Experiment Planner

Overview

Use this skill when the user needs to explore a research idea, continue an authorized experiment workflow, or accept and interpret results. It turns claims into a testable story and experiment plan. It is an adapter over existing research-agent ideas, not a replacement for the user's writing, review, rebuttal, figure, evidence, or GitHub release skills.

Core Boundaries

• Default domain: general deep learning and computer science research. Adapt to collaborative perception, 3D perception, or autonomous driving only when the task context calls for it.
• For discussion-only planning, keep output in chat unless the user asks for a saved artifact. When the user asks to implement or run experiments in a repository, persist the pre-run result contract: update the paper's final LaTeX tables when a manuscript is in scope; otherwise update the existing experiment-planning document or create experiment-plan.md at the repository root.
• Do not launch long experiments, deploy GPU jobs, modify code, or retry failed runs unless the user explicitly asks for execution or a still-valid approval covers the ordinary continuation. A bounded same-config recovery stays within that approval; do not silently expand its scope.
• Do not replace paper-writing, paper-review-panel, rebuttal-response-skills, paper-visual-craft, or github-project-release; hand off to them only after the research plan or results are ready.
• Treat external projects as references, not installed dependencies. Read references/source-map.md before discussing provenance or upgrading this skill from upstream sources.
• During approved execution, a still-valid earlier user authorization covers ordinary implementation, local bug fixes, queueing in-scope runs, and evaluation. It does not authorize a new experiment family, dataset, seed policy, or paper-only TODO; those remain explicit opt-ins.

Entry Modes

Choose the mode that matches the current request:

• Exploration: turn a new research direction into a falsifiable claim. Use the complete matrix for a substantial new study; a small pilot can use the minimal question/comparison/protocol-plus-budget/endpoint/decision contract.
• Approved execution: continue within the frozen contract and the scope of a still-valid user authorization. Do not silently expand the experiment family, dataset, seed policy, or resource budget.
• Acceptance: compare observed artifacts with the frozen contract and record whether the relevant gate passes, fails, or is inconclusive.

For a continuation, load the latest plan, results, and pass receipt when available. Reuse still-valid decisions about the claim, baseline, metrics, budget, seed, and protocol. Do not restart the initial grill, literature pass, or full matrix unless a material change or unresolved blocker makes it necessary; ask only questions that affect the next decision.

Default Workflow

1. Grill consensus: for a substantial new exploration, use $grill-me style interaction to clarify problem, motivation, proposed claim, baseline/control, compute budget, success criteria, and unacceptable shortcuts. For a small pilot, record only the minimal contract. For a continuation, reuse the settled contract and ask only changed or blocking questions.
2. Literature inspiration: for a substantial new exploration or a changed novelty claim, use $research-evidence for related papers, novelty risk, prior experiment patterns, and unsupported claims. Use $search-first when the task may need existing code, datasets, tools, or implementations. A small pilot may defer this pass when it does not change the decision. Reuse prior literature decisions when the claim and scope are unchanged.
3. Story viability check: decide whether the idea can support a clean paper story: important problem, credible gap, specific method difference, feasible validation, and claims that will not outrun the evidence.
4. Claim freeze: freeze the smallest verifiable claim before planning runs. Avoid changing the story repeatedly while experiments are running.
5. Contract freeze: before scheduling a substantial study or full runs, define the final main-result, ablation, and necessary diagnostic tables. For every metric, record its plain-language definition, unit, direction, aggregation, and any delta reference. For a small pilot, freeze only the endpoint, comparison, protocol-plus-budget, and decision gate. Use the in-scope project's explicit missing-value schema and do not write claims from placeholder cells.
6. Idea validation first: design the smallest pilot/smoke/sanity experiment that can falsify or support the core hypothesis. If multiple GPUs are idle, parallelize only independent exploration runs with clear ownership.
7. Minimum sufficient matrix: only after the pilot passes, add the main result and claim-critical ablations. Add robustness, diagnostics, efficiency, qualitative results, or failure analysis only when they support a paper claim or answer a credible reviewer question; do not add them for symmetry.
8. Subagent coordination: keep the main session responsible for planning, task decomposition, and final result acceptance. Use explorer for read-only repo/config/protocol investigation. Use worker for implementation with explicit file or module ownership. Do not manually override subagent model or reasoning settings unless the user explicitly requests it.
9. Impact-based validation: prioritize checks by their ability to block the next decision or invalidate the claim. Reuse a passing check or receipt when code, data, configuration, protocol, relevant environment, and dependencies are unchanged. Run the smallest useful CPU/configuration smoke check, then proceed to a feasible authorized GPU pilot; do not add endless CPU gates before that pilot. Do not use hashes or hash parity as routine acceptance checks.
10. Run discipline: test that the command starts and produces plausible small outputs; remove test data after smoke checks; launch the full run only after the relevant sanity gate passes; inspect early samples, logs, and artifacts; stop continuous monitoring once the run is confirmed healthy unless the user asks otherwise. Record a compact reusable pass receipt with the scope, checks/evidence, decision, and next action; add a command/run reference or exception only when needed for interpretation or recovery.
11. Acceptance discipline: freeze the acceptance rule, metric, comparison, and claim boundary before inspecting the outcome. Never weaken a gate, switch the primary metric, or redefine the baseline after seeing a result. A diagnostic gate tests a bug, confound, or mechanism; it is not by itself the whole scientific verdict.
12. Explicit-only expansion policy: record one fixed seed and keep compared runs under the same evaluation and checkpoint-selection policy. Use one training run by default. New experiment families, datasets, multi-seed or repeated-seed policies, or other auxiliary runs require explicit opt-in. An earlier authorization covers ordinary implementation, local bug fixes, queueing in-scope runs, and evaluation only. A paper TODO, idle GPU, small margin, low cost, reviewer expectation, or venue norm is not authorization. Keep the resource and time budget honest; mention unrun options only as limitations or explicitly opt-in follow-ups.
13. Default decisions: ask only questions whose answers materially change the plan. If a non-critical choice goes unanswered, use the recommended default and record it as an assumption.

Validation and Failure Taxonomy

Name the relevant gate before running it and do not treat all failures as the same:

• Engineering failure: the command, data path, configuration, checkpoint, or artifact contract is broken. Repair the implementation or protocol before interpreting scientific results.
• Optimization failure: the intended method does not train, converge, or improve the fixed target under a fair protocol. Report it as a method or tuning failure, not automatically as a failure of the underlying idea.
• Prototype failure: a sound, deployable pilot misses its predeclared mechanism endpoint, including a negative endpoint despite a plausible mechanism. Report that measured benchmark endpoint, but do not infer impossibility or falsification of the broader mechanism.
• Core-construct failure: the predeclared construct-level test contradicts the central hypothesis under the frozen protocol. Close or narrow that claim unless new evidence justifies a separately approved design.
• Inconclusive/blocked: noisy or confounded evidence, missing support, an invalid protocol, or an unresolvable engineering issue prevents a gate from being interpreted. Do not relabel this status as a scientific failure.

Diagnostic gates can localize a failure or block a particular interpretation, but passing one does not establish the entire scientific story. Keep the gate, evidence, and remaining uncertainty explicit.

Output Contract

Default to a concise in-chat experiment matrix. Before producing a matrix, read references/experiment-matrix.md. The full 19-field matrix is for a substantial new study. A small new pilot needs only the minimal question/comparison/protocol-plus-budget/endpoint/decision contract. For a continuation, return only changed fields, the next decision, or a compact pass receipt, and reference unchanged fields from the existing plan.

The substantial-study matrix must include:

• research question
• core hypothesis
• paper claim
• storyline
• literature inspiration
• baseline/control
• table contract
• metric definitions
• idea validation experiment
• expected signal
• failure modes
• diagnostic checks
• follow-up experiments
• subagent/task ownership
• compute/resource assumptions
• seed policy
• success gate
• claim gate
• next action

Use the in-scope project's explicit missing-value schema. If none is defined, use TBD (pending), N/A (inapplicable), or unknown (needs user input) as appropriate; do not force -- or invent project facts. Keep recommendations executable, but do not perform execution inside this skill unless the user asks for implementation or running commands, or a still-valid prior authorization already covers the ordinary continuation.

Handoff Rules

• Use $research-evidence before making novelty, citation, or literature coverage claims.
• Use $search-first before proposing new implementation utilities, pipelines, tool integrations, or dataset-processing code.
• Use writing skills only after the experiment story is stable enough to draft a paper section, rebuttal, review, table, or figure.
• For code work, assign worker tasks with disjoint write scopes and remind the worker not to revert others' changes.
• For investigation, assign explorer tasks that are specific, read-only, and non-overlapping with the main session's current work.

Failure Modes To Catch

• The idea is interesting but not falsifiable with available data or compute.
• The proposed contribution is only a presentation change, not a testable method or analysis difference.
• The baseline/control is missing, unfair, or weaker than the claim requires.
• The pilot experiment cannot distinguish mechanism from implementation noise.
• The plan jumps to full benchmark runs before smoke and sanity checks pass.
• The story changes after seeing results without recording a clear reason.
• A continuation reopens settled decisions or asks for the entire initial matrix without a material change.
• Engineering, optimization, prototype, and core-construct failures are conflated.
• A diagnostic gate is treated as the complete scientific verdict.
• An acceptance threshold is weakened, or the primary metric/baseline is changed, after the outcome is visible.
• Validation expands into hash checks or endless low-impact CPU gates instead of reaching a feasible downstream pilot.
• A new experiment family, dataset, seed policy, or paper-only TODO is smuggled into an existing authorization or budget.
• Subagents receive vague tasks, overlapping write scopes, or authority to run long jobs without main-session acceptance.