aminer-exp-extraction

aminer-exp-extraction is a skill for Claude Code, Codex from CanXiangCC/aminer-open-skill. It costs 184 tokens per session (1,113 once invoked), scanned A, original, MIT.

A script that reads research papers in Markdown and uses a language model to extract experiment data into JSON files.

In plain words
What is it for?
It helps turn paper text into machine-readable experiment records for later analysis or data processing. You provide local Markdown files or a CSV containing paper IDs and Markdown URLs.
Why use it?
It removes the need to manually find relevant sentences and copy experiment details into a structured format. It can process one paper or a batch of cached papers.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit It helps turn paper text into machine-readable experiment records for later analysis or data processing. You provide local Markdown files or a CSV containing paper IDs and Markdown URLs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/canxiangcc/aminer-open-skill/aminer-exp-extraction
Install

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.

Any agent
npx skills add CanXiangCC/aminer-open-skill --skill aminer-exp-extraction
Clone the repo
git clone --depth 1 https://github.com/CanXiangCC/aminer-open-skill

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin aminer-exp-extraction/plugin install aminer-exp-extraction after adding the marketplace above.

Wrote 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.

agentmods badge for aminer-exp-extraction

README.md
[![agentmods](https://agentmods.dev/badge/skills/canxiangcc/aminer-open-skill/aminer-exp-extraction/github.svg)](https://agentmods.dev/skills/canxiangcc/aminer-open-skill/aminer-exp-extraction)
Your own site
<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.

agentmods 80×15 button for aminer-exp-extraction

Your own site · 80×15
<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>
Per session 184 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,113 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash d504a09a2605, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 44 executable files (extract_experiments.py, pipeline/__init__.py, pipeline/benchmark/__init__.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/aminer-exp-extraction/SKILL.md · 53 lines

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

  1. Install requests from requirements.txt.
  2. 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.
  3. 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)
  4. 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).

Read the full file on GitHub · 53 lines

Files

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.

Changes

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.

  1. 11d ago First seen · 53 lines · 184 tokens per session scan A d504a09a2605

Subscribe to this mod's changes

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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