Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution,

ai research reproduction Purpose Guide README first deep learning reproduction toward the smallest trustworthy run with auditable evidence. Preserve documented meaning; record assumptions, deviations and blockers instead of changing semantics to manufacture success. Load specialized references only for a concrete uncertainty. Fast Path For a routine bounded run, keep the control path short: 1. Read the target README and only the target test/config/source needed to understand the documented command. 2. Run scripts/orchestrate repro.py repo <repo plan only agent output with any explicit user timeout bound ( timeout or train timeout ) already supplied; review command candidates , the selected cmd XX , side effect contract, selection fingerprint, and returned reviewed run args . With no output dir , later evidence goes to <repo /repro outputs regardless of caller cwd. 3. Run the selected candidate, or another reviewed candidate, with run selected command id <cmd XX plan fingerprint <fingerprint agent output plus requested timeout/metric/source adjacent options. Preserve an explicit user command timeout bound instead of silently making it stricter on a routine trusted run. timeout limits the target command; do not wrap the whole orchestrator in an equal or shorter external timeout, because it still needs time to terminate children and write terminal evidence. A changed command set fails closed; setup/download commands are never target candidates. 4. Run verify output agent output ; inspect detailed evidence files only when verification fails or the result is partial/blocked. 5. Deliver the bounded result and stop. For a host with short tool call deadlines, rerun planning with include agent handoff and follow references/agent job.md ; otherwise keep the direct path above. Job completion is not task acceptance, and uncertain state is never a reason for automatic replay. Do not inspect orchestrate repro.py , annotate readme.py , bundled/ , writers, or runtime internals on a normal success path. Inspect implementation only for a concrete blocker, unexpected side effect, bundle integrity failure, or unresolved safety question. Use scripts/doctor.py for first use environment/install diagnostics. Executed commands keep full lifecycle/log evidence under repro outputs/ runtime/<run id / . Fit Use this skill for repository grounded, multi phase trusted reproduction where the goal is a small reproducible target. Do not use it for paper summaries, generic setup, isolated scanning, standalone commands, open ended research design, or explicitly authorized candidate exploration. Trusted Target Selection Choose the smallest target that can honestly demonstrate repository grounded reproduction: 1. documented inference 2. documented evaluation 3. documented training startup or partial verification 4. full training only after explicit user confirmation Treat README guidance as the primary reproduction intent. Use repository files to clarify the README, not to silently replace it. When the README and paper conflict, record the conflict and use paper context resolver only for the narrow reproduction critical gap. Workflow 1. Treat README guidance as primary; extract and select the minimum trustworthy target. 2. Use setup/assets only for target specific prerequisites and analyze project only when structural clarification is needed. 3. Use minimal run and audit for inference/evaluation/smoke and run train for training startup, kickoff, or resume; direct execution is the default. 4. Pause before fuller training or changes to dataset, split, checkpoint, preprocessing, metric, loss, model semantics, or interpretation. 5. Award result match only against explicit expected metrics and tolerance; process success alone is not reproduction success. 6. Write the evidence bundle, return the requested bounded result, and stop; optional stages are not automatic follow up work. Patch Boundary Prefer no repository edits. If edits are needed, keep them conservative and auditable: Try command line arguments, environment variables, path fixes, dependency version fixes, or dependency file fixes before code changes. Reproduction fixes are allowed when needed, but they must not be hidden. State what changed, why it was necessary, whether it changes scientific meaning, and whether it affects comparability with the paper, README, or baseline. Avoid changing model architecture, core inference semantics, training logic, loss functions, or experiment meaning. If repository files must change, create a branch named repro/YYYY MM DD short task , keep verified patch commits sparse, and record README fidelity impact in PATCHES.md . See references/patch policy.md . Outputs Always target repro outputs/ : Use the templates under assets/ and references/output spec.md . Keep summaries short, commands copyable, machine state stable, and scientific/comparability changes explicit. ANNOTATED README.md must preserve the source README byte for byte outside inserted evidence blocks and pass its strip/check round trip. Use source adjacent readme only for an owned RIGORPILOT README.md ; never replace an unrelated file. Distinguish verified facts from inference. Reference Loading Workflow judgment: references/agent operating principles.md . Human readable output: references/language policy.md . Scientific/comparability judgment: references/research rigor principles.md and, when experiment details matter, references/deep learning experiment principles.md . Protocol sensitive changes: references/research safety principles.md and references/patch policy.md . Personal rigor and lessons are advisory only; keep specialized detail in references/scripts rather than expanding this entrypoint.

ai-research-reproduction