/ai-research-reproduction

name: ai-research-reproduction
description: 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, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, score chasing, or broad research assistance outside repository-grounded reproduction.

ai-research-reproduction

Purpose

Guide README-first deep learning reproduction toward a minimal trustworthy run with auditable evidence. Reproduction is not "make it run by changing anything"; faithfully read the README, environment, weights, datasets, and documented commands, then record results and deviations. Start with `references/agent-operating-principles.md`; load `references/research-rigor-principles.md` and `references/deep-learning-experiment-principles.md` when scientific meaning or experiment details are at stake.

For first-use problems, run `scripts/doctor.py` with the intended Python (read-only; optional `--repo` and `--require-module`). The deterministic entrypoint is `scripts/orchestrate_repro.py` with a self-contained `_bundled/` runtime, so this skill works when installed alone; separately installed companion skills remain optional reusable entrypoints. Use the entrypoint and `--help` for routine runs; inspect its implementation when a concrete blocker or safety question requires it. Executed commands persist lifecycle state, append-only events, and full streamed stdout/stderr under `repro_outputs/_runtime/<run_id>/`. A `CANCEL` file in the active run directory requests process-tree cancellation. For recovery, queues or model gates, read `references/runtime-and-model-adapter.md`; for the optional model/tool loop, read `references/agent-runner.md` and use `scripts/run_agent.py`.

Fit

Use this skill when all are true:

• The target is an AI code repository with a README, scripts, configs, or

documented commands.

• The request spans multiple trusted phases such as intake, setup, execution,

training verification, analysis, paper-gap resolution, and reporting.

• The desired result is a small reproducible target, not broad experimentation.

Do not use this skill for paper summaries, generic environment setup, isolated repo scanning, standalone command execution, open-ended research design, or explicit candidate-only 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. Read the README and nearby repo signals. 2. Run the bundled `repo-intake-and-plan` stage to extract commands and targets. 3. Select and justify the minimum trustworthy target. 4. Run `env-and-assets-bootstrap` only for target-specific environment, checkpoint, dataset, and cache assumptions. 5. Run `analyze-project` only when structure, insertion points, or suspicious implementation patterns need read-only clarification. 6. Use `minimal-run-and-audit` for documented inference, evaluation, smoke, or sanity execution. Keep direct execution as the default; native shell syntax requires explicit review and authorization. 7. Use `run-train` instead when the selected trusted target is training startup, short-run verification, full kickoff, or resume. 8. Pause for human review before fuller training claims or any change that could alter dataset, split, checkpoint, preprocessing, metric, loss, model semantics, or result interpretation. 9. Award `result-match` only when explicit expected metrics are compared under a recorded tolerance; observed metrics alone prove execution, not reproduction. Then write the standardized outputs and a concise final note in the user's language when practical. 10. Once the requested target and evidence checks are complete, return the bounded result and stop. Optional stages and further README commands 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/`:

SUMMARY.md
COMMANDS.md
LOG.md
SCIENTIFIC_CHANGELOG.md
COMPARABILITY_REPORT.md
status.json
ANNOTATED_README.md   # original README + colored per-section agent-action annotations
PATCHES.md   # only if patches were applied

Use the templates under `assets/` and the field rules in `references/output-spec.md`.

• Put the shortest high-value summary in `SUMMARY.m