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 ssmurfgg04-gif/context-m --skill aminer-deep-searchgit clone --depth 1 https://github.com/ssmurfgg04-gif/context-mWrote 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/ssmurfgg04-gif/context-m/aminer-deep-search)<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/aminer-deep-search"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/aminer-deep-search/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/ssmurfgg04-gif/context-m/aminer-deep-search"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/aminer-deep-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00140 | $0.01585 |
| Opus 5 | $0.00070 | $0.00792 |
| Sonnet 5 | $0.00028 | $0.00317 |
| Haiku 4.5 | $0.00014 | $0.00159 |
Grade A, and why
aminer-deep-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
98% identical to aminer-deep-search — 112 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AMiner Deep Search
Host-model-driven survey paper collection. You (the model reading this) are the controller: run the tool scripts, read their JSON output, judge relevance yourself, and iterate until the collection target is met.
Scope
- Use for: survey bibliography collection (hundreds of papers), keyword expansion, backward-citation snowballing.
- Do not use for: single-paper lookup or Q&A (route to
aminer-free-academic), personalized recommendations (route toaminer-daily-paper).
Pre-flight
- Check the key without printing it:
[ -z "${AMINER_API_KEY:-}" ] && echo "AMINER_API_KEY missing" || echo "AMINER_API_KEY exists"
If missing, stop and ask the user to set AMINER_API_KEY (console: https://open.aminer.cn/open/board?tab=control). Never print the key.
- Confirm the
topicand thetarget-size(default 400). If your round plan is estimated to cost ¥5 or more, tell the user the estimate and get confirmation before starting.
Tools
Both scripts live in scripts/ under this skill directory. They print exactly one JSON document to stdout (the tool result); diagnostics and a [cost] line go to stderr. They never score relevance — that is your job.
scripts/aminer_api.py — AMiner API calls
| Subcommand | Endpoint | Price |
|---|---|---|
search --query Q [--size 20] [--year YYYY] [--order n_citation|year] [--max-pages 3] |
GET /api/paper/search/pro + free paper/info enrichment |
¥0.01/page |
qa-search [--query "natural language question"] [--topic-high '[["termA","termB"],["termC"]]'] [--size 20] [--year-from Y] [--year-to Y] [--citation-sort] |
POST /api/paper/qa/search (always use_topic=true; the backend ignores query when use_topic=false) + free enrichment |
¥0.05/call |
info --ids id1 id2 ... |
POST /api/paper/info (batched ≤100 ids) |
Free |
references --ids id1 id2 ... [--per-seed 20] |
GET /api/paper/relation per seed + free enrichment |
¥0.10/seed |
Output shape: search/qa-search/info print [{id, title, year?, venue?, abstract_slice?}]; references additionally includes source_paper_ids (which seeds cited the paper). Seeds themselves are excluded from references output.
What ships with it
7 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.
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 · 112 lines · 140 tokens per session scan A a94ea113befd
aminer-deep-search is a skill published in the GitHub repository ssmurfgg04-gif/context-m (1 stars, last pushed yesterday), licensed Apache-2.0. It adds 140 tokens to every session and 1,585 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to aminer-deep-search, differing in 112 lines, and is treated as a copy.
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