Skills de Claude Code · página 71

Build a complete, standards-aligned lesson plan with clear objectives, a timed activity sequence, differentiation, and assessment. Use when asked to write a lesson plan, plan a class or lesson, design a teaching session, or structure instruction for a topic. Produces a ready-to-teach plan with measurable objectives, a minute-by-minute flow, materials, checks for understanding, and differentiation for varied learners.

Turn a skill's recommendations into real, executed actions — open the tickets, file the issues, post the updates — safely: dry-run preview, risk-classified, approval-gated, then recorded back to the brain. Use when asked to act on a plan, file tickets from a checklist, create issues from a PRD, execute the recommended next steps, or wire a skill's output into GitHub/Linear/Slack. Produces a dry-run actions plan with per-action risk, executes only after approval via the connected action MCP, and logs what was done. Nothing acts silently.

Maintain a durable, local markdown memory ('brain') of your product context, decisions, hypotheses, and stakeholders that other skills read from and write back to. Use when asked to set up a brain, ingest notes/artifacts into memory, recall what's known about a topic, log a decision with provenance, or run a weekly brain review. Produces a structured brain/ folder (knowledge, decisions, hypotheses, stakeholders, entities, source) with provenance-tagged facts, plus ingest/recall/record/review operations with approval-gated, append-only write-back.

Specify an autonomous or tool-using AI agent before building it. Use when asked to design an AI agent, define an agent's tools and guardrails, scope what an agent is allowed to do, or write an agent spec/PRD. Produces an agent spec — goal & scope, tools with permissions, the control loop, guardrails & approval gates, memory, escalation/handoff, evaluation, and failure handling.

Design an evaluation plan for an LLM or AI feature before shipping it. Use when asked how to evaluate a prompt/model/agent, set up an eval harness, define quality metrics for an AI feature, or build a regression gate. Produces an eval plan — task definition, datasets, metrics & rubrics, baselines, automated + human evals, a pass bar, and a regression gate.

Write a PRD for an AI-powered feature, covering the things normal PRDs miss. Use when asked to spec an AI/LLM feature, write a PRD for a feature that uses a model, or plan an AI capability (assistant, summarizer, generator, classifier). Produces an AI feature PRD — problem & UX of uncertainty, model approach, eval criteria, guardrails, fallback behaviour, the data flywheel, and cost/latency budget.

Document a dataset so others know what it is, how it was made, and when not to use it. Use when asked to write a datasheet for a dataset, document training/eval data, or assess whether a dataset is fit for a use. Produces a datasheet — motivation, composition, collection process, preprocessing, recommended uses & limits, distribution, and maintenance.

Model the cost and latency of an LLM feature before it ships and surprises the bill. Use when asked to estimate LLM API costs, set a latency/token budget, decide which model tier to use, or bring down the cost of an AI feature. Produces a cost & latency budget — token math per request, monthly cost projection, model tiering, caching/streaming levers, p95 latency targets, and a guardrail/alert plan.

Document a deployed ML/AI model so others can use it responsibly. Use when asked to write a model card, document a model's intended use and limitations, or prepare an AI model for review/launch. Produces a complete model card — intended use, training data, evaluation metrics across slices, limitations, ethical considerations, and a deployment checklist.

Design a Retrieval-Augmented Generation system end to end. Use when asked to design a RAG pipeline, a 'chat with your docs' feature, a knowledge assistant, or to debug why a RAG system gives wrong/ungrounded answers. Produces a RAG design doc — ingestion & chunking, embeddings & index, retrieval & reranking, the generation prompt, grounding/citations, evaluation, and failure modes with mitigations.

Write a board pre-read that's sent before the meeting so the meeting is about decisions, not status. Use when asked to prepare a board pre-read, a board update/package, or pre-meeting materials for a board. Produces a board pre-read — a TL;DR, the metrics dashboard vs. plan, what's working / what's not, the decisions and asks for the board, and risks — designed to be read in advance.

Allocate a finite budget or headcount across competing initiatives by return and strategic fit. Use when asked to allocate budget, decide where to invest, build a funding/portfolio plan, or make trade-offs across initiatives under a cap. Produces a capital-allocation plan — initiatives scored by expected return × strategic fit per dollar, a funded/unfunded split against the cap, the cut line, and the reasoning.

Write a crisp decision memo that drives a clear decision, not a discussion. Use when asked to write a decision memo, a recommendation memo, a one/six-pager for a decision, or to get leadership to decide something. Produces a decision memo — the decision & recommendation up front, the context, options with trade-offs, what you'd need to believe, risks, and the explicit ask with a deadline.

Write a strategy memo that commits to a bet and says what you won't do. Use when asked to write a strategy memo, articulate a strategy, make the case for a strategic direction, or align the team on where to focus. Produces a strategy memo — the strategic question, the diagnosis, the bet/approach, why now, explicit non-goals (what we're NOT doing), how we'll know it's working, and the risks.

Keep a running brag document of your accomplishments so reviews and promo cases write themselves. Use when asked to start or update a brag doc, log a win, track accomplishments, or prep evidence for a review/promotion. Produces a structured, dated accomplishment log — impact-first entries with metrics, scope, and the evidence link — grouped so it drops straight into a self-review or promo packet.

Map where you are against the next level and build a concrete plan to close the gap. Use when asked to map a career ladder, find the gap to the next level, build a development/growth plan, or figure out what to work on to get promoted. Produces a level-gap map — current vs. target competencies side by side, the specific gaps, and a prioritised 1–2 quarter plan of evidence-generating projects to close them.

Prepare for a 1:1 so it drives outcomes instead of becoming a status update. Use when asked to prep for a one-on-one, build a 1:1 agenda, prepare to talk to your manager (or a report), or raise something hard in a 1:1. Produces a focused 1:1 agenda — your top topics with the outcome you want for each, the asks, updates kept brief, and growth/feedback threads, tuned to direction (with your manager vs. with a report).

Build a promotion case that proves you're already operating at the next level. Use when asked to write a promo packet/case, prepare for a promotion committee, or make the case for a level-up or title change. Produces a promotion packet — the level-up thesis, evidence mapped to each next-level competency, scope/impact highlights, peer-quote slots, and the gaps to close before submitting.

Plan a compensation negotiation grounded in numbers and leverage, not nerves. Use when asked to negotiate salary, evaluate or counter a job offer, prepare for a comp conversation, or compare offers. Produces a negotiation plan — total-comp comparison across offers, your target/walk-away and BATNA, the value-based justification, the counter scripts, and what to negotiate beyond base.

Write a performance self-review that's specific, evidenced, and balanced. Use when asked to write a self-review, self-assessment, or self-evaluation for a performance cycle. Produces a complete self-review — accomplishments mapped to impact and competencies, growth areas owned honestly, and a forward-looking development plan, in the voice of the person being reviewed.

Build a data retention and deletion schedule grounded in legal basis. Use when asked to create a data retention policy, set retention periods, plan data deletion/minimisation, or answer 'how long can we keep this data?'. Produces a retention schedule — data categories with their retention period, legal/business basis, deletion trigger and method, plus flags for data kept with no basis or no defined period.

Assess GDPR compliance and build the core records (ROPA, lawful basis, DSAR, DPIA triggers). Use when asked to get GDPR-compliant, build a Record of Processing Activities, decide a lawful basis, handle data-subject requests, or check whether a DPIA is needed. Produces a GDPR assessment — a ROPA, lawful-basis mapping per activity, DSAR workflow, DPIA-trigger screen, and a prioritised gap list.

Map HIPAA Security Rule safeguards and run a risk analysis for systems handling PHI. Use when asked to become HIPAA-compliant, assess HIPAA safeguards, prepare for handling PHI/ePHI, or scope a BAA. Produces a HIPAA assessment — the administrative/physical/technical safeguards with required-vs-addressable status, a risk analysis, BAA scope, and a prioritised remediation plan.

Scope an ISO 27001 ISMS and build the Statement of Applicability across Annex A controls. Use when asked to implement ISO 27001, scope an ISMS, build a Statement of Applicability (SoA), or prepare for ISO 27001 certification. Produces an ISMS plan — scope & context, risk-treatment approach, an Annex A control applicability table (the SoA), and a prioritised implementation roadmap.

Assess SOC 2 readiness across the Trust Services Criteria and produce a gap remediation plan. Use when asked to prepare for a SOC 2 audit, run a SOC 2 readiness/gap assessment, scope controls, or get audit-ready. Produces a readiness report — scope & criteria, a control-by-control status, a weighted readiness score, prioritised gaps with owners, and the evidence each control needs.

Run a third-party / vendor security review and assign a risk tier with required controls. Use when asked to assess a vendor's security, run a third-party risk assessment, complete a security questionnaire about a vendor, or decide what due diligence a new tool needs. Produces a vendor risk assessment — a data/access-driven risk tier, the questionnaire focus, required evidence (SOC 2, pen test, DPA), residual risk, and an approve/conditional/reject recommendation.

Route a fuzzy request to the right skill in this library. Use when the user is unsure which skill fits, asks 'which skill should I use for X', describes a task without naming a skill, or when a request could plausibly match several skills. Produces a best-fit recommendation with the inputs to gather, a runner-up with the tie-breaker, and a workflow recipe when the job spans multiple skills.

Turn a task you repeat every week into a reusable personal skill or prompt — so you say 'do this' instead of re-explaining it every time. Use when asked help me make a skill for, turn this repetitive task into a template, I do this every week, or create a reusable prompt for this. Produces a captured spec of the repetitive task (its inputs, steps, and what good output looks like), a reusable skill/prompt you can invoke by name, and guidance on saving and refining it — lowering the barrier from AI user to AI author, one weekly task at a time.

Route a fuzzy request to the right skill in this library. Use when the user is unsure which skill fits, asks 'which skill should I use for X', describes a task without naming a skill, or when a request could plausibly match several skills. Produces a best-fit recommendation with the inputs to gather, a runner-up with the tie-breaker, and a workflow recipe when the job spans multiple skills.

Turn a task you repeat every week into a reusable personal skill or prompt — so you say 'do this' instead of re-explaining it every time. Use when asked help me make a skill for, turn this repetitive task into a template, I do this every week, or create a reusable prompt for this. Produces a captured spec of the repetitive task (its inputs, steps, and what good output looks like), a reusable skill/prompt you can invoke by name, and guidance on saving and refining it — lowering the barrier from AI user to AI author, one weekly task at a time.

Design a 360-degree feedback survey or write a structured 360 feedback report. Use when asked to build a 360 feedback process, write 360 feedback for a colleague, design a feedback survey, or produce a feedback report. Produces either a complete survey instrument with rating scales and open-ended questions, or a structured narrative feedback report with themes, strengths, and development areas.

Decode a 401k or workplace retirement plan — the real cost of its funds, the match's fine print, vesting math, and the plan features worth using or avoiding. Use when someone asks 'is my 401k any good', 'decode my 401k plan', 'which funds should I look at', or 'what fees am I paying'. Produces a fee decode in dollars-over-time, match and vesting math, a fund-lineup triage by cost, and the questions for HR or the plan administrator.

Plan a trip that actually works with a disability or access need — confirm real accessibility (not just 'accessible' labels), book the assistance in advance, plan for equipment and medication, and build in the contingencies for when access breaks down. Use when someone says 'plan an accessible trip', 'travelling with a wheelchair/disability', 'book assistance for my flight', or 'will this hotel actually work for me'. Produces an access-verified itinerary, an assistance-booking checklist, an equipment/medication plan, and contingency scripts. Verify specifics with providers.

Request a reasonable accommodation at work or in education — frame it around the barrier and the adjustment (not your diagnosis), cite the right process, and navigate the back-and-forth constructively. Use when someone says 'I need a workplace accommodation', 'request reasonable adjustments', 'ADA/Equality Act accommodation', or 'how do I ask for accommodations for my disability/condition'. Produces the request letter, a barriers-and-adjustments map, disclosure guidance, and a plan for the interactive process. Not legal advice — routes to the formal process and to advocacy where needed.

Build a structured account plan for any key customer or target account. Use when asked to create an account plan, key account strategy, strategic account review, or territory plan. Produces a complete account plan with relationship map, growth opportunities, risks, and 90-day action plan.

Get back into a locked or hacked account the right way — the official recovery routes, what proof you'll need, and how to re-secure it so it doesn't happen again. Use when asked I'm locked out of my account, my account got hacked, help me recover my [email/social/bank] account, or I lost access to 2FA. Produces the official recovery path for the account type, the identity proof to prepare, a re-securing checklist for after you're back in, and warnings about fake 'recovery' services and support scams.

Simulate the acquirer's diligence team hunting for reasons to cut your price — their internal red-flags memo with a price-chip estimate per finding. Use when asked to red-team my company before a sale, how will an acquirer attack our valuation, pre-diligence audit, or what will DD find. Produces the acquirer's internal memo (revenue quality, key-person, tech debt, concentration, legal) and a debrief on which flags are fixable before a process.

Write platform-native paid ad copy with multiple angles to test. Use when asked to write ad copy, Google/Facebook/LinkedIn/Instagram ads, PPC headlines, or paid social creative copy. Produces ready-to-ship variants per platform (headlines, primary text, descriptions, CTAs) across distinct angles, sized to each platform's limits, with a note on what each variant tests.

Optimize an article for Answer Engine Optimization (AEO) so AI engines like ChatGPT, Perplexity, and Claude can extract, quote, and cite it. Use when asked to AEO-optimize, make content AI-readable, improve AI citation chances, or adapt an article for answer engines. Produces an AEO-optimised rewrite with question headings, 50–80 word answer capsules, a paragraph-length audit, and flagged trust signals.

Work through the first hours and days after a disaster — a fire, flood, storm, or evacuation — in the right order: safety and people first, then documenting for insurance and aid, then the immediate recovery steps, without missing the things that cost money or health later. Use when someone says 'my house flooded/burned', 'what do I do after the disaster', 'we just evacuated, now what', or 'the storm damaged everything'. Produces a triaged action plan (safety → document → claim → recover), the do-not-miss list, and where to get help. Not legal advice; routes to emergency services and official aid.

Enforce the simplest meeting rule that works — no agenda, no meeting — with the three-line agenda format (purpose, decisions sought, pre-reads), the 24-hour rule, and the graceful cancel scripts. Use when asked write an agenda for this meeting, should this meeting happen, our meetings have no agendas, or cancel this meeting politely. Produces the three-line agenda, the happen-or-cancel verdict, the cancel/convert scripts, and the team norm rollout.

Review an LLM agent design and find where it will be unreliable, expensive, or unsafe. Use when asked to review an agent architecture, critique a multi-step/tool-using agent, debug an agent that loops or goes off-task, or harden an agent before launch. Produces a structured review — task fit, control flow, tools, memory/context, failure handling, cost, and safety — with prioritised findings and fixes.

Redesign seat-based pricing for the agent era — when one human runs ten agents, per-seat models collapse. Use when agents are eroding seat counts, when asked to migrate to usage- or outcome-based pricing, to price an agent/API tier, or to defend revenue as customers automate their own usage. Produces a pricing migration plan: the new value metric, fences, agent-tier design, cannibalisation math, and a phased migration for existing customers. For general pricing and packaging strategy use pricing-strategy.

Hire an AI agent the way you'd hire an employee — a role spec with success criteria, a structured work-sample interview run on your real tasks, reference checks (what do actual users report), probation KPIs, and termination criteria written before day one. Use when choosing between AI agents/tools/copilots for a job, formalizing an AI pilot, or 'which agent should we use for X'. Produces the role spec, interview pack with scoring rubric, a decision record, and a probation plan.

Run a blameless postmortem for an incident caused by an AI agent or LLM feature — hallucinated facts shipped to users, runaway tool use, prompt injection, cost blowouts, or wrong actions taken autonomously. Use when asked to write up an AI incident, analyse why an agent did something wrong, or produce corrective actions after an LLM failure. Produces a structured postmortem with trace reconstruction, a root-cause layer analysis, and corrective actions including a permanent regression case. For non-AI production incidents use incident-postmortem.

Specify the tracing, metrics, and alerting for an AI agent or LLM feature in production. Use when asked what to log for an LLM app, design agent tracing or spans, define quality and cost monitors, or answer 'how do we know if the agent is misbehaving?'. Produces an observability spec with a trace schema, metric definitions with owners and alert thresholds, sampling and retention policy, and a privacy note for logged content.

Audit whether AI agents can actually use your product — docs, APIs, onboarding, errors, and discoverability, evaluated from a non-human user's perspective. Use when asked if a product is agent-ready, to audit a site or API for AI usability, to prepare for agentic traffic, or when agents keep failing against your product. Produces a scored readiness report with per-surface findings and a prioritised fix list. For optimising a single article for AI citation use aeo-optimizer; for designing the MCP server itself use mcp-server-spec.

Offboard an AI agent the way you'd offboard an employee — inventory what it knew and touched, export then purge its memory, revoke every credential and access grant, and write the handover for its successor (human or agent). Use when decommissioning an agent or bot, switching agent vendors, ending an AI pilot, or when someone asks 'what did this thing have access to?'. Produces a severance checklist, an access-revocation table, a memory disposition record, and a successor handover.

Assess whether and how someone can safely stay in their own home as they age — the home hazards, the support gaps, and the modifications and services that make it work. Use when asked can my parent stay in their home safely, aging in place assessment, is it safe for them to live alone, or what do we need for them to stay home. Produces a room-by-room safety read (fall hazards, accessibility), an honest look at the daily-living and support gaps, the modifications and services that could close them, warning signs that home may no longer be safe, and how to raise it respectfully — helping a family make a clear-eyed, dignity-preserving decision. Not medical advice.

Prepare the conversations with aging parents that everyone postpones — the driving talk, the money talk, the care-options talk, the moving talk — each with an opener that doesn't ambush, a dignity-first script, rehearsal against realistic resistance, and the fallback when it goes badly. Use when someone says 'I need to talk to my dad about driving', 'my mum won't discuss her finances', 'we need to talk about care', or is dreading a visit for exactly this reason. Produces the conversation plan, a rehearsal, and the small-steps fallback. A preparation tool, not family therapy — and it says so when the situation needs more.

Run a club, PTA, or association AGM that finishes on time and holds up later — the notice and agenda done right, a quorum plan, minutes that capture decisions not conversations, elections without awkwardness, and the follow-up that makes decisions real. Use when a volunteer says 'I have to run the AGM', 'what goes in the agenda', 'nobody comes to our meetings', or 'our elections are a mess'. Produces the notice, agenda, chair's script, minutes template, and quorum rescue plan.

Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where the agent can fail (bad input, hallucination, wrong tool call, edge cases, silent errors), the checks that catch each (validation, evals on real cases, human-in-the-loop gates, monitoring), a right-sized reliability plan scaled to the stakes, and a rollout that earns trust incrementally — so an agent that works in a demo becomes one that works in reality. For builders putting AI agents into real workflows.

Evaluate performance fairly when output is AI-assisted — what still measures the human, what now measures the tooling, and how to run the review conversation. Use when reviewing someone whose work is heavily AI-assisted, when output volume stopped meaning anything, when calibrating a team with uneven AI adoption, or when writing review criteria for the AI era. Produces review guidance: a what-measures-whom analysis, rewritten criteria, calibration rules for mixed-adoption teams, and conversation scripts. For the general review document use performance-review; for redesigning the role itself use role-redesign-for-ai.

Review AI-authored code for its characteristic failure modes — plausible-but-wrong logic, hallucinated APIs, over-engineering, dead scaffolding, and silent security shortcuts. Use when reviewing an AI-generated or heavily AI-assisted PR, when AI-written code keeps shipping subtle bugs, or when setting review standards for a team using coding agents. Produces a focused review with AI-specific findings, verification steps per risk class, and a team checklist for AI-authored changes. For general PR review use code-review-checklist — this skill covers what that one assumes a human wouldn't do.

Audit a content library, docs site, or blog for AI-generated filler that's eroding trust and search performance — and triage what to fix, rewrite, or delete. Use when asked to find slop in a content library, audit AI-written content quality, explain why content engagement or rankings dropped after scaling with AI, or set a quality bar for AI-assisted publishing. Produces an audited inventory with per-piece verdicts, the detection signals used, a triage plan, and a publishing quality gate that prevents recurrence. For a single article's AI-citability use aeo-optimizer; for the strategy itself use content-calendar or seo-content-brief.

Build the context an AI needs to do a task well — the background, constraints, examples, and format it can't guess — so you get a great result on the first try instead of a generic one you have to keep correcting. Use when asked why does AI give me generic answers, how do I give AI better context, my AI results are mediocre, or how do I get it right the first time. Produces the specific context this task needs (who/what/constraints/examples/format), a reusable primer you can paste ahead of the request, the difference between a starved prompt and a well-briefed one, and what to leave out — turning vague back-and-forth into a strong first result.

Decide when and how your product and communications must (or should) label AI-generated content, and write the disclosure policy — surface-by-surface rules, exact label wording, and the review trigger for regulations like the EU AI Act's transparency obligations. Use when asked 'do we have to label AI content', 'write our AI disclosure policy', 'are we covered for the AI Act', or when marketing/support/product start shipping AI-generated output. Produces a disclosure policy with a per-surface matrix and ready-to-use label copy. Not legal advice.

Check AI output before you trust or use it — where it's likely wrong, what to verify, and how to catch confident-sounding errors. Use when asked can I trust this AI answer, how do I verify what AI told me, fact-check this AI output, or is this AI response reliable. Produces a risk read on the specific output (the claims most likely to be wrong or made up), the parts that need independent verification vs the parts that are low-risk, how to actually verify each, the tells of AI hallucination and overconfidence, and a habit for building verification into your AI use — because AI is confidently wrong often enough that unchecked trust is a real risk.

Audit whether the organisation's AI spend actually paid — measured against baselines, not vendor math or vibes. Use when a CFO asks what the AI tools returned, when renewing AI contracts, when consolidating overlapping AI subscriptions, or to build the measurement plan before the next spend. Produces an ROI audit with per-tool verdicts (keep/consolidate/cut), the honest-measurement method behind each number, and a baseline plan for whatever can't be scored yet. To forecast ROI before an investment use roi-estimator; this skill measures what already happened.

Figure out which AI tool actually fits the task in front of you — chatbot, coding assistant, image model, agent, or none — instead of forcing one tool onto everything. Use when asked which AI tool should I use for, what's the best AI for, do I even need AI for this, or should I use ChatGPT or something else. Produces a match between your task and the right kind of AI tool (with why), the trade-offs that matter for your case, when the answer is a non-AI tool or plain human effort, and how to try it cheaply before committing — so you pick by fit, not by hype or habit.

Write an AI usage policy people can actually follow — approved tools, data rules, disclosure duties, and review obligations, in one page instead of legal fog. Use when asked for a company AI policy, acceptable-use rules for ChatGPT/Claude/Copilot at work, guidance on what data may go into AI tools, or to fix a policy nobody reads. Produces a one-page usable policy plus the decision log behind it. Not a substitute for legal advice; pairs with compliance-checklist for regulatory mapping and ai-ethics-review for system-level assessments.

Design an AI-assisted workflow for a recurring task — which steps to hand to AI, which to keep human, and how they connect — so you get leverage without losing quality or control. Use when asked how do I use AI for [process], automate this with AI, design an AI workflow, or where does AI fit in my process. Produces a map of the task's steps split into AI-does / human-does / human-checks, the right tool/prompt for each AI step, the hand-offs and review points, the failure modes to guard against, and a start-small rollout — turning a manual process into a reliable AI-assisted one that keeps you in control.

Check live air quality anywhere with zero API keys — Open-Meteo's air-quality API via curl, decoded from raw PM2.5 and AQI numbers into what they mean for going outside. Use when asked what's the air quality, is it safe to run outside, AQI in my city, or pollution levels right now. Produces the current AQI and pollutant levels, the plain-language health read with the standard bands, and the rerunnable command.

Build an all-hands that lands with everyone from intern to VP — the mixed-altitude structure (the story for all, the numbers for some), the wins-with-names section done right, the hard-news slide handled straight, and the Q&A design that gets real questions. Use when asked build the all-hands deck, make the monthly town hall not boring, how do we share the numbers with everyone, or announce this change at all-hands. Produces the segment structure, the altitude-mixed content rules, the hard-news handling, and the Q&A mechanics.

TAKT ワークフローエンジン。Agent Team を使ったマルチエージェントオーケストレーション。ワークフロー YAML（steps / initial_step）に従ってマルチエージェントを実行する。

SpecStory Lore - mine your SpecStory coding histories (any agent - Claude Code, Codex, Cursor, Gemini, and more) into a persistent corpus, surface your reproducible workflows with corroborated evidence, and interactively forge the chosen ones into skills installed across all your agent harnesses. Use when the user wants to turn past AI coding sessions into reusable skills, asks "what could I make into a skill", "mine my lore", "forge skills from my history", or points at a .specstory/history directory.

SpecStory Workthreads - a weekly work-thread rollup across a team's repos from SpecStory coding histories (any agent - Claude Code, Codex, Cursor, Gemini, and more). It groups the window's sessions into threads of work per project and labels each new / open / recently closed, so a lead sees what shipped, what is still an open loop, and what was just started. Use when someone asks "what happened this week", "what is still open", "what did the team finish", "give me the weekly rollup", or wants a status report over a .specstory/history corpus.

Help users create and run AI evaluations. Use when someone is building evals for LLM products, measuring model quality, creating test cases, designing rubrics, or trying to systematically measure AI output quality.

Help users define AI product strategy. Use when someone is building an AI product, deciding where to apply AI in their product, planning an AI roadmap, evaluating build vs buy for AI capabilities, or figuring out how to integrate AI into existing products.

Help users synthesize and act on customer feedback. Use when someone is analyzing NPS responses, processing support tickets, reviewing user research, synthesizing feedback from multiple channels, or trying to identify patterns in customer input.

Help users apply behavioral science to product design. Use when someone is designing for habit formation, reducing friction, applying psychology to UX, increasing retention through behavioral principles, or using nudges to influence user behavior.

Help users craft compelling brand narratives. Use when someone is defining brand strategy, writing company positioning, creating pitch narratives, developing messaging frameworks, or trying to make their company story more memorable.

Help users get promoted at work. Use when someone is preparing for a promotion conversation, building their case for advancement, trying to understand what's blocking their promotion, or figuring out how to get to the next level in their career.

Help users build and scale their sales organization. Use when someone is hiring their first salespeople, deciding when to bring on sales leadership, structuring sales compensation, or transitioning from founder-led sales.

Help users build and maintain strong team culture. Use when someone is defining team values, creating psychological safety, onboarding to a new team, navigating cultural change, or building distributed team norms.

Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve AI output quality.

Help users navigate career changes and pivots. Use when someone is considering a new role, transitioning into product management, evaluating job offers, taking a sabbatical, or feeling stuck in their current position.

Help users develop and coach product managers. Use when someone is managing PMs, creating development plans, running performance reviews, or trying to level up their PM team's capabilities.

Help users build and grow product communities. Use when someone is starting a community, scaling an ambassador program, driving community-led growth, or choosing between user, developer, or partner communities.

Help users understand and respond to competition. Use when someone is positioning against competitors, evaluating market threats, running competitive war games, or deciding how much to focus on competitors versus customers.

Help users conduct effective hiring interviews. Use when someone is designing an interview loop, crafting interview questions, evaluating candidates in real-time, or building a structured interview process.

Help users run better customer and user interviews. Use when someone is preparing for user research, planning discovery interviews, writing interview questions, analyzing interview findings, or trying to understand customer needs.

Help users build content marketing strategies. Use when someone is starting a blog, building SEO, creating thought leadership content, or deciding on content formats and distribution channels.

Help users work effectively across functions. Use when someone is navigating PM-engineering relationships, resolving cross-team conflicts, building product trios, or improving handoffs between design, engineering, and product.

Help users create compelling product visions. Use when someone is writing a vision statement, defining a long-term product direction, aligning teams on the future state, or distinguishing vision from strategy.

Help users delegate effectively. Use when someone is struggling to let go of tasks, deciding what to delegate, building team autonomy, or balancing being hands-on vs hands-off.

Help users understand and build design engineering capabilities. Use when someone is creating a design engineering function, hiring design engineers, or bridging the gap between design and engineering teams.

Help users build and scale design systems. Use when someone is creating a component library, establishing design tokens, scaling brand consistency, or deciding when to invest in a design system.

Help users design and optimize growth loops. Use when someone is building viral mechanics, designing referral programs, creating product-led acquisition, or figuring out how to make their product grow itself.

Help users design effective surveys. Use when someone is creating customer surveys, NPS measurements, product-market fit surveys, or feedback collection mechanisms.

Help users implement effective dogfooding practices. Use when someone is trying to get their team to use their own product, designing internal usage programs, or building user empathy through personal product use.

Help users manage their energy for sustained performance. Use when someone is feeling burned out, trying to find their zone of genius, scheduling for productivity, or identifying what drains vs energizes them.

Help users build strong engineering culture. Use when someone is improving developer experience, fostering technical excellence, designing engineering practices, or scaling an engineering organization.

Help users navigate enterprise sales. Use when someone is closing large deals, managing complex buying committees, handling procurement, or converting PLG users to enterprise contracts.

Help users make better hiring decisions. Use when someone is evaluating job candidates, making hiring decisions, conducting reference checks, reviewing work samples or take-homes, calibrating their hiring bar, or deciding between finalists.

Help users evaluate emerging technologies. Use when someone is assessing new tools, making build vs buy decisions, evaluating AI vendors, or deciding on technical architecture.

Help users make better decisions between competing options. Use when someone is weighing pros and cons, comparing alternatives, struggling with a difficult choice, deciding between speed and quality, or asking "should we do X or Y?

Help users build relationships with mentors and sponsors for career growth. Use when someone is looking for career guidance, wants to find a mentor, needs an advocate at work, is trying to build their professional network, or asking how to get advice from senior leaders.

Help founders close their first customers and build repeatable sales processes. Use when someone is doing founder-led sales, trying to get their first customers, writing cold outreach, running early sales calls, or asking when to hire their first salesperson.

Help founders raise capital and build investor relationships. Use when someone is preparing a pitch deck, deciding whether to raise venture capital, meeting with investors, or asking about fundraising strategy.