「measure」の検索結果

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Run work as an explicit event-driven state machine executed by subagents. The orchestrator is the runtime from the canonical loop — `state, effects = init(machine); execute(effects); while state is active: event = nextEvent(); state, effects = transition(machine, state, event); execute(effects); return state.output`. All routing judgment is authored ONCE, at plan time, into a machine file (states, event alphabet, transition table, budgets, final states); at runtime the orchestrator does pure table lookups and dispatches effects — it never decides, never authors, never ingests artifacts. Subagent/sync protocol is the timeboxed-iterating one: role prompts scaffolded once to disk and dispatched BY PATH, an initialiser preamble every subagent reads first, a self-populating cheatsheet, per-unit files, tiny structured returns, independent measurement gate. Use when the user says "workmachine", "run this as a state machine", "state-machine this", or when a goal has a small number of clearly named phases with explicit outcomes (built / passed / failed / blocked) and you want the routing to be auditable rather than judged on the fly. For clock-driven loops use timeboxed-iterating; for wide graphs with judged edge criteria use workgraph.

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Rigorous analysis expert covering limits, continuity, sequences, series, and measure theory foundations

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Rigorous probability theorist covering measure-theoretic probability, stochastic processes, and advanced probability

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Adopt declscope on an existing Go codebase and drive its diagnostics to zero. Read this when introducing declscope to a repository, when choosing its configuration, when running declscope shrink for the first time, or when clearing a declscope baseline. Covers sizing each rule before enabling it, reading the diagnostics as structure, the remedy for each shape, and the measurement traps that produce false confidence. For writing new code in a repository that already runs declscope, use declscope-authoring.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Performance profiling principles. Measurement, analysis, and optimization techniques.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Abductive reasoning about causes. Takes a phenomenon, separates observations from interpretations, then spawns diagnosticians applying different reasoning lenses (technical, human-factors, process, incentive-structure, environmental, temporal, measurement-artifact, statistical) in isolation. Orchestrator evaluates candidate causes for fit-against-evidence, calibrates confidence honestly, and reports leading candidate(s) with distinguishing evidence needed. Produces feedback only — no code, no tickets, no artifacts.

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Use when assessing body fat percentage from skinfold measurements, calculating body density, or interpreting body composition results using Durnin-Womersley or Jackson-Pollock equations.

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Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests.

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Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

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Runtime profiling with multi-metric measurement, instrumentation, and performance map generation

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Estimate the full development cost of an existing codebase from lines of code, architectural complexity, and team-composition overhead. Use for 'how much would this cost to build', 'what did this codebase cost', development-cost or build-cost estimates, calendar-time estimates, and Claude/AI ROI on a delivered codebase. Estimates by measured LOC and complexity, not by ticket volume.

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Use when recording, tallying, or triaging stochastic review findings — the append-only JSONL ledger where Tier 2 (advisory, unevidenced) reviewer output goes instead of blocking language. Triggers on "log this finding", "ledger add", "tally the findings", "triage findings", "is this finding recurring", "retire this noise", or whenever a reviewer produces an unevidenced concern that should be measured for recurrence rather than chased. For the review itself see code-review-and-quality or skill-library-review; for verifying formal claims with deterministic evidence see adversarial-claims-reviewer.

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Audit your company's AI spending — find waste, measure ROI, and right-size your tool stack.

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Manual-only skill for minimizing total codebase size. Only activate when explicitly requested by user. Measures success by final code amount, not effort. Bias toward deletion.

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Ask Claude, ChatGPT, and Gemini a question and capture the full trace behind the answer (the search queries each engine ran, the pages it retrieved, and the sources it cited, span by span). Use when asked to "trace an AI answer", "see what AI engines cite", "check AI citations", "which sources does ChatGPT/Claude/Gemini use", "why does the AI recommend X", or for any GEO (Generative Engine Optimization) measurement.

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Score an Instagram creator's (or a list of creators') audience authenticity and engagement quality using Xpoz. Measures real engagement rate against views rather than follower count, the bot/spam share of their comments, and an overall 0-100 authenticity score for influencer vetting and partner selection. Use when asked to "vet this influencer", "are their followers real", "check engagement quality for @username", "rate these Instagram accounts", "compare creators before a sponsorship", or when the user pastes a list of Instagram handles to evaluate.

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Content-addressed baseline-archive epoch partitioning. Partition baseline_archive.jsonl + the self-improving hub's baseline serving by a HASH of the full baseline-production+measurement spec — when the spec (margin_rule, fitness/margin logic version, the 4 roles' model+source, rubric/dim-set, bench, seed-pool identity) changes, the hash changes → a new epoch series (like seed-gen gen-*). Triggered by "baseline epoch", "baseline 아카이빙", "epoch partition", "spec hash", "content-addressed", "margin_rule namespace", "production logic 구분", "baseline 하위 서빙" keywords.

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Ground GEODE design and evaluation work in Stanford CS329A Parts 2 and 5. Use for test-time compute, planning, multi-step reasoning, deep research, LATS, independent subplans, best-of-N, repair depth, measurement replication, verifier authority, and GEODE/Eco²/SIL/Crucible comparisons.

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Apply GEODE's measured architecture, module and symbol naming, Python type/class, persisted schema, test, site, and versioning conventions. Use when adding or moving modules, choosing dataclass/Pydantic/Protocol/ABC forms, naming classes or files, designing stored data, reviewing convention compliance, or deciding compatibility and release version treatment.

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Design, implement, or evaluate GEODE's GEO capability with source-grounded stage diagnosis, frozen measurement contracts, and no-model preflight before live engine runs.

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