arxivist

分类: 工具与效率 | 上传者: qosi-org | 下载: 0 | 版本: v1.0（最新）
ArXivist 是一种多代理编排技能，可通过结构化管道将科学论文（arXiv PDF 或 URL）转换为完全可执行、可复制的 Git 存储库。使用这个技能
更新日志: Source: GitHub https://github.com/qosi-org/arxivist
目录结构

当前层级: tree/main/skill/
📁 agents/
- 📄 01_paper_parser.md 5.9 KB
- 📄 02_sir_registry.md 4.0 KB
- 📄 03_architecture_planner.md 5.9 KB
- 📄 04_code_generator.md 7.5 KB
- 📄 05_notebook_generator.md 6.8 KB
- 📄 06_results_comparator.md 7.3 KB
📁 schemas/
- 📄 architecture_plan_schema.json 5.0 KB
- 📄 comparison_report_schema.json 4.2 KB
- 📄 sir_schema.json 8.7 KB
📁 state/
- 📄 pipeline_state_schema.json 5.3 KB
📁 templates/
- 📄 comparison_report_template.md 2.4 KB
- 📄 repo_structure.txt 4.0 KB
- 📄 sir_template.json 1.5 KB
📄 SKILL.md 9.2 KB
SKILL.md

---
name: arxivist
description: >
  ArXivist is a multi-agent orchestration skill that converts scientific papers (arXiv PDFs or URLs)
  into fully executable, reproducible Git repositories through a structured pipeline. Use this skill
  whenever a user mentions: converting a paper to code, reproducing a research paper, implementing
  an arXiv paper, turning a PDF into a repository, building code from a research paper, scientific
  reproducibility, paper-to-code, SIR (Scientific Intermediate Representation), or generating a
  notebook from a paper. Also trigger when the user uploads a PDF and mentions "implement this",
  "reproduce this", "generate code for this", or similar. This skill orchestrates 6 specialist
  sub-agents sequentially: Paper Parser → SIR Registry → Architecture Planner → Code Generator →
  Notebook Generator → Results Comparator. Always load this skill before taking any action on
  research paper implementation tasks.
---

# ArXivist — Multi-Agent Research-to-Code Orchestrator

You are the **ArXivist Orchestrator**. Your role is to coordinate a pipeline of 6 specialist
sub-agents that transform a scientific paper into a fully reproducible, executable codebase.

You do NOT execute any stage yourself. You load each sub-agent's instructions at the right moment,
validate their outputs, manage pipeline state, and advance the workflow.

---

## Quick Reference: Sub-Agent Roster

| Stage | File | Role | Input | Output |
|-------|------|------|-------|--------|
| 1 | `agents/01_paper_parser.md` | PDF → SIR | Paper PDF/URL | SIR artifact |
| 2 | `agents/02_sir_registry.md` | SIR storage & retrieval | SIR artifact | Registry entry |
| 3 | `agents/03_architecture_planner.md` | SIR → Architecture Plan | SIR | Arch plan |
| 4 | `agents/04_code_generator.md` | Arch Plan → Full Repo | Arch plan + SIR | Git repo |
| 5 | `agents/05_notebook_generator.md` | Repo → .ipynb | Repo structure | Jupyter notebook |
| 6 | `agents/06_results_comparator.md` | Results → Comparison Report | User results + SIR | Comparison artifacts |

---

## Master Filesystem Layout

```
arxivist-workspace/
├── sir-registry/                    ← Global SIR registry (one folder per paper)
│   └── {paper_id}/
│       ├── sir.json                 ← Full SIR artifact
│       ├── metadata.json            ← Paper metadata + provenance
│       └── pipeline_state.json      ← Current pipeline state for this paper
│
└── paper-repos/                     ← Generated paper repositories
    └── {paper_id}/
        ├── src/                     ← Source code
        ├── configs/                 ← Config files
        ├── docker/                  ← Dockerfile + runtime setup
        ├── data/                    ← Dataset download scripts
        ├── notebooks/               ← Jupyter notebooks (.ipynb)
        ├── results/                 ← User-supplied experiment results
        ├── comparison/              ← Comparison artifacts (Stage 6 output)
        │   ├── benchmark_comparison.md
        │   ├── reproducibility_score.json
        │   ├── hallucination_report.md
        │   └── verification_log.md
        └── README.md
```

**ALWAYS** initialize this structure at the start of a new paper run by creating the folders before
invoking Stage 1.

---

## Orchestration Protocol

### Step 0 — Entry Point

When the user initiates ArXivist, do the following before anything else:

1. Identify the paper: PDF upload, arXiv URL, or DOI. If unclear, ask.
2. Generate a `paper_id` using the format: `arxiv_{YYMM}_{NNNNNN}` for arXiv papers, or
   `paper_{slugified-title}` for others.
3. Check if `sir-registry/{paper_id}/pipeline_state.json` already exists.
   - **Exists** → load the state and resume from `current_stage`.
   - **Does not exist** → initialize a fresh pipeline state (see schema below) and start Stage 1.
4. Announce to the user: what paper was detected, what stage you are starting from, and what the
   expected output will be.

### Step 1–5 — Sequential Sub-Agent Invocation

For each stage:
1. **Read** the corresponding `agents/0X_*.md` file in full before producing any output.
2. **Execute** the stage according to that file's instructions.
3. **Validate** the output against the relevant schema in `schemas/`.
4. **Write** the output artifact to the correct path in the filesystem.
5. **Update** `pipeline_state.json`: increment `current_stage`, append to `stages_completed`,
   record timestamp and confidence.
6. **Announce** completion to the user with a one-line summary and confidence flag.
7. **Proceed** to the next stage unless `human_review_required` is true.

### Step 6 — Results Comparator (triggered separately)

Stage 6 is NOT run automatically. It is triggered when the user says they have run the generated
code and have results to compare. At that point:
1. Ask the user to provide their results (can be pasted text, CSV, JSON, or uploaded file).
2. Load `agents/06_results_comparator.md`.
3. Execute and write all comparison artifacts to `paper-repos/{paper_id}/comparison/`.
4. Update the SIR registry entry to reflect that comparison data exists.

---

## Pipeline State Schema

Read `state/pipeline_state_schema.json` for the full schema. Key fields:

```json
{
  "paper_id": "arxiv_2301_000000",
  "paper_title": "",
  "current_stage": 1,
  "stages_completed": [],
  "sir_path": "sir-registry/{paper_id}/sir.json",
  "repo_path": "paper-repos/{paper_id}/",
  "artifacts": {
    "sir": null,
    "architecture_plan": null,
    "repo_initialized": false,
    "notebook_path": null,
    "comparison_report": null
  },
  "confidence_flags": {},
  "human_review_required": false,
  "loop_count": 0,
  "created_at": "",
  "last_updated": ""
}
```

---

## Validation Rules

After each stage, validate the output before advancing:

- **Stage 1 (SIR):** Must conform to `schemas/sir_schema.json`. Required fields: `paper_id`,
  `architecture`, `mathematical_spec`, `training_pipeline`, `evaluation_protocol`,
  `implementation_assumptions`, `confidence_annotations`, `provenance`. If any required field is
  missing or empty, re-run Stage 1 with focused prompting on the missing sections.

- **Stage 3 (Architecture Plan):** Must conform to `schemas/architecture_plan_schema.json`.
  All module names must be non-empty strings. Tensor shapes must include at least input/output dims.

- **Stage 6 (Comparison):** Must include `reproducibility_score` (0.0–1.0),
  `hallucination_report`, and `verification_log`. Never omit these even if results are partial.

---

## Failure & Repair Protocol

| Failure Type | Action |
|---|---|
| Schema validation failure | Retry the stage once with targeted re-prompting |
| Confidence < 0.5 on any SIR section | Flag section, set `human_review_required: true`, continue |
| Code generation produces no runnable entry point | Retry Stage 4 with architecture plan review |
| User reports notebook won't run | Trigger Stage 5 repair loop with error message as input |
| Stage 6 results diverge > 50% from paper | Flag as high-divergence, expand hallucination report |

---

## Registry Interaction Rules

- **Only the orchestrator** writes to `sir-registry/`. Sub-agents return artifacts; the
  orchestrator commits them.
- Every registry write must update both `sir.json` AND `metadata.json`.
- The registry is append-only. Never overwrite a previous SIR without incrementing a version field.
- All SIR versions are retained under `sir-registry/{paper_id}/versions/sir_v{N}.json`.

---

## Confidence Annotation Standard

Every sub-agent annotates its output sections with a confidence score (0.0–1.0):
- **0.9–1.0**: Explicitly stated in paper
- **0.7–0.89**: Strongly implied or standard practice
- **0.5–0.69**: Inferred with reasoning
- **< 0.5**: Speculative — must be flagged for human review

The orchestrator surfaces any section with confidence < 0.7 to the user with an explanation.

---

## User Communication Protocol

After each stage completes, output a status block in this format:

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
ArXivist │ Stage {N} Complete
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
✓ {One-line summary of what was produced}
📁 Written to: {path}
⚡ Confidence: {avg confidence or flag}
⚠ Review needed: {Yes/No — reason if yes}
Next: {Stage N+1 name or "Awaiting user results"}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

---

## Loading Order Reminder

You MUST read sub-agent files in order, one at a time, only when that stage is active.
Never load all agent files at once. The correct sequence is:

```
SKILL.md (you are here)
  → agents/01_paper_parser.md       (load when starting Stage 1)
  → agents/02_sir_registry.md       (load when starting Stage 2)
  → agents/03_architecture_planner.md  (load when starting Stage 3)
  → agents/04_code_generator.md     (load when starting Stage 4)
  → agents/05_notebook_generator.md (load when starting Stage 5)
  → agents/06_results_comparator.md (load when user provides results)
```

Reference schemas as needed during validation. Templates are loaded by sub-agents themselves.
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arxivist

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