Inno Code Survey

inno-code-survey

Canonical Summary

Acquires missing code repositories for the selected idea (Phase A) and conducts comprehensive code survey mapping academic concepts to implementations (Phase B). Outputs acquired_code_repos, updated_prepare_res, and model_survey for downst...

Trigger Rules

Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.

Resource Use Rules

• Read from references/ only when the current task needs the extra detail.
• Treat scripts/ as optional helpers. Run them only when their dependencies are available, keep outputs in the project workspace, and explain a manual fallback if execution is blocked.

Execution Contract

• Resolve every relative path from this skill directory first.
• Prefer inspection before mutation when invoking bundled scripts.
• If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
• Do not write generated artifacts back into the skill directory; save them inside the active project workspace.

Upstream Instructions

Inno Code Survey (Repo Acquisition + Code Survey)

Merges _acquire_missing_repos, _update_prepare_res_with_new_repos, and _conduct_code_survey from run_infer_idea_ours.py (lines 639–828, 1038–1052) into a single two-phase skill.

Directory structure

skills/inno-code-survey/
├── SKILL.md                                          ← this file
├── prompts/
│   ├── build_repo_acquisition_query.md               ← Phase A query template
│   └── build_code_survey_query.md                    ← Phase B query template
├── references/
│   ├── repo_acquisition_agent.md                     ← Phase A agent system prompt & tools
│   └── code_survey_agent.md                          ← Phase B agent system prompt & tools
└── scripts/
    └── github_search_clone.py                        ← GitHub search + clone helper

Path conventions

All file paths use semantic directory names under the project root:

Inputs

These are aligned with outputs from inno-idea-generation and inno-prepare-resources:

Outputs

Phase A — Repo Acquisition

Full template & parameter docs: prompts/build_repo_acquisition_query.md Agent system prompt & tools: references/repo_acquisition_agent.md

Maps to _acquire_missing_repos (lines 745–792) + _update_prepare_res_with_new_repos (lines 639–686).

Step A1: Analyze the selected idea and identify gaps

Read selected_idea and identify 2–3 missing technical components — novel or specialized parts that are likely NOT in the standard repos already present in Experiment/code_references/.

Step A2: Search GitHub using the "Cascade" strategy

For each missing component, perform 6 distinct queries using progressive decomposition:

1. Level 1 (Specific): Search for the exact mechanism name
2. Level 2 (Broad): Strip context adjectives, search core technique
3. Level 3 (Atomic): Search for 3 base mathematical operators

Use the helper script or GitHub API directly:

# Option 1: Helper script
python scripts/github_search_clone.py --query "sinkhorn attention pytorch" --limit 5 --date-limit 2025-12-31

# Option 2: Direct GitHub API via curl
curl -s "https://api.github.com/search/repositories?q=sinkhorn+attention&per_page=5" \
  -H "Accept: application/vnd.github.v3+json"

Step A3: Clone selected repos

Clone the best candidate for each gap into Experiment/code_references/:

GIT_TERMINAL_PROMPT=0 git clone --depth 1 <clone_url> Experiment/code_references/<repo_name>

Step A4: Verify each clone

For each cloned repo:

1. Read README.md: cat Experiment/code_references/<repo_name>/README.md
2. Check language and domain relevance
3. Reject repos that don't match (wrong domain, empty, HTML-only)

Step A5: Build acquired_code_repos and update prepare_res

1. Build acquired_code_repos dict from verified clones:

   {
     "repo_name_1": "Experiment/code_references/repo_name_1",
     "repo_name_2": "Experiment/code_references/repo_name_2"
   }

2. Set context_variables["acquired_code_repos"] = acquired_code_repos
3. Parse prepare_res JSON, ensure reference_codebases and reference_paths arrays exist
4. For each entry in acquired_code_repos, if path not already in reference_paths:
   • Append repo name to reference_codebases
   • Append repo path to reference_paths
5. Serialize back to JSON as updated_prepare_res

Step A6: Save Phase A cache

1. Build extra_repo_info string:

   - Name: <name1> | Path: <path1>
   - Name: <name2> | Path: <path2>

   (Empty string if no repos acquired)

2. Write Experiment/code_references/logs/repo_acquisition_agent.json:

   {
     "context_variables": {
       "code_references_path": "<instance.Experiment.code_references if absolute (Dr. Claw), else path.join(project_path, ...)>",
       "references_path": "<instance.Ideation.references if absolute (Dr. Claw), else path.join(project_path, ...)>",
       "date_limit": "YYYY-MM-DD",
       "prepare_result": { ... },
       "acquired_code_repos": {
         "<name>": "<path>",
         ...
       },
       "updated_prepare_res": "<JSON string of updated prepare_res>"
     }
   }