Experiment Data Extraction
One script, one chain: md -> preprocess -> GLM filter -> LLM -> JSON. No manifests, no run state, no monitoring — just extraction. A single model service (Zhipu BigModel, default glm-5.3-flash) powers both the sentence filter and the extraction.
Pre-flight
- Install
requests from requirements.txt.
BIGMODEL_API_KEY must be set — it authenticates BOTH model stages (sentence filter + extraction) as Authorization: Bearer (OPENAI_API_KEY accepted as fallback). Never print key values. No other credential or internal service is used.- Optional overrides (env or flags):
LLM_CHAT_URL — default https://open.bigmodel.cn/api/paas/v4/chat/completions (used by both stages)LLM_MODEL — default glm-5.3-flash (fast variant, used by both stages; glm-5.3 / glm-5.2 also valid)
- Input is the paper's markdown: a LOCAL file (
--md/--md-dir), or paper_id,md_url CSV rows (--csv, md downloaded to --md-cache, cached across re-runs). Local mode: file stem = paper_id.
Run
# single paper
python3 extract_experiments.py --md /path/to/paper.md -o out.json
# batch: one md per paper, named <paper_id>.md
python3 extract_experiments.py --md-dir md_papers/ -o-dir out_json/
# batch from paper_id + md_url (CSV: header paper_id,md_url; md downloaded to md_cache/)
python3 extract_experiments.py --csv papers.csv --md-cache md_cache/ -o-dir out_json/
Per-paper failures don't stop the batch; exit code 2 means at least one failed. CSV downloads are cached — re-runs skip already-downloaded papers.
Output
One JSON per paper: paper_id, paper_title, research_problem(_description/_aliases), domain, experiments[] (name, type, methods, datasets, metrics, key_results, conclusion, limitations, evidence), plus stats (sentence counts, filter backend, elapsed). Schema identical to the production workflow's predictions.
API contract
The skill contacts exactly ONE service: the public Zhipu BigModel chat-completions API. No internal/AMiner gateway is called anywhere (the SciBERT /filter/batch path was removed; stale vendored call sites raise explicitly).
- Sentence filter (Stage-A, GLM): one chat call to
{LLM_CHAT_URL} with a numbered-sentence scoring prompt (system prompt + user prompt, temperature: 0.05). GLM returns {"kept": [{"i", "score"}, ...]}; client keeps score ≥ 0.6 (frozen threshold), original order, cap 60 (WF4_MAX_QWEN_SENTENCES). Unparseable response is a hard error; an explicitly empty kept list is a legitimate "no experiment sentences" result.
- Extraction (Stage-B):
POST {LLM_CHAT_URL} (Zhipu BigModel https://open.bigmodel.cn/api/paas/v4/chat/completions, default model glm-5.3-flash) standard OpenAI messages format, stream: false, temperature: 0.05, max_tokens: 2048 (frozen). Auth: Authorization: Bearer $BIGMODEL_API_KEY.
- Paper md download (
--csv mode) fetches the user-provided md_url (public OSS/HTTP link) into --md-cache; no AMiner API is queried. Non-http(s), localhost/private-network, and metadata-host URLs are refused, redirects are re-validated per hop, and downloads are size-capped (same policy as aminer-pdf-ocr).
1---2name: aminer-exp-extraction3description: [Activation] Use when the user provides paper markdown file(s) and requests experiment data extraction (methods, datasets, metrics, results). [Scope] For each paper md: preprocess -> GLM sentence filter (glm-5.3-flash, SciBERT replacement) -> LLM extraction (glm-5.3-flash) -> one JSON per paper. Both model stages call the same Zhipu BigModel chat-completions API. Extraction semantic params (prompt, temperature 0.05, cap 60) are frozen, identical to the production prod-wf4 workflow. [Routing] Input is local md files (--md/--md-dir) or paper_id+md_url CSV rows (--csv). Use aminer-pdf-ocr first when the source is a PDF (it produces the md). Use aminer-academic-search for searching papers by topic.4---56# Experiment Data Extraction78One script, one chain: `md -> preprocess -> GLM filter -> LLM -> JSON`. No manifests, no run state, no monitoring — just extraction. A single model service (Zhipu BigModel, default `glm-5.3-flash`) powers both the sentence filter and the extraction.910## Pre-flight11121. Install `requests` from `requirements.txt`.132. `BIGMODEL_API_KEY` must be set — it authenticates BOTH model stages (sentence filter + extraction) as `Authorization: Bearer` (`OPENAI_API_KEY` accepted as fallback). Never print key values. No other credential or internal service is used.143. Optional overrides (env or flags):15 - `LLM_CHAT_URL` — default `https://open.bigmodel.cn/api/paas/v4/chat/completions` (used by both stages)16 - `LLM_MODEL` — default `glm-5.3-flash` (fast variant, used by both stages; `glm-5.3` / `glm-5.2` also valid)174. Input is the paper's markdown: a LOCAL file (`--md`/`--md-dir`), or `paper_id,md_url` CSV rows (`--csv`, md downloaded to `--md-cache`, cached across re-runs). Local mode: file stem = paper_id.1819## Run2021```bash22# single paper23python3 extract_experiments.py --md /path/to/paper.md -o out.json2425# batch: one md per paper, named <paper_id>.md26python3 extract_experiments.py --md-dir md_papers/ -o-dir out_json/2728# batch from paper_id + md_url (CSV: header paper_id,md_url; md downloaded to md_cache/)29python3 extract_experiments.py --csv papers.csv --md-cache md_cache/ -o-dir out_json/30```3132Per-paper failures don't stop the batch; exit code 2 means at least one failed. CSV downloads are cached — re-runs skip already-downloaded papers.3334## Output3536One JSON per paper: `paper_id`, `paper_title`, `research_problem(_description/_aliases)`, `domain`, `experiments[]` (name, type, methods, datasets, metrics, key_results, conclusion, limitations, evidence), plus `stats` (sentence counts, filter backend, elapsed). Schema identical to the production workflow's predictions.3738## API contract3940The skill contacts exactly ONE service: the public Zhipu BigModel chat-completions API. No internal/AMiner gateway is called anywhere (the SciBERT `/filter/batch` path was removed; stale vendored call sites raise explicitly).4142- Sentence filter (Stage-A, GLM): one chat call to `{LLM_CHAT_URL}` with a numbered-sentence scoring prompt (system prompt + user prompt, `temperature: 0.05`). GLM returns `{"kept": [{"i", "score"}, ...]}`; client keeps score ≥ 0.6 (frozen threshold), original order, cap 60 (`WF4_MAX_QWEN_SENTENCES`). Unparseable response is a hard error; an explicitly empty kept list is a legitimate "no experiment sentences" result.43- Extraction (Stage-B): `POST {LLM_CHAT_URL}` (Zhipu BigModel `https://open.bigmodel.cn/api/paas/v4/chat/completions`, default model `glm-5.3-flash`) standard OpenAI messages format, `stream: false`, `temperature: 0.05`, `max_tokens: 2048` (frozen). Auth: `Authorization: Bearer $BIGMODEL_API_KEY`.44- Paper md download (`--csv` mode) fetches the user-provided `md_url` (public OSS/HTTP link) into `--md-cache`; no AMiner API is queried. Non-http(s), localhost/private-network, and metadata-host URLs are refused, redirects are re-validated per hop, and downloads are size-capped (same policy as `aminer-pdf-ocr`).
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