「experiment」の検索結果

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 new proteins that bind a chosen surface, using BindCraft's AlphaFold2-guided hallucination or the RFdiffusion backbone plus ProteinMPNN sequence pipeline. Use this skill to specify a target epitope by hotspot residue, trim a receptor to the region worth designing against, set up a design campaign, and filter the output on the in-silico metrics that predict experimental success — interface predicted TM-score, predicted aligned error at the interface, buried surface area, and shape complementarity. Also trigger on BindCraft, RFdiffusion, ProteinMPNN, minibinder, hallucination, inverse folding, hotspot residue, epitope targeting, ipTM, or de novo binder.

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Use when asked to prototype one design question through a cheap logic or UI experiment, including button-driven state-model checks. Not for polished artifacts: use polished-web-prototype.

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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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Throwaway experiments to validate feasibility before building.

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Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.

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Create today's daily_reports/YYYYMMDD.md by carrying over pending TODOs and recent experiment results from yesterday's report. Use at the start of a session.

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Run cross-experiment competition strategy synthesis. Invokes the competition-strategist agent which loads daily_reports/* + all SESSION_NOTES.md / claudeSummary.md / submit/SUBMISSIONS.md / KAGGLE_DIRECTION.md in parallel and proposes next moves. Use weekly or at inflection points.

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Use when developing in packages/agent-core-v2 (the DI × Scope agent engine) — adding or modifying a domain Service, choosing a LifecycleScope, wiring DI dependencies, splitting a domain across scopes, owning or migrating a config section, gating behavior behind an experimental flag, raising coded errors, working on the permission system, writing DI/Scope tests, porting business logic from agent-core (v1) to v2, triaging a main-branch commit against v2, or exposing a v2 domain over server-v2 while keeping the /api/v1 wire contract compatible with released clients. Self-contained guide organized by development stage (orient → design → implement → test → verify) plus align workflows for v1→v2 migration, main-branch commit triage, and server-v2 wire exposure; each file carries the rules, examples, and red lines for its step.

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Design A/B tests with hypotheses, variants, sample sizes, and analysis plans. TRIGGERS - Use when user wants to run A/B tests, split tests, or experiment with variations.

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Use for code generation, feature work, bug fixes, refactoring, and code review when a change creates or reshapes a multi-step flow, state lifecycle, side-effect or decision ownership, cross-unit composition, or boundary contract. Also use it when an existing structural problem makes those elements hard to trace or verify. Structural change is optional. Mechanical edits, throwaway experiments, and coherent local changes stay with their current owner and focused verification.

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Intake for an autonomous research run (Arbor). Use when the user wants to optimize a metric in a repo over many isolated experiments — 'optimize this benchmark', 'improve the model F1 overnight', 'beat the leaderboard'. Discovers the repo/eval/splits, measures the baseline, confirms a Research Contract, then emits a ready-to-send /auto-research command. Does NOT start the run itself.

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DECIDE-phase doctrine for an Arbor research run: when to merge, prune, combine, or finalize; how the held-out merge gate works; and the rule that the final report uses TEST scores. Load before deciding what to do with completed experiments.

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Use when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.

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Inspect and operate Inngest Cloud through its OAuth MCP connection. Use for deployed apps and functions, failed or slow runs, traces, events, environments, sessions, experiments, and Insights. Prefer this for Cloud operations when MCP tools are available; use CLI or REST skills for explicit terminal or HTTP tasks.

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Manus-style context engineering for Agent Teams. Coordinate multiple Claude Code instances with shared planning files. Use when complex tasks need parallel work (code review, debugging, feature development). Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1.

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REQUIRED before writing code for any feature touching auth, payments, credentials, tokens, sessions, file upload, user data, untrusted input, deserialization, network endpoints, or data deletion. Scope by that surface, not the task label — a research/experimental/local-only feature with none of it doesn't need this. Invoke it, record answers inline in the design doc or PR description, then write the code. Skipping this is how auth bypass, token leakage, and delete-the-wrong-record bugs ship. If the feature touches any of those surfaces and this hasn't run, the design is incomplete — do not proceed to implementation.

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REQUIRED before declaring a task done when the diff touches user input, SQL, shell, auth, credentials, file paths, serialization, crypto, network endpoints, data deletion, or dependency surface. Judge by that surface, not the task label — research/experimental/local-only code with none of it can skip this. Walks OWASP/ASVS/MITRE questions the compiler and the test suite won't catch. Pair with `rafter run` on the same diff. When the diff has that surface, marking it complete without both has been under-delivered — do not claim done.

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Entry point for rafter. Invoke when a sub-skill is unclear, or when the task needs `rafter run` (remote SAST+SCA), `rafter secrets` (local secrets-only), `rafter audit`, policy checks, or command-risk evaluation. Scope by security surface, not task label: engage when the diff touches auth, credentials/secrets/tokens, untrusted input, SQL, shell/exec, file paths, deserialization, crypto, network endpoints, data deletion, or dependencies; for research/experimental/local-only code with none of that, a quick surface check is enough. When such surface IS present and no rafter skill or CLI call has been made, invoke this before handing the task off — an un-evaluated "done" on genuine security surface is not done.

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Define the smallest viable experiment and MVP for a selected one-person company opportunity. Use when Codex needs to explain what MVP means when needed, verify prerequisites, ask one question at a time, present multiple MVP options, and write user-confirmed outputs into `opc-doc/`.

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On-demand journal peer review simulation expert — supports 7 research paradigms (experimental/computational/empirical_survey/interpretive/mixed_methods/systematic_review/interdisciplinary) with 6-step review workflow, outputting targeted revision suggestions based on journal-specific review standards.

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Use when the user asks to create, edit, rebuild, or refine editable diagrams in `.drawio` / diagrams.net format, especially for academic figures such as system architecture diagrams, technical roadmaps, workflow diagrams, method pipelines, module relationship diagrams, and experiment flowcharts. Use this skill when the user wants publication-ready outputs with both an editable source file and a PNG export, or when an existing figure image needs to be rebuilt into a clean editable academic-style diagram.

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MATLAB R2026a robotics and autonomy workflow for ROS Toolbox, Robotics System Toolbox, Navigation Toolbox, UAV Toolbox, sensor fusion, trajectory planning, simulation, and reproducible robot experiments. Use for ROS/ROS 2 bags, robot models, path planning, SLAM, state estimation, and autonomy prototyping.

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MATLAB R2026a workflow for signal processing, DSP, image processing, computer vision, lidar, medical imaging, deep learning, and AI-assisted reproducible experiments. Use for filters, spectrograms, wavelets, object detection, segmentation, classification, training, inference, and generated figures.

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