327건 · 관련도순
Design a chaos engineering experiment — hypothesis, blast radius, steady state, and abort conditions. Use when asked to "design a chaos experiment", "inject a failure", or "test the resilience of this service".
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Real-world problem formulation, mathematical abstraction, and applied mathematics for translating between practical problems and mathematical frameworks. Covers the modeling cycle (problem identification, assumptions, formulation, analysis, validation, interpretation), Polya's framework adapted for modeling, common model types (linear, exponential, logistic, periodic, power-law), dimensional analysis (Buckingham Pi theorem), optimization (linear programming, gradient descent, constraint satisfaction), probability models (Markov chains, queuing theory, Monte Carlo simulation), statistical modeling (regression, hypothesis testing, model selection), model criticism (overfitting, underfitting, sensitivity analysis), and real-world case studies. Use when formulating mathematical models, performing dimensional analysis, optimizing systems, running simulations, or evaluating model validity.
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A/B testing, randomization, sample size calculation, confounding control, and causal inference for data science. Covers the full experimental lifecycle from hypothesis formulation through power analysis, randomization strategies, blocking, factorial designs, sequential testing, and the potential outcomes framework for causal claims. Use when designing experiments, planning A/B tests, calculating sample sizes, or reasoning about causation from data.
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Generate intelligence briefings for the planning dashboard. Use this skill whenever a request file appears in `.planning/console/inbox/pending/` whose `type` field starts with `intelligence.` (refresh_briefing, triage_finding, snapshot_diff, investigate_section, dismiss_finding). The skill reads the per-project KB at `.gsd/intelligence/intelligence.db`, synthesizes a briefing with a causal hypothesis + acknowledged uncertainty + confidence label and ranked moves, then writes the result back to the KB. Always trigger this skill for these request types — do not generate briefings manually.
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Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
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When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.
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Hypothesis-Test Loops via REPL for Exploratory Abductive Inference
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Writes or reviews a pre-analysis plan before data collection — registry choice (OSF, AEA RCT Registry, AsPredicted, registered reports), PAP structure, models and decision rules locked in advance, analysis code run on simulated data, contingencies for attrition, failed manipulations, and exclusions, deviation records, and timeline. Operationalizes the pre-data-collection side of DA-RT. Use when the user asks to draft or audit a pre-registration or PAP, choose a registry, decide what to lock versus leave exploratory, or handle a later deviation. Hypotheses and estimands come from hypothesis-building, post-hoc reporting from methods-reporting.
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Turns a theory into falsifiable, pre-registerable hypotheses, covering the DGP and DAG, identification, SATE versus PATE, counterfactual and directional framing, named estimands, justified SESOIs, the choice among NHST, equivalence, interval, and minimum-effect tests, scope conditions, and primary, secondary, and exploratory tiers. Use when the user asks to turn a research question or theory into hypotheses, whether a prediction is falsifiable, what the estimand is, or how to predict a null. Framing goes to narrative-building, the plan to pre-registration-writing.
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Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.
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Pytest testing patterns for Python — fixtures, behavioral naming, behavior/risk-driven coverage, property-based testing with Hypothesis when useful, and mutation testing when justified. Use when writing tests, designing fixtures, configuring coverage, or applying parametrize, async testing, or property-based strategies.
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Shapes for running many sub-agents at once — fan-out with a barrier, maker/checker, generate-and-filter, tournaments for ranking, hypothesis fan-out for root cause, and loops that stop on a condition with a cap. Use when a phase has several independent targets, when a task is too large or too repetitive for one window, when the same edit applies across many files, when ranking or judging a large set, when several hypotheses compete, or when work must repeat until a signal says stop. Does not apply when units depend on each other's results or the cause is still unknown — investigate first, then parallelize.
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Multi-step reasoning engine for complex analysis and systematic problem solving. Use when: (1) Complex debugging scenarios with multiple layers, (2) Architectural analysis and system design, (3) Problems requiring hypothesis testing and validation, (4) Multi-component failure investigation, (5) Performance bottleneck identification. Triggers: "--think", "--think-hard", "--ultrathink", "analyze step by step", "break down this problem", "systematic analysis". IMPORTANT: Do NOT use for simple single-step tasks.
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The commit-R protocol: a test-driven rewrite of one compartment of Anabasis v2. Commit what is in flight, remove the compartment's tests in an "R:" commit, write them again from a stated hypothesis so the production code fails on purpose, remove the production files in a second "R:" commit, then write the production code again, rewrite the neighbouring functions the old shape bent, and delete what served only it. Use when the operator says "commit R", "commit-R protocol", "remove and rewrite the tests" or "rewrite from scratch", or asks for a test-first pass that looks for a faster, simpler path to the same outcome.
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When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.
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Classifies raw pilot request → structured JSON {task_type, class, artifact, budget_estimate, confidence, routing_tag, resolution_path, schema_version, result_type, expected_result_kind, result_kind_resolution, hypothesis_relation}. Keyword-fast (<200ms) or Haiku fallback (<60s). Does NOT create WP or call executor.
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Systematic, hypothesis-driven debugging workflow with triage-based track routing. Use for debugging, fixing bugs, and investigating errors.
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Systematic, hypothesis-driven debugging workflow with triage-based track routing. Use when asked to "fix this bug", "debug this", "why is this failing", "this is broken", "investigate this error", "track down this issue", or any debugging situation. Supports --deep flag to force full investigation.
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Design and execute chaos engineering experiments using Litmus or Chaos Mesh. Test system resilience through controlled fault injection, validate hypothesis-driven tests, and improve failure recovery. Use before major product launches, after architecture changes to validate resilience, during GameDays or disaster recovery drills, to validate assumptions about failure modes, or as part of an SRE maturity program.
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Guide a person in systematic observation of systems, patterns, or phenomena. AI coaches neutral attention, field notes methodology, pattern recognition, hypothesis formation, and structured reporting for debugging, research, and system understanding. Use when a person wants to understand a system's behavior before intervening, when someone keeps jumping to conclusions and needs the discipline of observation first, when preparing an evidence-based report, or when studying team dynamics or process effectiveness through direct observation.
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AI solution exploration using ant colony optimization — deploying scout hypotheses, reinforcing promising approaches, detecting diminishing returns, and knowing when to abandon a strategy. Use when facing a problem with multiple plausible approaches and no clear winner, when the first approach is not working but alternatives are unclear, when debugging with no obvious root cause requiring parallel hypothesis investigation, or when previous attempts have converged prematurely on a suboptimal approach.
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AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis. Implements Cursor Debug Mode methodology — structured hypothesis ranking, targeted code instrumentation, human-in-the-loop reproduction gate, log-confirmed root cause, and mandatory cleanup.
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Generate structured Product Requirements Documents using hypothesis-driven methodology with Implementation Phases tracking.
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Sequential thinking and structured problem solving. Break down complex problems into steps with revision and branching capabilities. Use for multi-step analysis, planning, and hypothesis verification.
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