「search skill」の検索結果

Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store

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 an energy-optimized, time-blocked daily plan based on circadian rhythm research and GTD principles

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 personalized cold outreach sequences for LinkedIn and email. Use when someone needs to reach prospects, warm up cold leads, or build a systematic outreach engine. Covers research, connection requests, follow-ups, and conversion.

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 pre-meeting prep brief in Claude Code. Researches participants, pulls vault context, builds agenda, surfaces sharp questions. Use when user says "prep for this meeting," "I have a call with," "meeting tomorrow with," or "prep brief for [name/company]."

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。

Get one actionable improvement for your AI product based on the latest GenAI PM briefs. Fetch the last 5 days of curated AI PM insights from genaipm.com, analyze the current repo/project, find synergy between trending topics and the user's work, then research the source material and apply a concrete improvement. Use when the user wants to improve their AI product, get coaching on AI PM best practices, apply the latest industry insights to their codebase, or run "/one-step-better-ai-pm". Requires a GenAI PM subscriber email (set GENAIPM_EMAIL env var or provide when prompted).

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。

Builds a pricing model, packaging/tier structure and willingness-to-pay research plan, then grades your existing pricing page or plan. Use when you ask 'how much should we charge', 'what should our pricing model be', 'seat vs usage vs outcome pricing', 'good-better-best tiers', 'what goes in the free plan', 'should we raise prices', 'freemium vs free trial', or 'how do we price our AI product'. Draws on 199 insights from 79 Lenny's Podcast guests including Madhavan Ramanujam, Naomi Ionita and Patrick Campbell.

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。

Builds a role scorecard, interview loop, sourcing plan, reference-check guide and close plan for a hire, and grades an existing job description or loop. Use when you say 'I need to hire a PM', 'write a job description', 'interview questions', 'how do I run references', 'first sales hire', 'first marketer', 'head of product search', 'scorecard' or 'how do I close a candidate'. Draws on 210 Lenny's Podcast guests including Adam Ward, Keith Rabois and Lauren Ipsen.

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。

Manage Azure DevOps projects, work items, repos, PRs, pipelines, wikis, test plans, security alerts, variable groups, environments/approvals, branch policies, and attachments. Use when user asks to: manage sprints, create/update work items, list repos, create PRs, run pipelines, search code, manage wiki pages, check security alerts, manage variable groups, approve deployments, or configure branch policies. Covers 13 domains with 99 tools via REST API.

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 and write Google Sheets spreadsheets - get content, update cells, append rows, fetch specific ranges, search for spreadsheets, and view metadata. Use when user asks to: read a spreadsheet, update cells, add data to Google Sheets, find a spreadsheet, check sheet contents, export spreadsheet data, or get cell values. Lightweight integration with standalone OAuth authentication supporting full read/write access.

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。

Interact with Google Docs - create documents, search by title, read content, and edit text. Use when user asks to: create a Google Doc, find a document, read doc content, add text to a doc, or replace text in a document. Lightweight alternative to full Google Workspace MCP server with standalone OAuth authentication.

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 and rewrite an Instagram profile end-to-end for 2026: profile photo, searchable NAME field weighted with a keyword, @handle, bio (150 chars: value plus topic plus proof), goal-matched link, category label, story highlights, the first-9 grid, and up to 3 pinned posts. Triggers on "review my Instagram profile", "fix my bio", "optimize highlights", "profile audit". The whole follow decision happens on the profile header. Not for writing captions (use ig-caption-writer).

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。

Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.

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。

Trigger for: explaining current FAR, DFARS, or agency-supplement text; determining documented acquisition-policy status for an agency and FAR part; comparing codified text, RFO model text, and agency deviations; tracing acquisition rulemaking; finding procurement comment periods; analyzing public comments; refreshing prior policy analysis; or producing an Acquisition Policy Impact Brief. Use published federal sources and supplied policy documents without making legal or procurement-specific applicability determinations. Do not use for clause selection, market research execution, opportunities, pricing, grants, or cooperative agreements.

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。

Trigger for: writing, revising, converting, or descoping a federal Statement of Work, Performance Work Statement, SOW, PWS, or SOO; develop executable requirements; define contract scope; create measurable performance standards; or prepare requirements before an IGCE. Produce a contract-file-ready .docx plus separate chat-only staffing and Section B handoffs. Never place FTEs, SOC codes, staffing estimates, IGCE content, CLINs, or pricing schedules inside the SOW/PWS body. Do NOT use for the IGCE, market research report, acquisition plan, QASP, or clause-selection matrix.

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。

Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, agent browsing, research. Triggers: browser, web automation, scrape, navigate, click, fill form, screenshot, browse web, playwright, headless browser, web agent, surf internet, record video

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。

Trigger for: Other Transaction or OT should-cost, cost estimate, cost-share analysis, milestone pricing, funding profile, proposed-price comparison, prototype price analysis, research OT budget, production follow-on OT estimate, or OT price-reasonableness support under 10 U.S.C. 4021 or 4022. Build auditable milestone-based .xlsx workbooks using BLS OEWS wages, CALC+ positioning, GSA Per Diem, materials, cost sharing, and agreement-specific fees. Use for pre-solicitation budgets and proposal comparisons. Do NOT use for FAR-based IGCEs, OT project descriptions, grants, or an Agreements Officer's determination. Requires the bls-oews, gsa-calc, and, when travel applies, gsa-perdiem MCP servers.

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。

Screens biomedical / life-science papers for signs of data fabrication, image manipulation, and statistical anomalies, using the detection techniques distilled from the field's canonical exposure platforms (PubPeer, Data Colada, Science Integrity Digest, For Better Science) and tools (ImageTwin/Proofig, statcheck, GRIM/GRIMMER, Problematic Paper Screener, Seek & Blastn). Use when asked to check a paper/figure for image duplication, blot splicing, impossible statistics, paper-mill or tortured-phrase signals, research integrity, or "is this data faked"; or when a user shares a figure, Western blot, supplementary dataset, or DOI and asks whether it looks manipulated. Reports observable anomalies as questions for clarification — it never accuses anyone of fraud.

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。

Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and config.

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。

Coder-agent working-file budget discipline: keep the editable working set (files you /add into writable context) under ~25k tokens, separate "read" from "edit", delegate breadth to a read-only repo-map, and drop files once edited. Use when an LLM coder-agent edits multiple files, when the working set must stay focused, or when the model starts editing the wrong file / missing targets because too much context dilutes attention. Search keywords: context window full, agent edits wrong file, too much context, /add /drop files, working file budget, context dilution, lost in the middle.

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。

Enhancement overlay for multi-agent / tool-using coder agents. Encodes the per-agent tool- scoping discipline that role-based frameworks (CrewAI, LangChain) document only as a passing best-practice: which agent gets which tool, and why blanket-sharing every tool to every agent is a correctness and blast-radius risk. Activates when an agent system has tools AND there is more than one agent (or one agent holding many tools). Treat a tool as a capability grant; scope by least-privilege. ENHANCE overlay — read alongside [[crewai]], [[agentsop-http-tool-wrapping]], [[agentsop-llm-tool-idempotency]]. Search keywords: which tools per agent, least-privilege agent, agent tool access, tool permissions, limit agent tools, scope tools to roles.overlay_type: enhancement

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。

Decision protocol for making side-effectful agent tools idempotent — so when an LLM tool call is retried (timeout, framework resume, user re-run, model duplicate emit), the second call is a no-op instead of a double-send. The load-bearing premise: the LM cannot promise it'll call exactly once; the tool must promise the second call is safe. Framework-agnostic — applies to LangGraph node bodies that re-run on resume, MCP tools, OpenAI tool-calling retries, CrewAI delegated tool invocations, and direct HTTP wrappers. Search keywords: duplicate email sent, charged twice, exactly-once, idempotency key, tool called twice, retry side effect, double-send, at-least-once delivery.

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。

Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.

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。

Enhancement-overlay SOP for adding sparse (BM25 / keyword) retrieval alongside dense (embedding) retrieval. Activate when a calling agent is building, reviewing, or debugging a retrieval pipeline whose corpus contains exact-match tokens — identifiers, error codes, SKUs, API/function names, proper nouns, citations, rare jargon — that pure dense embedding silently misses. Encodes the single decision rule (**hybrid is traffic-driven, not theoretical: add sparse only when the query share that depends on exact tokens is non-trivial**), the wiring of QueryFusionRetriever-style fusion (RRF vs alpha-weighted), and per-query-type alpha tuning. Frame the work as recovering lexical identity that dense pooling destroys, not as "add keyword search for completeness". Cross-links [[llamaindex]].

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。

Split a multi-call LM workflow by cognitive load, not by accuracy: let one strong model make the few reasoning decisions and a cheap model do the many mechanical executions (Aider architect+editor, DSPy optimizer-LM vs task-LM, vLLM speculative draft+target, LangGraph supervisor+worker are the same shape). Use when designing or cost-optimizing a pipeline that calls an LM many times, when deciding which steps need a strong reasoner vs a cheap executor, or when adding an escalation valve for when the cheap tier degrades. Search keywords: reduce LLM cost, cheaper model, lower token cost, model cascade, route to cheap model, strong model plus cheap model, LLM cost optimization.

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。