pm-claude-skills

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.

Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.

Analyse a finished A/B test and write the readout — the result, whether it's statistically and practically significant, what it means, and the ship/no-ship call. Use when asked to analyse experiment results, write an A/B test readout, interpret test data, or decide whether to ship a variant. Produces a clear verdict with the lift and confidence, segment cuts, the risks (peeking, novelty, sample), and a recommendation. Distinct from planning a test — this reads results.

Generate a WCAG 2.2 accessibility audit checklist and remediation suggestions for any UI or design. Use when asked to audit for accessibility, check WCAG compliance, review a design for a11y issues, or create an accessibility remediation plan. Produces a prioritised checklist with pass/fail assessments and specific fixes.

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.

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.

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.

Workflow recipe — the org-side AI adoption arc, policy to proof, by chaining 4 skills.

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.

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.