테스트·보안 스킬

Plan tests for an API endpoint or service — functional, negative, and contract. Use when asked to test an API, write API test cases, plan REST/GraphQL endpoint testing, or validate an API contract. Produces an API test plan — per-endpoint cases (status codes, schema, auth, validation, errors), boundary/negative cases, contract checks, and non-functional notes — so the API is verified beyond the happy 200.

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Audit a week the way Arnold Bennett's 'How to Live on 24 Hours a Day' (1908) prescribes — time as the one income that cannot be increased, the day-within-the-day, and starting with 90 minutes, not a life overhaul. Use when someone says 'I have no time', 'work eats everything', 'I want to learn X but can't fit it', or asks for a time audit or evening routine. Produces a time ledger, one reclaimed 'inner day' block, and Bennett's own warnings about overreach.

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Audit the beneficiary designations that quietly override wills — the account-by-account sweep, the life-event triggers that make them stale, and the coordination check against actual intentions. Use when asked check my beneficiaries, does my 401k go to my ex, do beneficiary forms beat a will, or what should I update after marriage/divorce/a birth. Produces the account sweep list, the stale-designation red flags, the intent-vs-paperwork comparison table, and the update checklist with the verify-in-writing step.

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Convert a meeting into async work that actually decides — the doc-plus-deadline format that replaces the room, the comment-window rules, the decision-closure step that async usually fumbles, and the honest test for what still needs synchronous. Use when asked can this meeting be async, replace our status meeting with a doc, run this decision without a call, or async isn't working for us. Produces the conversion design, the async artifact format, the closure protocol, and the still-needs-a-room list.

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Run the worst-case drill before an agent goes autonomous — the 'if this agent were fully hijacked right now, what's the damage' walk-through, the containment controls (caps, kill-switch, reversibility, isolation), and the recovery plan. Use when asked what's the worst my agent could do, run a blast-radius assessment, prepare for an agent going rogue, or am I ready to let this run unattended. Produces the worst-case walk-through, the containment controls, the reversibility audit, and the incident-recovery runbook.

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Write a specification section that a contractor can price accurately and a site can build from — performance stated, products defined, workmanship and tolerances given, and the coordination with the drawings made explicit. Use when asked to write a specification, spec a material or system, prepare an NBS-style section, or when a specification produced wildly different tender prices. Produces the scope and interface definition, the performance requirements, the product or system definition, execution and tolerance requirements, and the submittals and testing schedule.

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Surface the hidden assumptions a plan or belief rests on, then test what happens when each one is wrong. Use when asked what am I assuming here, check my assumptions, what if I'm wrong about, or stress-test my thinking. Produces the unstated assumptions your conclusion depends on (ranked by how load-bearing they are), a flip of each to see which one breaking would change everything, and the cheapest way to check the riskiest ones — because the assumption you didn't know you were making is what sinks plans.

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Extract and risk-rate hidden assumptions in a product brief or PRD. Use when asked to review a product brief for assumptions, audit a PRD for risks, find hidden assumptions, validate product plans, or run an assumption analysis. Produces a prioritised assumption map with confidence and impact scores, recommended validation methods, and critical assumption flags.

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Extract every hidden assumption from a plan or document and put a price on each one — what it costs if wrong, what it costs to test. Use when about to commit to anything whose author says 'obviously' or whose spreadsheet has hardcoded cells: the bounty hunt makes the invisible load-bearing beliefs explicit and tells you which three to test this week. Produces the assumption ledger (priced and ranked), the cheapest test for each dangerous one, and the document's honest confidence statement.

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Use when asked to check whether a figure is audited, which headline numbers sit outside the audited accounts, what 'adjusted' or 'underlying' means in this report, or who is vouching for the numbers in the highlights. For every headline figure in the highlights, chairman's statement and CEO review of an uploaded annual report, states whether it sits inside or outside the audited statements and what was added or removed versus the nearest audited number. Produces a boundary table, the list of figures outside the boundary, and a plain-English summary of who is vouching for what. Educational, not financial advice.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

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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.

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Audit one, several, or all existing Octane bindings; implement selected maintenance findings or remove redundant copied files with evidence matched to source ownership. Use for binding updates, lifecycle fixes, dependency or metadata maintenance, and convenience-import requests.

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Find and fix a bug in the octane monorepo. Use when given a failing test, a regression, or a report that behavior differs from expectation, including deciding whether a React mismatch is an intentional divergence.

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Audit or defend Octane performance. Use when a change can affect per-render, per-node, scheduling, compiler-output, SSR, hydration, or bundle cost, or when asked whether something is fast enough. Holds the V8-shape, DOM, and scheduling rules hot-path code must follow.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Intelligent loading performance analysis with automated workflows for TTFB investigation (DNS/connection/server breakdown), render-blocking detection, script performance deep dive (first vs third-party attribution), font optimization, and resource hints validation. Includes decision trees that automatically analyze TTFB sub-parts when slow, detect script loading anti-patterns (async/defer/preload conflicts), identify render-blocking resources, and validate resource hints usage. Features workflows for complete loading audit (6 phases), backend performance investigation, and priority optimization. Cross-skill integration with Core Web Vitals (LCP resource loading), Interaction (script execution blocking), and Media (lazy loading strategy). Use when the user asks about TTFB, FCP, render-blocking, slow loading, font performance, script optimization, or resource hints. Compatible with Chrome DevTools MCP.

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