「fal」の検索結果

Use when writing asyncio Python code — async/await coroutines, concurrent I/O with asyncio.gather, task creation and cancellation, semaphore rate limiting, producer-consumer queues, async context managers, async generators, WebSocket servers, aiohttp web scraping, async database operations, run_in_executor for blocking calls, or testing async code with pytest-asyncio. Covers FastAPI and aiohttp patterns, synchronization primitives, timeout handling, and common pitfalls like event loop blocking and missing await.

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 one falsifiable factual claim against evidence appropriate to the claim. Use when a concrete assertion about software, documentation, configuration, releases, or repository behavior must be classified as VERIFIED, REFUTED, or INCONCLUSIVE.

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 calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.

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。

Install and configure the GitHub CLI (gh) for AI agent environments where gh may not be pre-installed and git remotes use local proxies instead of github.com. Use when gh command not found, shutil.which returns None, need GitHub API access for issues or PRs or releases or workflow runs, or repository operations fail with 'failed to determine base repo' error. Provides auto-install script with SHA256 verification, GITHUB_TOKEN auth with anonymous fallback, and the required -R flag pattern for proxy environments. Covers project management via GitHub Projects V2, milestones via REST API, issue lifecycle templates, and label taxonomy management.

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。

Deprecated alias. Play work is now part of `deepline-gtm`, and the CLI documents plays itself. Use when a user or script still invokes `/deepline-plays`: immediately load and follow the `deepline-gtm` skill instead. Triggers: play, plays, workflow, waterfall, webhook, cron.

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。

Resolve LinkedIn profile URLs from name + company with strict identity validation to avoid 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 skillsaw reports a likely false positive, misses an edge case, behaves incorrectly, or needs an RFE. Gather a safe report and, only with explicit user permission, create a feedback bundle or open a GitHub issue.

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。

Interviews a researcher in rounds until an idea, design, or draft has no unexamined decision — numbered questions, each with why it matters and a recommended answer, facts looked up rather than asked, and settled decisions written to a file. Stages are idea (topic or hunch to a falsifiable question and contribution claim), design (estimand, identification, sample and power, measurement, analysis plan, pre-registration, venue), and defend (reviewer objections to a finished design or draft). Use when the user says "grill me", asks to stress-test or poke holes in an idea, design, or plan, asks what they are assuming, or brings an idea that is not yet a design. --plan grills software or process plans.

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。

Drafts or audits a scientific paper’s introduction and framing — the real-world "why", an evidence audit, the funnel from theory to a falsifiable if-then with a named estimand, bridges across experiments, and a contribution list matched to the design. Use when the user asks to write, restructure, or critique an introduction, motivate or frame a paper, repair a gap-in-the-literature opening, sharpen or calibrate a contribution claim, or connect experiments into one argument. Sentence-level flow and wording are out of 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。

Turns a theory into falsifiable, pre-registerable hypotheses: DGP and DAG, identification, estimands, SESOIs, test choice among NHST, equivalence, and minimum-effect tests, scope conditions, and hypothesis tiers. Use when converting a research question into testable predictions or predicting a null.

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。

Consults Fable as an independent, read-only reviewer at an effort the caller matches to the question. The calling session (usually Opus, or Haiku) holds the task and escalates one decision point. Use before committing to an interpretation or a substantial piece of writing or analysis, when stuck or not converging, when changing approach, or as a final check on a finished task. Fallback for when the native advisor tool is unavailable. Not for a session already running Fable.

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-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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。

Entity Framework Core best practices including NoTracking by default, query splitting for navigation collections, migration management, dedicated migration services, and common pitfalls to avoid.

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。

Value investing analysis using Graham (NCAV/net-net/defensive-investor) and Buffett (economic moat/ROE/FCF) methodologies. Covers single-stock diagnostics and batch screening for both Graham cigar-butt and Buffett quality-compounder criteria. Runs cross-statement reconciliation before scoring. Data from Longbridge CLI first, MCP fallback, WebSearch only for genuine gaps. Triggers: "格雷厄姆", "巴菲特", "捡烟蒂", "烟蒂股", "NCAV", "净流动资产", "护城河", "价值投资", "安全边际", "深度价值", "撿煙蒂", "煙蒂股", "淨流動資產", "護城河", "安全邊際", "Graham", "Buffett", "cigar butt", "net-net", "NCAV screen", "moat", "value investing", "margin of safety", "deep value", "quality compounder", "價值投資", "深度價值", "防御型投资者", "防禦型投資者"

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。

Extracts a company's visual identity from screenshots (best) or its website (fallback) and writes a DESIGN.md brand file: machine-readable tokens for colours, typography, spacing, corner radius and logo usage, plus short prose explaining how and when to use each, and a list of do's and don'ts. Any later page, deck, carousel or ad can read the tokens instead of guessing. Triggers: "brand kit", "brand guidelines", "extract our brand", "design tokens", "colours and fonts from", "visual identity", "DESIGN.md". Not for tone of voice (use /tov-guidelines) or messaging (use /messaging).

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。

Classifies raw pilot request → structured JSON {task_type, class, artifact, budget_estimate, confidence, routing_tag, resolution_path, schema_version, result_type, expected_result_kind, result_kind_resolution, hypothesis_relation}. Keyword-fast (<200ms) or Haiku fallback (<60s). Does NOT create WP or call executor.

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。

Designs multi-step agent workflows with tool usage, retry logic, state management, and budget controls. Provides orchestration diagrams, tool execution order, fallback strategies, and cost limits. Use for "AI agents", "agentic workflows", "multi-step AI", or "autonomous systems".

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 the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding work.

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 visual identity from screenshots (primary) or website URL (fallback) and compile into a DESIGN.md-format brand kit — YAML token frontmatter (colors, typography, rounded, spacing, components) + 8 ordered prose sections. The YAML tokens are machine-authoritative; downstream visual skills consume them directly. Writes to marketing/brand/brand-kit.md. Triggers - "brand kit", "brand identity", "visual identity", "design tokens", "extract brand", "DESIGN.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。

Build and query a local Markdown knowledge base ("vault"). TWO functions — (1) CONVERT raw files (PDF, Word/docx, PowerPoint/pptx, Excel/xlsx, csv/tsv, images, html, md/txt, json/yaml/code, audio/video) into clean Markdown with retrieval-friendly frontmatter; local-first (pandoc / python-pptx / openpyxl / pymupdf4llm / whisper), with cloud OCR (MinerU) only as a fallback. (2) ANSWER questions over the resulting vault with retrieval discipline — self-monitor coverage, flag missing/lossy content, and propose Maps-of-Content (MOCs). Triggers: "build/sync my local knowledge base", "convert these files to markdown for AI", "整理我的资料库", "把文件转成 md 给 AI 读", "本地知识库", "读我的本地 vault 回答", "这个主题我的资料里怎么说". Not for: one-off web research, or files that are already in a single doc you can read directly.

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。

Git Context Controller (GCC) v2 — Lean agent memory backed by real git. Stores hash + intent + optional decision notes instead of verbose markdown. Auto-bridges to aiyoucli vector memory when available. Dual mode: git-backed (lean index.yaml) or standalone (markdown fallback). Triggers on /gcc commands or natural language like 'commit this progress', 'branch to try an alternative', 'merge results', 'recover context'.

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。

Read Haskell package documentation, Haddock HTML, Hackage pages, or dependency source for cabal nix-style projects. Prefer locally built/cached docs and source from the cabal store, dist-newstyle, plan.json, and repo-cache; fall back to Hackage only for packages outside the project dependency set or when local docs are unavailable.

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。

Audit any URL against 17-point technical SEO checklist + algorithmic authorship rules. Uses /browse if available, falls back to checklist-based audit.

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。

Google App Script form-to-sheet integration with auto-retry, toast notifications, and Zalo/contact fallback. Reusable across any static website.

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。