「utc」の検索結果

Use when planning with fixed deadline or target outcome, working backward from future goal to present, defining milestones and dependencies, mapping critical path, identifying what must happen when, planning product launches with hard dates, multi-year strategic roadmaps, event planning, transformation initiatives, or when user mentions "backcast", "work backward from", "reverse planning", "we need to launch by", "target date is", or "what needs to happen to reach".

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Produce a plain-language manual test plan from the context supplied to it — an executive summary, a high-level list of named tests, and a detail section per test with the steps a person follows by hand and the outcomes they should expect. Use when you want to create, draft, generate, or outline a manual test plan, manual QA steps, hands-on verification steps, or an acceptance walkthrough for a feature, change, branch, plan, or PR. When the plan holds more than five tests and at least two natural categories emerge, both the test list and the detail sections are organized under plain-language categories. When nothing in the supplied context can be manually tested, it says so and asks for more context instead of producing a document. Does not analyze code for automated test coverage gaps — use automated-test-planning. Does not write test code — use tdd. Does not review code quality — use code-review. Does not stress-test an existing plan — use iterative-plan-review.

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Surfaces Han's shared standard for explaining technical work to a reader who will not implement it into the calling skill's own context, so the caller writes its escalations, confirmation turns, and stops as a concrete outcome the reader could observe rather than as a mechanism. Use when a skill is about to ask a person a question, stop for an input, or explain a technical consequence to someone who will not open the code. Governs what a run says to a person in a turn, where readability-guidance governs the shape of a written deliverable. Runs in the caller's context and hands control straight back; it does not produce a deliverable of its own, rewrite anything, or judge the caller's work. Carries guidance only and adds no self-check step.

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Test web applications end-to-end by simulating user interactions and verifying expected outcomes. Trigger when the user asks to: test a web app, verify a user flow, run end-to-end tests, QA a feature, check that a page works correctly, validate user journeys, or test a deployment.

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TRIGGER THIS when building DEI strategy, creating diversity goals, designing inclusive programs, building equity analyses, developing training, addressing inclusion issues, or tracking DEI metrics. Develops comprehensive diversity, equity, and inclusion strategies aligned to business goals with measurable outcomes, training programs, metrics frameworks, and accountability systems.

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Use when the user is closing an ISO week, reviewing planned outcomes against evidence, interviewing work Areas, resolving unfinished commitments, or asking for "ретро", "weekly retro", "week review", or lessons from the week.

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Use when a completed department retro should become accepted outcomes and a full living ISO-week plan, or the user asks "планирование", "план недели", or "что делаем на следующей неделе".

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Создаёт несколько вариантов дизайна 2D-поверхности (лендинг, герой, обложка, слайды): свой визуальный референс и автор на вариант, полный design.md с UTC/SHA-256 до кода, проверка в браузере, галерея, картинки в чат, выбор человека и второй раунд смешения победителей. Применять по /make-landing, «варианты дизайна», «несколько вариантов лендинга на выбор», «редизайн».

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When handing work to the Codex CLI earns its cost, and how to size the run: second-model review, bounded implementation hand-offs, sandbox permissions, model and reasoning effort. Use when the user asks for Codex or `codex exec`, when a change is complex or high-stakes enough that an independent reviewer would change the outcome, or when a delegated run needs its model, effort, or permissions chosen. For agents other than Codex itself.

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Think and work like an expert Ecologist. Use when a task calls for Ecologist judgment. Reasons from Preston/Fisher SADs and Hutchinson fundamental vs realized niches; designs quadrat/transect and distance/occupancy surveys; filters GBIF issue flags and iNaturalist DQA; fits vegan/iNEXT/unmarked GLMMs with Moran's I and nlme/glmmTMB spatial correlation while treating pseudoreplication, effort bias, and citizen-science artifacts as first-class failure modes.

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Think and work like an expert Causal Inference Scientist. Use when a task calls for Causal Inference Scientist judgment. Reasons from structural causal models, potential outcomes, and identification logic through DAGs and do-calculus, doubly-robust estimators (AIPW/TMLE, IPTW, g-formula), and design-based methods (IV, RD, Callaway-Sant'Anna DiD, synthetic control) while treating colliders and M-bias, positivity/overlap failure, and unmeasured confounding (Rosenbaum bounds, E-values) as first-class failure modes.

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Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.

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Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

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Analyzes time-to-event data using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression with lifelines. Builds survival models from clinical and omics features. Use when predicting patient survival or modeling time-to-event outcomes.

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Design guides for cytosine and adenine base editing using editing window optimization and BE-Hive outcome prediction. Select optimal positions for C-to-T or A-to-G conversions without double-strand breaks. Use when designing base editor experiments for precise nucleotide changes.

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Audit a design against Dieter Rams' ten "Good design is..." principles, then hand off a /make-plan prompt for one of three outcomes — new design, refine design, or redesign. Use when the user says "audit this design", "design review", "check this UI against Rams", "is this UI good", "critique this design", "design audit", or asks for a critique that should lead to a plan.

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Run outcome-focused Firecrawl workflows that produce deliverables such as research reports, literature reviews over published papers, SEO audits, QA reports, lead lists, knowledge bases, website design systems, and other structured web-data artifacts. Use when the user wants Firecrawl to complete a business, marketing, product, or creative workflow rather than merely scrape a page or integrate API calls into code.

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Map user missions from trigger to value moment, organizing features into coherent paths during PRD v0.4 User Journeys. Triggers on requests to map user journeys, define user flows, describe how users accomplish goals, or when user asks "map user journeys", "define user flows", "user missions", "how do users accomplish X?", "journey mapping", "what steps do users take?", "pain to value flow". Consumes PER- (Persona Definition), FEA- (Feature Value Planning), KPI- (Outcome Definition). Outputs UJ- entries with step flows, pain points, and value moments. Feeds v0.4 Screen Flow Definition.

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Define and prioritize features with strategic traceability during PRD v0.3 Commercial Model. Triggers on requests to define features, prioritize capabilities, scope MVP, map features to pricing tiers, identify parity vs. delta features, or when user asks "what features do we build?", "what's in MVP?", "which features matter?", "feature priority", "parity features", "what's our delta?". Consumes KPI- (Outcome Definition), BR- (Pricing Model, Moat), and CFD- (Market Moat Analysis) from v0.3. Outputs FEA- entries with strategic traceability and BR-FEA- governance rules. Feeds v0.4 User Journeys.

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Establish weekly customer-discovery cadence and Opportunity Solution Tree practice using Teresa Torres's Continuous Discovery Habits framework during PRD v1.0 Market Adoption. Triggers on requests to set up discovery cadence, build opportunity solution tree, run weekly customer interviews, or when user asks "Torres", "continuous discovery", "opportunity solution tree", "outcomes vs outputs", "weekly interviews", "assumption mapping". Outputs CFD-* discovery entries and updates to ADO-STAGE-* and PER-* with new evidence.

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Define measurable success metrics (KPIs) tied to product type during PRD v0.3 Commercial Model. Triggers on requests to define success metrics, set KPI targets, determine what to measure, establish go/no-go thresholds, or when user asks "how do we measure success?", "what metrics matter?", "what's our target?", "how do we know if this works?", "define KPIs", "success criteria". Consumes Product Type Classification (BR-) from v0.2. Outputs KPI- entries with thresholds, evidence sources, and downstream gate linkages.

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Goal-driven E2E testing where a browser agent (Playwright MCP / computer-use) reads a natural-language goal and explores the app via the accessibility tree to assert outcomes — no pre-written script. Covers when intent-driven beats scripted, making agent runs deterministic (pinned model, temperature 0, seeded data, bounded steps, explicit success assertion, snapshot-not-pixel), cost/latency control, the accessibility-tree-first interaction model, CI gating, and graduating a stable run into a scripted Playwright test. Use when: "agentic browser test," "goal-driven browser test," "let an agent explore the app," "natural-language E2E," "browser agent smoke test," "Playwright MCP test." Not for: Writing/maintaining deterministic scripted Playwright tests — that is playwright-automation. Testing your product's OWN LLM features — that is ai-system-testing. Related: playwright-automation, ai-system-testing, exploratory-testing, test-reliability, qa-project-context.

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Apply during planned rewrites and migrations with explicit phase boundaries. Converge on the target architecture; don't preserve smooth intermediate states with throwaway compatibility code.

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Battle-hardened NFT developer specializing in ERC-721/1155 implementations, gas-optimized minting, reveal mechanics, and marketplace integration. Has launched 50+ collections from stealth 1/1s to 10k PFP drops.Use when "build nft contract, mint function, erc721 implementation, erc1155 implementation, nft reveal, allowlist mint, dutch auction, lazy mint, on-chain metadata, royalty enforcement, operator filter, batch mint gas, token uri, soulbound token, nft, erc721, erc1155, erc721a, solidity, smart-contracts, opensea, blur, metadata, ipfs, arweave, royalties, minting, reveal, allowlist" mentioned.

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