Borrowing it
Nothing to install: this file belongs to htlin222/robust-lit-review. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/htlin222/robust-lit-review/main/.claude/skills/brainstorm-topic/SKILL.mdgit clone --depth 1 https://github.com/htlin222/robust-lit-reviewWrote 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/htlin222/robust-lit-review/brainstorm-topic)<a href="https://agentmods.dev/skills/htlin222/robust-lit-review/brainstorm-topic"><img src="https://agentmods.dev/badge/skills/htlin222/robust-lit-review/brainstorm-topic/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/htlin222/robust-lit-review/brainstorm-topic"><img src="https://agentmods.dev/badge/skills/htlin222/robust-lit-review/brainstorm-topic.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 | $0.00018 | $0.00598 |
| Opus 5 | $0.00009 | $0.00299 |
| Sonnet 5 | $0.00004 | $0.00120 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
brainstorm-topic scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://api.elsevier.com/content/search/scopus?query=TITLE-ABS-KEY(term)&count=0" \ How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brainstorm Research Topic Skill
You are a research methodology expert helping the user develop a comprehensive search strategy for a systematic literature review.
Process
Step 1: Understand the Topic
Ask the user for their research area. Probe with:
- What specific aspect interests you most?
- Clinical/applied or theoretical focus?
- Any population/setting constraints?
- Time period of interest?
Step 2: Generate Search Strategy
For the given topic, produce:
- Primary Search Terms (3-5 exact phrases)
- Synonyms and Alternatives (5-10 related terms)
- MeSH Terms (for PubMed — use the MeSH vocabulary)
- Emtree Terms (for Embase — use Emtree vocabulary)
- Boolean Query (combined with AND/OR/NOT)
- Scopus Field Codes (TITLE-ABS-KEY, AUTHKEY, etc.)
Step 3: Validate Search Terms
Use the APIs to test each query and report result counts:
# Test Scopus
curl -s "https://api.elsevier.com/content/search/scopus?query=TITLE-ABS-KEY(term)&count=0" \
-H "X-ELS-APIKey: $SCOPUS_API_KEY" | python -c "import sys,json; print(json.load(sys.stdin)['search-results']['opensearch:totalResults'])"
# Test PubMed
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&retmode=json&retmax=0&term=query&api_key=$PUBMED_API_KEY" \
| python -c "import sys,json; print(json.load(sys.stdin)['esearchresult']['count'])"
Step 4: Refine
Present a table:
| Database | Query | Results |
|---|---|---|
| Scopus | ... | N |
| PubMed | ... | N |
| Embase | ... | N |
If results are:
- Too many (>5000): Narrow with additional terms, date limits, or article type filters
- Too few (<50): Broaden synonyms, remove restrictive terms
- Sweet spot (100-1000): Proceed
Step 5: Output
Provide the finalized search strategy as a ready-to-use command:
lit-review "<TOPIC>" \
--term "term1" \
--term "term2" \
--term "term3" \
--target 50 \
--min-citescore 3.0
Or offer to run /lit-review directly with the refined terms.
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.
- 9d ago First seen · 71 lines · 18 tokens per session scan A 93c5f525124f
brainstorm-topic is a skill published in the GitHub repository htlin222/robust-lit-review (55 stars, last pushed 3mo ago), licensed MIT. It adds 18 tokens to every session and 598 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
research-agent
Project-first Research pipeline with live gates, source/PDF evidence, claim boundaries, compute and publication controls; includes an optional PubMed/arXiv standard-library helper.
scopus-researcher
Expert academic researcher using the Scopus MCP. Finds papers, retrieves full abstracts, builds author profiles, analyzes citation impact, and constructs advanced Boolean queries across the Elsevier Scopus database. Activate when asked to search for academic papers, analyze research trends, find citations, profile…
academic-research
Nested swiss-knife reference for academic literature work — find papers, fetch full-text PDFs, trace citations, write LaTeX manuscripts. First action for any "get me this paper" request: python3 /scripts/fetchpaper.py — walks arXiv → Unpaywall → Europe PMC → CORE → in-house publisher-page extraction…
deep-research
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 8 modes: full research, quick brief, paper review, lit-review, fact-check, three-way literature scan, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question…
adversarial-persona
Strategy: Role-play attacks from hostile personas — competing lab researcher, hostile reviewer, funding skeptic, domain outsider — each with distinct attack motivations and blind spots.
appropriateness-bounding
Establish acceptability standards through RAND/UCLA Appropriateness Method or Consensus Conference protocols.