute with scrape.

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Continuous environmental scanning across six PESTEL categories — Political, Economic, Social, Technological, Environmental, Legal. Not a one-time analysis but an ongoing radar. Three modes. Diagnostic mode runs a full scan and prioritises forces by impact, speed, and reversibility. Review mode checks the watch list monthly for movement and new signals. Alert mode defines concrete monitoring triggers for each top force. Use whenever someone wants to understand external forces acting on their business, assess macro risk, prepare for planning, or check whether the world has moved since last time they looked. Connects to the strategy map (forces act on the chain) and Wardley Map (forces drive component evolution). Based on practice by Eterdis (eterdis.com).

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Track Two / internal innovation system for running small, fast experiments outside the core business. Runs in three modes — Diagnostic for designing the full Protoloop from scratch (intake channels, loop structure, team, exit paths), Review for running the monthly heartbeat meeting (loop-by-loop decisions, forward-loading, portfolio health), and Alert Triggers for defining tripwires that protect, feed, and maintain momentum in the system. Updates company-context.md after every run. Triggers on questions about innovation pipelines, intrapreneurship, Track Two strategy, experimentation systems, handling ideas that don't fit the core business, or building systematic exploration alongside exploitation. Based on 20 years of 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。

Use when starting any CodexAI task; load this baseline before routing, editing, or claiming work is done.

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, auditing, or redesigning a web UI; select fast, prototype, or studio from the requested scope.

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 for security review, audit, hardening, compliance, DevSecOps, network, infrastructure, or application security with focused references.

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 implementing web UI in React, Next.js, Tailwind, CSS, or GSAP against an approved design, fast-path brief, or prototype UX contract.

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Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).

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。

Restate the last message in plain human language, with no jargon.

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。