nature-paper-card

The saved excerpt is a snapshot from when this skill was listed. The external source remains the complete and most current version.

---
name: nature-paper-card
description: "Build a structured deep-reading Paper Card for one scientific paper, analysing methods, experiment-to-claim evidence, limitations, and research ideas. Use for 论文精读卡、方法拆解、证据链分析; not full-paper bilingual translation or formal peer review."
---

# Nature Paper Card - Router

Use this skill to turn one paper into an evidence-grounded research card, not a translated abstract, generic summary, reviewer report, or publication article.

The skill uses:

- a static core under `static/core/` for principles, workflow, and the fixed output contract;
- one paper-type fragment under `static/fragments/paper_type/`;
- on-demand references for evidence labels, the exact card schema, and research-idea checks.

## Routing protocol

Follow these steps every time.

### 1. Load the manifest and core layer

Read [manifest.yaml](manifest.yaml), then read every file under `always_load`. Do not generate the card from this router alone.

### 2. Establish the source boundary

Identify which material is available:

- full paper with figures and tables;
- paper text without reliable layout;
- abstract or metadata only;
- an existing `nature-reader` artifact with stable source IDs.

Prefer an existing `nature-reader` artifact when supplied. Do not repeat full bilingual translation or figure extraction. If only partial material is available, create a visibly partial card and mark every unsupported section `Not assessable from supplied material`.

For a PDF or `nature-reader` source-map JSON, the bundled script is mandatory.

1. Resolve `SKILL_DIR` as the directory containing this loaded `SKILL.md`.
2. Verify `SKILL_DIR/scripts/prepare_paper.py` exists.
3. Run exactly the bundled script by its resolved path:

```text
python "SKILL_DIR/scripts/prepare_paper.py" INPUT \
  --output WORKDIR/source_bundle.json
```

Add `--render-dir WORKDIR/rendered-pages` when visual page review is needed. Inspect the script exit code and the bundle validation block before drafting.

For source-map input, also inspect `locator_summary` and `unlocated_blocks`. Only records under `pages` have verified positive PDF page locators. Missing or invalid page locators remain in `unlocated_blocks` with an explicit status and must be cited structurally, never as page 1.

Never write inline Python, a temporary extraction script, or a replacement script during a Paper Card run. Never patch the bundled scripts during a normal Paper Card run. Modify these scripts only when the user explicitly asks to develop, debug, or improve the skill itself.

Use this fixed locator state machine:

- `page-grounded`: the bundled script succeeds and validates reliable PDF page indices. Use PDF page plus structural locators. Printed page labels are optional metadata.
- `structure-grounded`: page extraction is unreliable, but reliable sections, figures, tables, equations, source blocks, or full text remain available. Do not emit page-number citations.
- `source-limited`: only an abstract, metadata, or user-provided excerpt is reliable. Do not emit page-number citations or infer unseen evidence.

If preparation fails, record the failure. Prefer an existing `nature-reader` source map or the environment PDF/OCR capability, but do not create a replacement script. Then enter the strongest supported fallback mode.

### 3. Classify the paper type

Use the manifest to choose one primary `paper_type` and, only for a genuinely hybrid paper, one secondary contribution lens:

- `methods`
- `discovery`
- `resource`
- `clinical`
- `materials`
- `review`

Load the primary fragment and no more than one secondary fragment. Classify by the paper's argument and evidence structure, not merely its discipline. State both selections before analysis. For example, an algorithm paper that also introduces a substantial dataset may use `methods` as the primary lens and `resource` as the secondary lens.

### 4. Build the evidence base before drafting

Build an internal evidence inventory before draf