--- name: inno-prepare-resources source: https://app.decimal.ai/s/inno-prepare-resources@1/SKILL.md source_sha256: 788c099549cd --- # Inno Prepare Resources ## Inputs Read from **`instance.json`**. Path values are **absolute** when the project is created by Dr. Claw; use as-is. If relative (e.g. hand-edited), resolve with `path.join(project_path, value)`. | Parameter | Required | Description | |------------------------|----------|-------------| | `instance` | Yes | Path to the instance JSON file (absolute in Dr. Claw). Use as-is to read the file. File contains `source_papers`, `task1`/`task2`, etc. | | `task_level` | Yes | Which task field to read from the instance — `"task1"` (Plan) or `"task2"` (Idea) | | `Ideation.references` | Yes | Path to Ideation references dir (absolute in Dr. Claw) — for downloaded papers and prepare logs | | `Experiment.code_references` | Yes | Path (absolute in Dr. Claw) — for cloned repos | | `Experiment.datasets` | Yes | Path (absolute in Dr. Claw) — for dataset files | | `category` | Yes | Research category tag (e.g. `nlp_qa`, `gnn`, `recommendation`). Used to locate the built-in dataset metaprompt | | `references` | Yes | A pre-formatted string listing all source papers from the instance | | `context_variables` | Yes | Shared context dictionary; this step will write `date_limit` into it | | `ideas` | No | Full innovative-idea / plan text. **Provide only in Plan mode** — when present the Prepare Agent query includes the ideas for more targeted repo selection | | `dataset_description` | No | Pre-built dataset description from the orchestrator (for custom / user-provided datasets). When provided, skip the metaprompt import in Step 3 | ## Outputs | Output | Description | |------------------------|-------------| | `prepare_res` | Full text response from the Prepare Agent (contains selected reference repositories and reasoning) | | `download_res` | Result log from downloading arXiv paper sources to local disk | | `dataset_description` | Composed prompt string describing the datasets, baselines, comparisons, and evaluation metrics | | `data_module` | The imported metaprompt module object (Idea mode). In Plan mode this is not returned | | `context_variables` | Updated with `date_limit` (str, YYYY-MM-DD) | ## Cache file outputs Every intermediate result must be persisted as a JSON file under `Ideation/references/logs/`. The directory layout follows: ``` Ideation/references/logs/ ├── load_instance.json ← written by orchestrator ├── github_search.json ├── download_arxiv_source_by_title.json └── prepare_agent.json ``` > `Ideation/references/logs/load_instance.json` is written by the **orchestrator** before this skill runs — do not overwrite it. ### Tool cache format (`tools/*.json`) Each tool output file records the function call arguments and result: ```json { "name": "", "args": { ... }, "result": } ``` **`github_search.json`** — written after Step 2: ```json { "name": "github_search", "args": { "metadata": { "source_papers": [ ... ], "task_instructions": "...", "date_limit": "YYYY-MM-DD" } }, "result": "" } ``` **`download_arxiv_source_by_title.json`** — written after Step 6: ```json { "name": "download_arxiv_source_by_title", "args": { "paper_list": ["paper title 1", "paper title 2"], "references_path": "" }, "result": "" } ``` ### Agent cache format (`agents/*.json`) Each agent output file records the final context variables (no conversation messages): ```json { "context_variables": { "references_path": "", "code_references_path": "", "datasets_path": "", "date_limit": "YYYY-MM-DD", "prepare_result": { "reference_codebases": ["repo1", "repo2"], "reference_paths": ["Experiment/code_references/repo1", "Experiment/code_references/repo2"], "reference_papers": ["paper title 1", "paper title 2"] } } } ``` **`prepare_agent.json`** — written after Step 4–5. Contains the final `context_variables` with `prepare_result` holding `reference_codebases`, `reference_paths`, and `reference_papers`. ## Step-by-step Instructions ### Step 1 — Load the evaluation instance Call `load_instance(instance.instance, task_level)` — when created by Dr. Claw, `instance.instance` is already absolute; otherwise resolve with `path.join(project_path, instance.instance)`. This reads the instance JSON and returns an **EvalMetadata** object containing: - `source_papers` — list of dicts, each with `reference`, `rank`, `type`, `justification`, `usage` - `task_instructions` — the task description text (from the field named by `task_level`) - `date_limit` — the publication date of the target paper (fetched from arXiv via the instance `url`); defaults to `"2024-01-01"` if metadata cannot be retrieved Write `date_limit` into `context_variables["date_limit"]`. > **Note**: `Ideation/references/logs/load_instance.json` should already exist — it was written by the orchestrator. If not, write it now following the tool cache format. > **Graceful handling**: If the instance JSON was constructed by the orchestrator and has no `url` or an empty `source_papers` list, use a sensible default `date_limit` and continue — do not raise an error. ### Step 2 — Search GitHub for related repositories Call `github_search(metadata)`. Iterate over every entry in `metadata["source_papers"]`. For each paper, use its `reference` (title) as the search query and call the GitHub Search Repositories API: ``` GET https://api.github.com/search/repositories?q=&per_page=10&page=1 ``` From each result item, extract: - **name**: `{owner}/{repo}` - **description**: repository description - **link**: `html_url` Format each paper's results as a human-readable block: ``` Here are some of the repositories I found on GitHub: Name: owner/repo Description: ... Link: https://github.com/owner/repo ``` Concatenate all papers' results into a single `github_result` string, using a `******************************` separator between papers. **Rate-limit handling**: wait ~2 seconds between consecutive GitHub API calls to avoid HTTP 403 throttling. The last paper does not need a delay. > **Fallback when `source_papers` is empty**: use keywords extracted from `task_instructions` as the search query instead, and perform a single GitHub search call to find relevant repositories. **Save** → `Ideation/references/logs/github_search.json` ### Step 3 — Build the dataset description Choose one of the following strategies (in priority order): 1. **Orchestrator override** — if `dataset_description` was passed as input, use it directly. 2. **Built-in metaprompt** — if `category` maps to an existing metaprompt module, import it and compose the description from its five fields: - `TASK` — what the task is about - `DATASET` — dataset files, structure, and loading instructions - `BASELINE` — representative baseline methods - `COMPARISON` — performance comparison table - `EVALUATION` — evaluation metrics and scoring functions - `REF` — additional references and notes The composed description follows this template: ``` You should select SEVERAL datasets as experimental datasets from the following description: {DATASET} We have already selected the following baselines for these datasets: {BASELINE} The performance comparison of these datasets: {COMPARISON} And the evaluation metrics are: {EVALUATION} {REF} ``` 3. **Manual / minimal** — if neither of the above is available, construct a minimal description from what is known about the task (e.g. from `task_instructions`), or ask the user for additional information. Also retain the `data_module` object (the imported metaprompt module) for later use in Idea mode. ### Step 4 — Query the Prepare Agent Build the query depending on mode: - **Idea mode** (no `ideas` provided): ``` You are given a list of papers, searching results of the papers on GitHub. List of papers: {references} Searching results of the papers on GitHub: {github_result} Your task is to choose at least 5 repositories as the reference codebases. Note that this time there is no innovative ideas, you should choose the most valuable repositories as the reference codebases. ``` - **Plan mode** (`ideas` provided): ``` You are given a list of papers, searching results of the papers on GitHub, and innovative ideas according to the papers. List of papers: {references} Searching results of the papers on GitHub: {github_result} innovative ideas: {ideas} Your task is to choose at least 5 repositories as the reference codebases. ``` Send the query to the **Prepare Agent** and record the full response as `prepare_res`. **Save** → `Ideation/references/logs/prepare_agent.json` (final context_variables only, no messages) ### Step 5 — Extract reference paper list Parse `prepare_res` to extract a JSON object containing `"reference_papers"` (a list of paper title strings the agent selected). Use bracket-matching JSON extraction — find the first complete `{…}` in the text, parse it, and read `reference_papers`. **Fallback**: if `reference_papers` is empty (e.g. the agent found no GitHub repos and returned nothing), fall back to the original `source_papers` titles from the instance metadata so that paper download can still proceed. ### Step 6 — Download arXiv paper sources Call `download_arxiv_source_by_title(paper_list, references_path)` where `references_path` is `instance.Ideation.references` (absolute in Dr. Claw) or `path.join(project_path, instance.Ideation.references)` if relative. This searches arXiv for each paper title, downloads the LaTeX / source archive, and extracts it into `Ideation/references/papers/`. Record the result log as `download_res`. **Save** → `Ideation/references/logs/download_arxiv_source_by_title.json` ## Checklist - [ ] `load_instance` called; `date_limit` written to `context_variables` (default used if unavailable) - [ ] `github_search` completed; result saved → `Ideation/references/logs/github_search.json` - [ ] `dataset_description` built (from orchestrator override, built-in metaprompt, or manual construction) - [ ] Prepare Agent queried; conversation saved → `Ideation/references/logs/prepare_agent.json` - [ ] `reference_papers` extracted from Prepare Agent output; fallback to source papers if empty - [ ] arXiv paper sources downloaded to `Ideation/references/papers/`; result saved → `Ideation/references/logs/download_arxiv_source_by_title.json` - [ ] All cache files written under `Ideation/references/logs/`