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Loads the evaluation instance, searches GitHub for related repositories, builds a dataset description, queries the Prepare Agent for reference codebases, and downloads arXiv paper sources.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/LigphiDonk/Oh-my--paper --skill inno-prepare-resourcesコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Search traceable academic papers, download legally accessible PDFs from arXiv and open-access sources, convert PDFs or page images to Markdown with a PaddleOCR layout-parsing API (or local pdfminer fallback), and organize the results into an AI-readable literature library. Use when Claude Code needs to build a paper corpus, batch OCR PDFs to Markdown, ingest real literature into a knowledge base, fetch arXiv or Hugging Face paper leads, or turn a directory of papers into structured Markdown plus metadata.
Delegate complex coding tasks to Claude Code CLI
Delegate coding tasks to OpenAI Codex CLI
SOC 職業分類に基づく
SKILL.md を表示中
| id | inno-prepare-resources |
| name | inno-prepare-resources |
| version | 1.0.0 |
| description | Loads the evaluation instance, searches GitHub for related repositories, builds a dataset description, queries the Prepare Agent for reference codebases, and downloads arXiv paper sources. |
| stages | ["ideation"] |
| tools | ["read_file","search_project","write_file"] |
| summary | Loads the evaluation instance, searches GitHub for related repositories, builds a dataset description, queries the Prepare Agent for reference codebases, and downloads arXiv paper sources. Covers both Idea mode and Plan mode (the only diff... |
| primaryIntent | data |
| intents | ["data","research"] |
| capabilities | ["research-planning","data-processing"] |
| domains | ["general"] |
| keywords | ["inno-prepare-resources","resource prep","research-planning","data-processing","inno","prepare","resources","loads","evaluation","instance","searches","github"] |
| source | builtin |
| status | verified |
| upstream | {"repo":"dr-claw","path":"skills/inno-prepare-resources","revision":"8322dc4ef575affaa374aa7922c0a0971c6db7d7"} |
| resourceFlags | {"hasReferences":false,"hasScripts":false,"hasTemplates":false,"hasAssets":false,"referenceCount":0,"scriptCount":0,"templateCount":0,"assetCount":0,"optionalScripts":false} |
Loads the evaluation instance, searches GitHub for related repositories, builds a dataset description, queries the Prepare Agent for reference codebases, and downloads arXiv paper sources. Covers both Idea mode and Plan mode (the only diff...
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.
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 |
| 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) |
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.jsonis written by the orchestrator before this skill runs — do not overwrite it.
tools/*.json)Each tool output file records the function call arguments and result:
{
"name": "<tool_name>",
"args": { ... },
"result": <result_value>
}
github_search.json — written after Step 2:
{
"name": "github_search",
"args": {
"metadata": {
"source_papers": [ ... ],
"task_instructions": "...",
"date_limit": "YYYY-MM-DD"
}
},
"result": "<concatenated github_result string>"
}
download_arxiv_source_by_title.json — written after Step 6:
{
"name": "download_arxiv_source_by_title",
"args": {
"paper_list": ["paper title 1", "paper title 2"],
"references_path": "<instance.Ideation.references if absolute, else path.join(project_path, instance.Ideation.references)>"
},
"result": "<download result log string>"
}
agents/*.json)Each agent output file records the final context variables (no conversation messages):
{
"context_variables": {
"references_path": "<use instance.Ideation.references as-is if absolute, else path.join(project_path, ...)>",
"code_references_path": "<use instance.Experiment.code_references as-is if absolute, else path.join(project_path, ...)>",
"datasets_path": "<use instance.Experiment.datasets as-is if absolute, else path.join(project_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.
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, usagetask_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 retrievedWrite date_limit into context_variables["date_limit"].
Note:
Ideation/references/logs/load_instance.jsonshould 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
urlor an emptysource_paperslist, use a sensible defaultdate_limitand continue — do not raise an error.
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=<paper_title>&per_page=10&page=1
From each result item, extract:
{owner}/{repo}html_urlFormat 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_papersis empty: use keywords extracted fromtask_instructionsas the search query instead, and perform a single GitHub search call to find relevant repositories.
Save → Ideation/references/logs/github_search.json
Choose one of the following strategies (in priority order):
Orchestrator override — if dataset_description was passed as input, use it directly.
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 aboutDATASET — dataset files, structure, and loading instructionsBASELINE — representative baseline methodsCOMPARISON — performance comparison tableEVALUATION — evaluation metrics and scoring functionsREF — additional references and notesThe 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}
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.
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)
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.
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
load_instance called; date_limit written to context_variables (default used if unavailable)github_search completed; result saved → Ideation/references/logs/github_search.jsondataset_description built (from orchestrator override, built-in metaprompt, or manual construction)Ideation/references/logs/prepare_agent.jsonreference_papers extracted from Prepare Agent output; fallback to source papers if emptyIdeation/references/papers/; result saved → Ideation/references/logs/download_arxiv_source_by_title.jsonIdeation/references/logs/