inno-code-survey

Files (6)

• SKILL.md
• build_code_survey_query.md · prompts/
• build_repo_acquisition_query.md · prompts/
• code_survey_agent.md · references/
• repo_acquisition_agent.md · references/
• github_search_clone.py · scripts/

SKILL.md (first lines)

# 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](workspace/medical/Medical_ai_scientist_idea/research_agent/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:

| Path | Contents |
|------|----------|
| `Ideation/references/papers/` | Downloaded arXiv LaTeX sources (`.tex`, `.txt`, `.md`) |
| `Experiment/code_references/<repo_name>/` | Cloned GitHub repositories |
| `Experiment/code_references/model_survey.md` | Code survey implementation report |
| `Experiment/code_references/logs/` | Phase A & B agent cache files |

## Inputs

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

| Input | Source | Description |
|-------|--------|-------------|
| `selected_idea` | `Ideation/ideas/selected_idea.txt` or `final_selected_idea_data` | The finalized selected idea (full markdown) |
| `download_res` | `inno-prepare-resources` output | Result log from downloading arXiv paper sources |
| `prepare_res` | `inno-prepare-resources` output (JSON) | Contains `reference_codebases` and `reference_paths` |
| `context_variables` | Shared context dict | Accumulated pipeline context |
| `instance.json` | `<project_path>/instance.json` | Paths are **absolute** when created by Dr. Claw (`Experiment.code_references`, `Ideation.references`); use as-is or resolve with `path.join(project_path, value)` if relative. Also `date_limit` from context. |

## Outputs

| Output | Description | Consumer |
|--------|-------------|----------|
| `acquired_code_repos` | Dict of `{name: path}` for newly cloned repos | Phase B, cache |
| `updated_prepare_res` | `prepare_res` JSON with new repos merged into `reference_codebases` / `reference_paths` | Downstream pipeline |
| `extra_repo_info` | Formatted string listing acquired repos | Phase B query |
| `model_survey` | Comprehensive code survey implementation report | `inno-experiment-dev` |

---

## Phase A — Repo Acquisition

> Full template & parameter docs: [prompts/build_repo_acquisition_query.md](prompts/build_repo_acquisition_query.md)
> Agent system prompt & tools: [references/repo_acquisition_agent.md](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:

```bash
# 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/`:

```bash
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`

From LigphiDonk/Oh-my--paper, MIT.

More from LigphiDonk/Oh-my--paper