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概要と使いどころ
Translate technical analysis into business language. Use when explaining statistical concepts to non-analysts, simplifying technical findings, or bridging communication between data teams and business stakeholders.
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。
Structured peer review for analytical work. Use when reviewing teammates' analysis, providing constructive feedback, or establishing analysis quality standards.
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。
Produce a high-confidence, CONCISE implementation / architecture / strategy PLAN that follows the OMC plan system end to end. It first runs OMC's real requirements interview (auto-chains the `omc-plan` skill: one question at a time, explore-first, Analyst consult) to produce an initial plan in `.omc/plans/`, then DEEPENS that plan with a 3-round ITERATIVE fusion loop — each round an Opus 4.8 panelist (Agent subagent) and a GPT-5.5 panelist (via codex) critique/improve it IN PARALLEL and BLIND, then Opus 4.8 (the orchestrator) judges and synthesizes one tighter plan that seeds the next round, stopping early on convergence. Writes the final dense plan back to the same `.omc/plans/<slug>.md`, then offers the OMC handoff (`/omc-plan --review` → `/team` or `/ralph`). Use for high-stakes planning, architecture decisions, strategy calls, and hard debugging — especially when the user runs /plan or asks how to approach a non-trivial build. Reuses the `fusion` skill's run_codex.sh + judge rubric. NOT for trivial tasks (single pass is enough).
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。
This skill should be used when performing web research beyond a simple single search -- looking into topics, comparing options, investigating questions, finding recommendations, or any task where effective use of Exa, Firecrawl, and Reddit tools matters. Triggers on "research", "look into", "investigate", "compare", "find out about", "search for", "find information", "what do people think about", "what are the best", "look up", or multi-source search tasks. Also invocable explicitly by deep-research team members via the Skill tool.
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 your north star metric, a metric tree with owned input metrics and guardrails, and a review cadence, as a one-page metrics spec you can paste into a doc. Use it when you ask 'what should our north star be', 'which metric should this team goal on', 'our dashboard is a mess', 'how do we measure AI productivity', 'activation metric', 'OKR metrics', 'vanity metrics', or 'metrics review cadence'. Draws on 254 insights from 118 Lenny's Podcast guests incl. Sarah Tavel, Crystal Widjaja and Jessica Lachs.
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。
Runs a product quality review on a journey, feature, landing page, onboarding flow, or AI/agent surface and returns a friction log, a scored critical-journey review, and a taste bar your team can calibrate against. Use when someone asks is this good enough to ship, how polished should it be, why do users bounce, what is our quality bar, how do we build taste, or to review a design, prototype, onboarding, or checkout. Draws on 264 insights from 100 Lenny's Podcast guests incl. Katie Dill, Dylan Field and Stewart Butterfield.
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 pile of ideas, requests and goals into a confidence-scored roadmap plus a goals/OKR set the team can actually follow. Use when someone says 'prioritize', 'what should we build next', 'roadmap', 'now next later', 'ICE or RICE', 'write our OKRs', 'quarterly planning', 'too many priorities' or 'sales keeps changing the plan'. Draws on 133 Lenny's Podcast guests including Itamar Gilad, Christina Wodtke and Janna Bastow.
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。
Writes (or rewrites) a spec, PRD, PR-FAQ or shaped pitch and grades it against what the best product teams actually do. Use when you ask 'write a PRD', 'how do I spec this', 'PR/FAQ', 'working backwards doc', 'shape this project', 'do we even need a PRD', or 'engineering says the spec isn't detailed enough'. Draws on 56 insights from 38 Lenny's Podcast guests incl. Ryan Singer, Bill Carr and Lane Shackleton.
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。
Diagnoses why your product team ships slowly and produces a velocity plan with a baseline, a bottleneck map, the top 3 interventions and a stop-doing list. Use it when you say 'we're too slow', 'how do we ship faster', 'projects keep slipping', 'too many meetings', 'approvals take forever', 'our releases are scary', 'velocity', 'cycle time', 'we hired more people and nothing got faster' or 'AI made coding fast but we still ship slowly'. Draws on 226 Lenny's Podcast guests including Farhan Thawar, Nicole Forsgren and Melissa Perri.
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: finding federal opportunities; capture and bid screening; competitor or incumbent intelligence; recompete pipelines; teaming partner research; agency, customer, or market intelligence; federal labor-rate or pricing context; or refreshing prior GovCon research. Always begin with the complete workflow menu and obtain the user's selection before research, preflight, file generation, web search, or MCP calls. Produce sourced chat findings or an optional validated GovCon Growth Brief without making a bid decision from public data alone.
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。
Manages Linear issues, teams, and projects via CLI. Lists issues, creates tasks, views details, links issues, and runs GraphQL queries. Must use for "my Linear issues", "create Linear task", "link issues in Linear", "Linear API query", or any Linear project management request.
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 rubric for promoting a prose prompt into a typed DSPy Signature. This is an ENHANCE overlay on top of the [[dspy]] library skill: it does NOT teach DSPy syntax — it answers the coder-agent decision "when do I stop hand-writing a prompt string and declare it as a `dspy.Signature`, and how do I name/describe its fields so the optimizer and the calling code both get a clean contract." Activate when: a prompt string grows past ~50 lines; the LM output is consumed by code (parsed, branched on, stored) rather than read by a human; the same prompt is reused across >1 call site; or a teammate asks "should this be a Signature?". Do NOT activate for one-shot throwaway prompts, or for HOW-TO questions about DSPy modules /optimizers/compile — defer those to the [[dspy]] skill and the [[agentsop-dspy]] workflow skill. Search keywords: typed prompt, structured prompt, DSPy Signature, prompt as a function, prompt contract, when to formalize a prompt.
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。
Cross-framework enhancement overlay for choosing a multi-agent topology BEFORE writing any agent. A binary-question rubric — is single-agent + tools enough? do agents need to know about each other? does the output need one voice? — maps the answer to single-agent / supervisor / swarm / sequential / hierarchical. Activates when a coder agent is tempted to "split the work into roles" or reaches for a multi-agent framework. Encodes the *selection rubric* that the per-framework skills assume but never surface. Search keywords: when to use multi-agent, single vs multi agent, do I need multiple agents, supervisor vs swarm, multi-agent vs single agent, agent team design.
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。
SOP for building multi-agent systems with CrewAI — role-based collaboration, sequential/hierarchical processes, Flows, memory, delegation. Use when modeling agent teams with clear roles and task pipelines.
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。
Universal discipline for any LM-driven loop — agent retries, plan-act-observe, multi-agent handoffs, optimiser passes, test-fix cycles. Encodes the one rule every framework documents quietly and every team relearns expensively: the LM in the loop is NEVER a reliable terminator. Termination must be provided by an explicit counter + exit predicate + stagnation signal + escalation path that live OUTSIDE the LM's control. This is a tool- level, framework-agnostic skill. It maps onto LangGraph (recursion_limit + state counter + interrupt), CrewAI (max_iter + max_rpm + human_input), Claude / OpenAI SDKs (max_iterations + tool_use_budget), DSPy (declared evaluation budget), Aider (REPL + explicit retry cap), and AutoGen (max_consecutive_auto_reply). Search keywords: infinite loop, recursion limit, recursion_limit, GraphRecursionError, max iterations, max_iter, agent stuck, agent won't stop, runaway agent, ReAct loop not terminating, agent repeating itself.
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。
Protect Claude Code sessions from context overflow by running a background daemon that monitors session size and auto-prunes before compaction hits. Use when the user says "guard", "protect session", "context getting long", "prevent compaction", "session management", or is running agent teams that need continuous context protection.
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。
Screen a Medicare/Medicaid claims corpus for fraud, waste, and abuse and produce ranked, fully-cited investigation referrals for an SIU / program-integrity team. Use when asked to run a fraud sweep, screen claims for FWA, find billing anomalies, or generate investigation referrals over a claims dataset.
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 applications for AI prompt injection, agent security, and LLM permission boundary vulnerabilities. Use when the user mentions 'prompt injection,' 'LLM security,' 'AI security,' 'jailbreak,' 'indirect prompt injection,' 'prompt leaking,' 'AI red team,' 'LLM vulnerabilities,' 'AI input validation,' 'system prompt extraction,' 'agent security,' 'MCP security,' 'AI permissions,' 'AI privilege escalation,' or needs to secure any application with AI features, AI agents, or LLM integrations.
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。
Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the security slice). Use when the user mentions 'AI risk,' 'AI governance,' 'NIST AI RMF,' 'AI compliance,' 'ML governance,' 'model risk management,' 'AI fairness,' 'AI bias,' 'algorithmic accountability,' 'AI Bill of Rights,' 'EU AI Act,' 'AI transparency,' 'model card,' 'AI red team,' 'AI safety,' 'responsible AI,' 'model drift,' 'concept drift,' 'AI monitoring,' 'AI incident,' or needs to assess or govern an AI / ML system.
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, run, and improve a Security Operations Center — alert prioritization, runbook authoring, escalation criteria, on-call structure, alert tuning workflow, MTTD / MTTR / fidelity KPIs, analyst tiering, and shift handoffs. Use when the user mentions 'SOC,' 'security operations,' 'SOC analyst,' 'alert triage workflow,' 'runbook,' 'escalation,' 'on-call,' 'SOC tiering,' 'tier 1 / tier 2,' 'MTTD,' 'MTTR,' 'alert fatigue,' 'alert tuning,' 'shift handoff,' 'SOAR,' or wants to design or improve a security operations team.
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。
Strategic product leadership toolkit for Head of Product including OKR cascade generation, market analysis, vision setting, and team scaling. Use for strategic planning, goal alignment, competitive analysis, and organizational design.
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 Storj Gerrit team code collaboration service, which we just called it gerrit, when the user ask to do it
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。
Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.
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。
Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.
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。