Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add CanXiangCC/aminer-open-skill --skill aminer-exp-extractiongit clone --depth 1 https://github.com/CanXiangCC/aminer-open-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/canxiangcc/aminer-open-skill/aminer-exp-extraction)<a href="https://agentmods.dev/skills/canxiangcc/aminer-open-skill/aminer-exp-extraction"><img src="https://agentmods.dev/badge/skills/canxiangcc/aminer-open-skill/aminer-exp-extraction/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/canxiangcc/aminer-open-skill/aminer-exp-extraction"><img src="https://agentmods.dev/badge/skills/canxiangcc/aminer-open-skill/aminer-exp-extraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00184 | $0.01113 |
| Opus 5 | $0.00092 | $0.00557 |
| Sonnet 5 | $0.00037 | $0.00223 |
| Haiku 4.5 | $0.00018 | $0.00111 |
Grade A, and why
aminer-exp-extraction scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
requestsfromrequirements.txt. BIGMODEL_API_KEYmust be set — it authenticates BOTH model stages (sentence filter + extraction) asAuthorization: Bearer(OPENAI_API_KEYaccepted as fallback). Never print key values. No other credential or internal service is used.- Optional overrides (env or flags):
LLM_CHAT_URL— defaulthttps://open.bigmodel.cn/api/paas/v4/chat/completions(used by both stages)LLM_MODEL— defaultglm-5.3-flash(fast variant, used by both stages;glm-5.3/glm-5.2also valid)
- Input is the paper's markdown: a LOCAL file (
--md/--md-dir), orpaper_id,md_urlCSV 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).
What ships with it
46 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .gitignore 19 B
- extract_experiments.py 13 KB runs code
- pipeline/__init__.py 0 B runs code
- pipeline/benchmark/__init__.py 361 B runs code
- pipeline/benchmark/config.py 1.8 KB runs code
- pipeline/benchmark/parse_helpers.py 7.2 KB runs code
- pipeline/benchmark/stages/__init__.py 180 B runs code
- pipeline/benchmark/stages/bert_client.py 2.4 KB runs code
- pipeline/benchmark/stages/llm_client.py 1.7 KB runs code
- pipeline/benchmark/stages/openai_chat_llm_client.py 6.6 KB runs code
- pipeline/benchmark/stages/sentence_clean.py 3.0 KB runs code
- pipeline/benchmark/stages/union_merge.py 2.3 KB runs code
- pipeline/benchmark/workflows/__init__.py 98 B runs code
- pipeline/benchmark/workflows/base.py 2.7 KB runs code
- pipeline/benchmark/workflows/registry.py 1.1 KB runs code
- pipeline/benchmark/workflows/wf1_merged.py 15 KB runs code
- pipeline/benchmark/workflows/wf8_core.py 19 KB runs code
- pipeline/json_repair.py 7.4 KB runs code
- pipeline/production/__init__.py 399 B runs code
- pipeline/production/adapters/__init__.py 241 B runs code
- pipeline/production/adapters/dataset_confidence.py 20 KB runs code
- pipeline/production/adapters/gateway_auth.py 1.0 KB runs code
- pipeline/production/adapters/glm_sentence_filter.py 5.6 KB runs code
- pipeline/production/adapters/wf4_bert_struct.py 9.5 KB runs code
- pipeline/production/adapters/wf4_nobert_struct.py 4.3 KB runs code
- pipeline/production/adapters/wf4_normalize.py 19 KB runs code
- pipeline/production/adapters/wf4_prompt_adapter.py 13 KB runs code
- pipeline/production/adapters/wf4_prompt.py 10 KB runs code
- pipeline/production/adapters/wf4_sentence_clean.py 3.9 KB runs code
- pipeline/production/adapters/wf4_stages.py 31 KB runs code
- pipeline/production/adapters/wf4_struct_input.py 6.5 KB runs code
- pipeline/production/adapters/wf4_union.py 3.5 KB runs code
- pipeline/production/adapters/wf8_llm.py 2.5 KB runs code
- pipeline/production/adapters/wf8_stages.py 14 KB runs code
- pipeline/production/config.py 4.3 KB runs code
- pipeline/production/schema.py 7.2 KB runs code
- preprocess/__init__.py 691 B runs code
- preprocess/compact_markdown.py 16 KB runs code
- preprocess/pipeline.py 1.6 KB runs code
- preprocess/section_union_abs_intro.py 10 KB runs code
- preprocess/section_union_common.py 2.2 KB runs code
- preprocess/section_union_dataset_fallback.py 15 KB runs code
- preprocess/section_union.py 2.7 KB runs code
- preprocess/strip_references.py 3.3 KB runs code
- reference_detector.py 9.5 KB runs code
- requirements.txt 15 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 53 lines · 184 tokens per session scan A d504a09a2605
aminer-exp-extraction is a skill published in the GitHub repository CanXiangCC/aminer-open-skill (60 stars, last pushed 3d ago), licensed MIT. It adds 184 tokens to every session and 1,113 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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