--- id: daily-repo-intake-and-plan name: "repo-intake-and-plan" url: https://skills.yangsir.net/skill/daily-repo-intake-and-plan author: lllllllama domain: ai-agent-core-development tags: ["deep-learning", "ai-agents", "research", "reproducibility", "experimentation"] install_count: 127500 rating: 4.80 (716 reviews) github: https://github.com/lllllllama/ai-paper-reproduction-skill --- # repo-intake-and-plan > RigorPilot 技能旨在赋能AI智能体，使其能够以科学严谨的方式复现、改进和探索深度学习研究项目。它强调有意义的变更、公平的比较、可复现的证据以及可审计的修改，从而推动深度学习研究的实质性进展，而非盲目追求分数。该技能专注于深度学习研究仓库，处理环境设置、数据、模型训练与评估等关键环节。 **Stats**: 127,500 installs · 4.8/5 (716 reviews) ## Before / After 对比 ### 加速深度学习实验复现 **Before**: 手动复现深度学习论文实验，需要耗费大量时间进行环境配置、代码调试和结果验证，通常需要数天甚至数周，且容易出现人为错误，导致复现失败或结果不一致。 **After**: AI智能体利用RigorPilot技能，能够自动化处理深度学习实验的复现、改进和探索过程，大幅缩短实验周期，提高复现成功率和结果的可靠性，让研究人员专注于创新。 | Metric | Before | After | Change | |---|---|---|---| | 实验复现时间 | 72小时 | 12小时 | -83% | ## Readme # RigorPilot Skills Research-first Agent Skills for Deep Learning Experiments. RigorPilot helps AI agents reproduce, improve, and explore deep learning research projects with scientific rigor: meaningful changes, fair comparison, reproducible evidence, and auditable modifications. > Not just higher scores. Meaningful deep learning research progress.

English | 简体中文

research-first deep learning reproducibility explicit exploration platforms skills public skills tests clients

Brand note: the project brand is `RigorPilot Skills`; the recommended GitHub repository slug is `rigorpilot-skills`. Legacy install paths remain documented only as compatibility fallbacks while older clients and bookmarks migrate. Migration note: - Project brand: `ai-research-workflow-skills` -> `RigorPilot Skills` - Existing compatible skill slugs remain available. - Preferred install source: `lllllllama/rigorpilot-skills` - Legacy fallback source: `lllllllama/ai-paper-reproduction-skills` - `ai-paper-reproduction` -> `ai-research-reproduction` - `research-explore` -> `ai-research-explore` ## What RigorPilot Is - Research-first Agent Skills for deep learning experiments. - It helps AI agents reproduce, improve, explore, and audit deep learning research work. - It is designed for personal research use first. - It values scientific meaning, fair comparison, reproducibility, explainability, and collaborator control. - It encourages meaningful novelty during exploration, but does not overclaim novelty. ## What RigorPilot Is Not - Not a generic coding agent. - Not a score-chasing automation framework. - Not a guarantee of novel discoveries. - Not a replacement for researcher judgment. - Not a rigid workflow that should weaken strong models. ## Core Principles 1. Do not chase scores blindly. 2. Do not claim novelty lightly. 3. Do not break comparability silently. 4. Do not disguise engineering fixes as research contributions. 5. Do not leave collaborators out of control. See [references/research-rigor-principles.md](references/research-rigor-principles.md). ## Rigor and Novelty Rigor is the baseline. Novel is the aspiration. Novelty and significance remain hypotheses until supported by literature contrast, ablation evidence, and fair comparison. RigorPilot should add research judgment and audit awareness without making strong models slower, more mechanical, or less capable. ## Deep Learning Focus RigorPilot is built for deep learning research repositories where README commands, environment setup, data, weights, checkpoints, training, evaluation, metrics, logs, baselines, SOTA tables, and ablations all matter. This repository is still built around one compatibility rule: `trusted by default`. - Ambiguous requests route to the trusted lane. - Exploration requires explicit authorization. - Trusted outputs are auditable and durable. - Explore outputs are candidate-only and disposable. Shared operating principles live in [references/agent-operating-principles.md](references/agent-operating-principles.md). They keep the skills focused on high-level guidance: think before acting, keep the solution small, change only what is necessary, and work toward verifiable goals. They are guardrails, not a detailed script for every implementation choice. ## 🧭 Current Repo Snapshot This repository currently ships: - `11` skills total: `9` public skills and `2` helper skills. - `6` trusted-lane public skills and `3` explore-lane public skills. - `4` project-scoped Claude Code command wrappers under `.claude/commands/`. - `45` Python scripts, including `43` test scripts with focused `research-explore` regressions and document-structure checks. - A RigorPilot Explore chain that now includes bounded idea-seed generation, explicit idea score breakdowns, atomic idea decomposition, and implementation-fidelity evidence split into planned, heuristic, and observed layers. - A documented and tested workflow intended to be usable from both Windows PowerShell and Linux shells. The skills use the open `SKILL.md` layout, so the same repository can be installed into neutral Agent Skills directories as well as Codex and Claude Code. For shared local installs, prefer `~/.agents/skills/` or `./.agents/skills/`. Client-specific installs under `~/.codex/skills/` and `~/.claude/skills/` remain supported. ## 💻 Windows and Linux Notes This repository is intended to be usable on both Windows and Linux. - The command examples below are written in a shell-neutral style around `python ...`, `npx ...`, and relative paths. - For user-scoped install targets, prefer `$HOME/.agents/skills`, `$HOME/.codex/skills`, and `$HOME/.claude/skills`. These work well in Linux shells and in PowerShell, and Python accepts forward slashes on Windows paths. - Project-scoped paths such as `./.agents/skills` and `./tmp/codex-skills` are also valid on both platforms. - The repository validation and routing checks are already exercised on Windows and Linux-oriented environments through local tests and CI. ## 🛠️ Install For most users, start with `npx`. It is the shortest path and should be enough for normal use. ### Recommended: `npx` Install the full repository skill set: ```bash npx skills add lllllllama/rigorpilot-skills --all ``` Install only the trusted main entrypoint: ```bash npx skills add lllllllama/rigorpilot-skills --skill ai-research-reproduction ``` Install only the exploratory main entrypoint: ```bash npx skills add lllllllama/rigorpilot-skills --skill ai-research-explore ``` If you only want to get started quickly, stop here. Claude Code can auto-invoke these skills when the descriptions match, or you can call them directly with commands such as `/ai-research-reproduction`, `/ai-research-explore`, and `/safe-debug`. Project-scoped Claude Code slash commands currently ship for: - `/ai-research-reproduction` - `/ai-research-explore` - `/analyze-project` - `/safe-debug` ### Advanced: local clone installs Use the Python installer only if you are developing locally, need a project-scoped install, or want to target neutral Agent Skills, Codex, or Claude Code directories manually.
Show local install commands Install from a local clone into a neutral Agent Skills directory: ```bash python scripts/install_skills.py --client agents --target "$HOME/.agents/skills" --force ``` Install into a project-scoped neutral Agent Skills directory: ```bash python scripts/install_skills.py --client agents --target ./.agents/skills --force ``` Install with the default neutral target: ```bash python scripts/install_skills.py --force ``` Install the full repository skill set in Codex: ```bash npx skills add lllllllama/rigorpilot-skills --all ``` Install only the trusted reproduction orchestrator in Codex: ```bash npx skills add lllllllama/rigorpilot-skills --skill ai-research-reproduction ``` Legacy GitHub source fallback, if the new slug is not yet available in your environment: ```bash npx skills add lllllllama/ai-paper-reproduction-skills --all ``` Install from a local clone into Codex: ```bash python scripts/install_skills.py --client codex --target "$HOME/.codex/skills" --force ``` Install from a local clone into Claude Code: ```bash python scripts/install_skills.py --client claude --target "$HOME/.claude/skills" --force ``` Install into a project-scoped Claude Code skills directory: ```bash python scripts/install_skills.py --client claude --target ./.claude/skills --force ``` PowerShell note: - In Windows PowerShell, the same commands work as written above. - If you prefer explicit Windows-style paths, replace `$HOME/.codex/skills` with something like `$env:USERPROFILE\\.codex\\skills`.
## 🎯 Choose an Entry Point RigorPilot modes map to the current compatible skill slugs: | If you want to... | RigorPilot mode | Current skill slug | |---|---|---| | Reproduce a deep learning repository from README commands | Reproduce | `ai-research-reproduction` | | Explore meaningful and potentially novel ideas on top of current research | Explore | `ai-research-explore` | | Improve a baseline while preserving comparability | Improve | `ai-research-explore`, `explore-code`, `explore-run` | | Audit changes, scientific meaning, and comparability | Audit | `analyze-project`, `safe-debug`, generated reports | | Analyze repository structure without editing | Analyze | `analyze-project` | | Prepare environment, datasets, weights, and cache assumptions | Setup | `env-and-assets-bootstrap` | | Run documented evaluation or inference conservatively | Run | `minimal-run-and-audit` | | Start or verify training conservatively | Train | `run-train` | | Debug a failure safely | Debug | `safe-debug` | Bundled helper skills: - `repo-intake-and-plan` - `paper-context-resolver` ## 🛣️ Lane Model ### 🔒 Trusted Lane Use the trusted lane for reproduction, setup, analysis, bounded execution, training verification, and debugging. - Primary end-to-end orchestrator: `ai-research-reproduction` - Output directories: `repro_outputs/`, `train_outputs/`, `analysis_outputs/`, `debug_outputs/` - Default stance: preserve scientific meaning, minimize semantic changes, surface assumptions and blockers ### 🧪 Explore Lane Use the explore lane only when the researcher explicitly authorizes candidate-only exploratory work. - Primary end-to-end orchestrator: `ai-research-explore` - Narrow leaf skills: `explore-code`, `explore-run` - Output directory: `explore_outputs/` - Key anchor: `current_research` `current_research` should be a durable reference such as a branch, commit, checkpoint, run record, or already-trained local model state. It does not imply a trusted baseline; it is the context the exploration branches from. ### 🧰 Helper Lane Helpers are intentionally narrow and should usually be orchestrator-invoked rather than used as the first entry point. ## 🔗 Client Compatibility `SKILL.md` is the canonical cross-client contract in this repository. - Required for portability: `SKILL.md`, repository-local `scripts/`, and `references/` - Optional Codex UI metadata: `agents/openai.yaml` - Optional Claude Code project entrypoints: `.claude/commands/*.md` - Not allowed: making skill behavior depend on a client-specific metadata file See [references/client-compatibility-policy.md](references/client-compatibility-policy.md). ## 🔁 Lifecycle View The repository follows a lifecycle-oriented routing model: ```mermaid flowchart LR A[Understand] --> B[Reproduce] B --> C[Set up] C --> D[Run or train] D --> E[Debug] E --> F[Report] B -. explicit only .-> G[Explore] G --> H[Rank candidates] H --> F ``` This lifecycle is intentionally shallow. It helps the agent choose the right lane and evidence target without forcing a fixed implementation sequence inside each repository. ## 🗺️ Routing Overview ```mermaid flowchart TD A[User request] --> B{Explicit candidate-only exploration?} B -- No --> C[Trusted lane] B -- Yes --> D[Explore lane] C --> C1[ai-research-reproduction] C --> C2[analyze-project] C --> C3[env-and-assets-bootstrap] C --> C4[minimal-run-and-audit] C --> C5[run-train] C --> C6[safe-debug] D --> D1[ai-research-explore] D --> D2[explore-code] D --> D3[explore-run] C1 -. helper .-> H1[repo-intake-and-plan] C1 -. helper .-> H2[paper-context-resolver] ``` ## 🧠 RigorPilot Explore Flow `ai-research-explore` is the RigorPilot Explore entrypoint when the researcher has already frozen the task family, dataset, evaluation method, and provided SOTA references, then explicitly authorizes candidate-only exploration on top of `current_research`. In RigorPilot terms, this is meaningful and potentially novel candidate work, not verified novelty. ```mermaid flowchart LR A[current_research + frozen campaign] --> B[Outer loop:
understand, source, gate] B --> C{candidate worth trying?} C -- No --> D[Stop with blocker or checkpoint] C -- Yes --> E[Inner loop:
bounded change or run] E --> F[Smoke and evidence] F --> G[Rank candidate] G --> B G --> H[explore_outputs
candidate-only summary] ``` Current RigorPilot implementation highlights: - Researcher ideas are preserved, then optionally expanded with bounded synthesized or hybrid seed ideas in `analysis_outputs/IDEA_SEEDS.json`. - Idea ranking uses hard gates plus explicit weighted breakdowns in `analysis_outputs/IDEA_SCORES.json`. - Selected ideas are decomposed into atomic academic concepts in `analysis_outputs/ATOMIC_IDEA_MAP.md` and `analysis_outputs/ATOMIC_IDEA_MAP.json`. - Implementation fidelity distinguishes planned, heuristic, and observed implementation evidence in `analysis_outputs/IMPLEMENTATION_FIDELITY.md` and `analysis_outputs/IMPLEMENTATION_FIDELITY.json`. - Executor-observed evidence now comes from emitted `changed_files`, `new_files`, `deleted_files`, and `touched_paths` rather than planned target placeholders. The two-loop rhythm is a guide, not a never-stop autonomous agent. RigorPilot Explore stops at explicit blockers, unclear scientific meaning, exhausted budget, missing anchors, or human checkpoints. The explore lane must not claim trusted reproduction success, global benchmark completeness, or verified novelty. ## 📦 Public Skill Matrix | Lane | Skill | Purpose | |---|---|---| | Trusted | `ai-research-reproduction` | End-to-end README-first reproduction orchestrator | | Trusted | `env-and-assets-bootstrap` | Conservative environment, dataset, checkpoint, and cache planning | | Trusted | `minimal-run-and-audit` | Trusted inference, evaluation, smoke, and sanity execution | | Trusted | `analyze-project` | Read-only project analysis, model mapping, and risk surfacing | | Trusted | `run-train` | Training startup verification, resume handling, bounded monitoring, and training records | | Trusted | `safe-debug` | Research-safe debugging: analyze first, patch only after approval | | Explore | `ai-research-explore` | RigorPilot Explore orchestration on top of `current_research` with repo understanding, idea gating, and governed experiments | | Explore | `explore-code` | Exploratory code adaptation, transplant, and stitching on isolated branches | | Explore | `explore-run` | Small-subset probes, short-cycle trials, and ranked exploratory runs | | Helper | `repo-intake-and-plan` | Narrow helper for repo scanning and README command extraction | | Helper | `paper-context-resolver` | Narrow helper for README-paper gap resolution | ## 🧪 Testing Coverage Map This repository does not publish a single line-coverage percentage in the README. Instead, it documents the regression surface that is currently covered by repository tests. | Coverage area | Current scope | Representative checks | |---|---|---| | Registry, installation, and wrappers | File-level integrity, install targets, Claude wrappers, README routing | `test_skill_registry.py`, `test_install_targets.py`, `test_claude_command_wrappers.py`, `test_readme_selection.py` | | Skill principles and concision | Shared operating principles, lifecycle docs, and compact main entrypoints | `test_operating_principles_structure.py` | | Trusted lane rendering and routing | Reproduction, training, analysis, debug, lane routing | `test_output_rendering.py`, `test_train_output_rendering.py`, `test_analysis_output_rendering.py`, `test_safe_debug_output_rendering.py`, `test_training_lane_routing.py` | | Explore lane orchestration | Dry run, campaign flow, checkpoint, abandon path, artifact consistency, execution feasibility | `test_research_explore_dry_run.py`, `test_research_explore_campaign_flow.py`, `test_research_explore_campaign_checkpoint.py`, `test_research_explore_campaign_abandon.py`, `test_research_explore_artifact_consistency.py` | | Explore idea and implementation contracts | Idea seeds, atomic decomposition, implementation fidelity, contract shape | `test_idea_seed_generation.py`, `test_atomic_idea_decomposition.py`, `test_implementation_fidelity.py`, `test_research_explore_contracts.py` | | Explore execution evidence | Training and non-training executor evidence propagation | `test_research_explore_variant_execution.py`, `test_research_explore_nontraining_execution.py` | | Research lookup | Provider resolution, cache, inventory rendering, repo extractors, evidence layering | `test_research_lookup_arxiv_provider.py`, `test_research_lookup_repo_extractor.py`, `test_research_lookup_inventory_rendering.py`, `test_research_lookup_evidence_layers.py` | Coverage notes: - `scripts/validate_repo.py` is still the fast file-level validator. - Deeper behavior contracts are primarily guarded by the explore and rendering regression tests above. - GitHub Actions validates the repository on `ubuntu-latest`, `macos-latest`, and `windows-latest`. ## 📁 Output Directories | Directory | Purpose | |---|---| | `repro_outputs/` | Trusted reproduction bundle | | `train_outputs/` | Trusted training execution bundle | | `analysis_outputs/` | Read-only project analysis plus research map, change map, eval contract, source inventory/support, improvement bank, idea cards, idea seeds, atomic idea map, implementation fidelity, mapping, and resource plan | | `debug_outputs/` | Safe debug diagnosis and patch plan | | `sources/` | Free-first research lookup records with `sources/records/`, stable names, bounded provider resolution, repo-local extraction, and an auditable index | | `explore_outputs/` | RigorPilot Explore changeset, idea gate, experiment plan, experiment manifest, scientific changelog, comparability report, split static/runtime smoke reporting, ledger, and ranked run summary | ## Suggested Research Evidence Two evidence artifacts are now generated by the standardized trusted and explore writers; the remaining names are future-compatible evidence concepts: | Artifact | Meaning | |---|---| | `SCIENTIFIC_CHANGELOG.md` | Generated now. Records what changed, why it changed, whether it affects scientific meaning, and whether it remains comparable. | | `COMPARABILITY_REPORT.md` | Generated now. Explains whether results can still be compared to README, paper, baseline, or SOTA references. | | `REPRODUCIBILITY_NOTES.md` | Records commands, configs, seeds, checkpoints, datasets, environment assumptions, and known gaps. | | `NOVELTY_CLAIM.md` | States possible novelty as a hypothesis, supporting evidence, missing evidence, limitations, and required ablations. | | `ABLATION_PLAN.md` | Describes what needs to be isolated to validate the candidate change. | | `EXPERIMENT_LEDGER.md` | Records runs, metrics, commands, artifacts, changed files, and evidence status. | Existing outputs such as `analysis_outputs/`, `sources/`, `explore_outputs/`, `repro_outputs/`, `train_outputs/`, and `debug_outputs/` remain compatible. RigorPilot may gradually map more existing artifacts to these research evidence concepts. ## 🧩 Campaign Inputs `ai-research-explore` still accepts a plain `variant_spec.json`, but the preferred input for the third scenario is `research_campaign.json` or `research_campaign.yaml`. The durable campaign core is: - `current_research` - `task_family` - `dataset` - `benchmark` - `evaluation_source` - `sota_reference` - `compute_budget` `candidate_ideas` and `variant_spec` are useful, but they are not required in every campaign. `ai-research-explore` preserves researcher ideas and may add a small number of bounded synthesized or hybrid seed ideas for search-space expansion. Generated seeds stay bound to `current_research`, `task_family`, `dataset`, and the frozen `evaluation_source`. Optional campaign blocks: - `research_lookup` - `idea_policy` - `idea_generation` - `source_constraints` - `feasibility_policy` See [skills/ai-research-explore/references/research-campaign-spec.md](skills/ai-research-explore/references/research-campaign-spec.md). ## 💬 Example Prompts **Trusted reproduction** ```text Use ai-research-reproduction on this deep learning research repo. Stay README-first, prefer documented inference or evaluation, avoid unnecessary repo changes, and write outputs to repro_outputs/. ``` **Current-research exploration** ```text Use ai-research-explore on top of current_research improved-model@branch. Work on an isolated branch, coordinate code and run exploration together, try several variants, and rank candidates in explore_outputs/. ``` **Third-scenario campaign exploration** ```text Use ai-research-explore with research_campaign.json. Treat the provided task family, dataset, evaluation source, and SOTA table as frozen inputs, rank the candidate ideas, keep each candidate single-variable, and write RigorPilot evidence outputs to analysis_outputs/ and explore_outputs/. ``` **Read-only analysis** ```text Use analyze-project on this repo. Read the code, map the model and training entrypoints, and flag suspicious patterns without editing files. ``` **Trusted training** ```text Use run-train on this repo. Run the selected documented training command conservatively for startup verification and write train_outputs/. ``` **Safe debug** ```text Use safe-debug on this traceback. Diagnose the failure first, propose the smallest safe fix, and do not patch until I approve. ``` **Exploratory code only** ```text Use explore-code on an isolated branch. Try a LoRA adaptation for this backbone, keep it exploratory only, and summarize the changes in explore_outputs/. ``` **Exploratory runs only** ```text Use explore-run on an experiment branch. Do a small-subset short-cycle sweep, rank the top runs, and treat the results as candidates only. ``` ## ✅ Local Validation Run the repository checks: ```bash python scripts/validate_repo.py python scripts/test_skill_registry.py python scripts/test_trigger_boundaries.py python scripts/test_operating_principles_structure.py python scripts/test_claude_command_wrappers.py python scripts/test_readme_selection.py ``` Run output and orchestration regressions: ```bash python scripts/test_output_rendering.py python scripts/test_train_output_rendering.py python scripts/test_analysis_output_rendering.py python scripts/test_safe_debug_output_rendering.py python scripts/test_explore_output_rendering.py python scripts/test_explore_variant_matrix.py python scripts/test_atomic_idea_decomposition.py python scripts/test_idea_seed_generation.py python scripts/test_implementation_fidelity.py python scripts/test_research_explore_contracts.py python scripts/test_research_explore_dry_run.py python scripts/test_research_explore_campaign_flow.py python scripts/test_research_explore_campaign_abandon.py python scripts/test_research_explore_campaign_checkpoint.py python scripts/test_research_explore_artifact_consistency.py python scripts/test_research_explore_variant_execution.py python scripts/test_research_explore_nontraining_execution.py python scripts/test_orchestrator_dry_run.py python scripts/test_training_lane_routing.py ``` Run research-lookup regressions: ```bash python scripts/test_research_lookup_arxiv_provider.py python scripts/test_research_lookup_doi_provider.py python scripts/test_research_lookup_github_provider.py python scripts/test_research_lookup_url_provider.py python scripts/test_research_lookup_repo_extractor.py python scripts/test_research_lookup_cache.py python scripts/test_research_lookup_inventory_rendering.py python scripts/test_research_lookup_evidence_layers.py ``` Run setup and installer regressions: ```bash python scripts/test_bootstrap_env.py python scripts/test_install_targets.py python scripts/test_setup_planning.py python scripts/install_skills.py --client agents --target ./tmp/agents-skills --force python scripts/install_skills.py --client codex --target ./tmp/codex-skills --force python scripts/install_skills.py --client claude --target ./tmp/claude-skills --force ``` ## 📚 References - Research rigor principles: [references/research-rigor-principles.md](references/research-rigor-principles.md) - Deep learning experiment principles: [references/deep-learning-experiment-principles.md](references/deep-learning-experiment-principles.md) - Shared operating principles: [references/agent-operating-principles.md](references/agent-operating-principles.md) - Skill registry: [references/skill-registry.json](references/skill-registry.json) - Explore variant spec: [references/explore-variant-spec.md](references/explore-variant-spec.md) - Explore module roadmap: [references/explore-module-roadmap.md](references/explore-module-roadmap.md) - Client compatibility policy: [references/client-compatibility-policy.md](references/client-compatibility-policy.md) - Routing policy: [references/routing-policy.md](references/routing-policy.md) - Trigger boundary policy: [references/trigger-boundary-policy.md](references/trigger-boundary-policy.md) - Branch and commit policy: [references/branch-and-commit-policy.md](references/branch-and-commit-policy.md) - Output contract: [references/output-contract.md](references/output-contract.md) - Research pitfall checklist: [references/research-pitfall-checklist.md](references/research-pitfall-checklist.md) ## ⚠️ Current Limits - `run-train` is a bounded training monitor, not a full long-running scheduler. - Trusted reproduction still avoids silent semantic changes. - Helper skills remain narrow and are not intended to become public catch-all entry points. - Exploratory work must stay isolated from trusted baselines. - `ai-research-explore` is the governed RigorPilot Explore orchestrator, not an open-ended autonomous research agent. ## 🧱 Scope RigorPilot Skills is a research-first skill repository for deep learning experiments, optimized for scientific meaning, comparability, reproducibility, collaborator control, and auditable workflow boundaries. --- *Source: https://skills.yangsir.net/skill/daily-repo-intake-and-plan* *Markdown mirror: https://skills.yangsir.net/api/skill/daily-repo-intake-and-plan/markdown*