「experiments」の検索結果

Look up prior Claude Code sessions when context is lost or forgotten. Use when asked what was done before, what happened in the last session, or any request to recall past conversation history, prior decisions, experiments, or outcomes. Indexes and searches raw JSONL transcripts from ~/.claude/projects/ via DuckDB. Returns verbatim user messages, summarizes AI actions and sub-agent outcomes, detects tool errors and user frustration signals, and reports tool usage statistics. Summaries cached at ~/.claude/kaizen/session-summaries/.

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Designs conjoint and factorial-vignette experiments: attributes and restrictions, effective-N power, estimand choice among AMCE, marginal means, and AMIE, forced choice versus rating, and PAP tiers. Use when planning a conjoint, critiquing an attribute table, sizing respondents and tasks, or testing interactions.

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Designs and diagnoses list experiments (the item count technique): whether indirect measurement is warranted, control-list construction, design variants, estimators and assumption tests in the list package, and power. Use when measuring a sensitive item or debugging an implausible prevalence estimate.

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Designs multi-country survey experiments: case selection, localization and TRAPD translation, stimulus calibration, per-country power, and pooled versus per-country analysis with measurement-equivalence checks. Use when fielding the same experiment in several countries.

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Drafts or audits a scientific paper's introduction: real-world motivation, evidence audit, theory-to-if-then funnel with a named estimand, bridges across experiments, and calibrated contributions. Use to write, frame, or critique an introduction.

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Designs conjoint and factorial-vignette experiments, covering attribute architecture and randomization restrictions, effective-N power, treatment realism, the choice among AMCE, marginal-mean, and AMIE estimands, forced-choice versus rating designs, and PAP tiers. Use when the user is planning a conjoint, drafting or critiquing an attribute table, asking how many respondents or tasks are needed, whether to report AMCEs or marginal means, or how to test interactions. Reviewing an existing design goes to conjoint-diagnostics, cleaning the export to conjoint-cleaning.

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Designs multi-country survey experiments, covering case selection with a stated comparative logic, instrument localization and TRAPD translation, stimulus calibration, per-country power, and pooled versus per-country analysis with measurement-equivalence checks. Use when the user fields the same experiment in several countries, asks which countries to include, how to adapt an instrument without breaking comparability, whether to pool, or how large each country sample must be. Question wording goes to survey-design, reporting to methods-reporting, the study plan to pre-registration-writing.

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Expert chaos engineer specializing in controlled failure injection, resilience testing, and building antifragile systems. Masters chaos experiments, game day planning, and continuous resilience improvement with focus on learning from failure.

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Automate and research the GitHub Copilot app desktop experience. Use for driving Copilot App sessions, creating isolated app experiments, mapping app UI/accessibility controls, taking app screenshots, saving PNG captures, optimizing WebP images, testing prompts without submitting, using app/session APIs, canvases, automations, macOS Accessibility, AppleScript, and safe end-to-end Copilot App workflow probes.

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Run structured prompt-injection attack and defense experiments against an LLM-integrated app before production by measuring attack success and testing detection or recovery pipelines.

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Expert growth product management guidance for SaaS applications. Use when designing growth loops, optimizing activation and onboarding, building retention systems, creating referral mechanics, running growth experiments, defining north star metrics, or implementing PLG strategies. Covers the full growth lifecycle from acquisition to monetization.

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First-run guided setup for a fresh gAIOS clone. Interviews the user (identity, role, priorities, voice, connections, team, pain, guardrails) and fills the templates — CLAUDE.md, context/, references/voice.md, connections.md — then introduces the WAT / wiki / workflows / experiments features. Trigger on "/setup", "set me up", "get started", "onboard me", or a fresh clone where CLAUDE.md still has {{placeholders}}.

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Respan for LLM observability. Use to set up tracing or the gateway, manage prompts (create/version/deploy), run evals, experiments, and datasets, or work with traces and logs. Trigger for any Respan platform task or when instrumenting an app to send telemetry to Respan.

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Design the smallest viable test to validate or invalidate a critical assumption. Based on Torres's assumption testing framework, organized by Gilad's AFTER model (Assessment → Fact-Finding → Tests → Experiments → Release Results).

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Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper from my experiments", "turn this idea and these results into a paper", "generate a conference submission", "run paper-orchestra on X", or otherwise wants the end-to-end paper-writing pipeline. Coordinates the outline-agent, plotting-agent, literature-review-agent, section-writing-agent, and content-refinement-agent skills.

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Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

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Audit recent high-relevance papers from a target venue to identify claim-relevant experimental evidence gaps after core P0 results exist. Use for venue completeness and reviewer-risk analysis; not for redefining the research question or running experiments.

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Validate and scientifically interpret completed research runs, including negative, null, boundary, confounded, failed, and inconclusive outcomes. Use after execution to assess evidence and claim impact; not to edit methods or schedule experiments.

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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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Systematically interpret nuclear magnetic resonance spectra (1H, 13C, DEPT, and 2D experiments) to elucidate molecular structure. Covers chemical shift assignment, coupling pattern analysis, integration, and correlation of multi-dimensional data into coherent structural proposals.

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Operator-side inventory of experiment PRs for auditing a Senpai research track. It outputs merged winners, a compact results table, and full details for deep dives.

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How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

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Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For ready-made ADMET numbers without training a model use admet-prediction; for benchmark datasets and task-aware splits use pytdc.

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Design guide + benchmarked pitfalls for building Monte-Carlo / Simulated Annealing refinement layers on top of greedy hex-grid layouts for code visualization (packages/gui/src/store/hexLayout.ts, sandbox/hex-sandbox, future Rust ports). Use when: (1) planning to add MC/SA on top of an existing hex-grid pack, (2) deciding whether a simpler SA would match a hierarchical MC with force-directed + chain drag, (3) budgeting wall-clock for million-node cold-start, (4) debugging "blob turns Swiss-cheese" or "folders stop nucleating" during MC refinement, (5) choosing between folder cohesion and raw Σ-link-length as cost function. Contains four hard-won, quantified lessons and anti-patterns that would take a full day of experiments to rediscover.

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