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 cynthiajones34/GBrain --skill data-researchgit clone --depth 1 https://github.com/cynthiajones34/GBrainWrote 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/cynthiajones34/gbrain/data-research)<a href="https://agentmods.dev/skills/cynthiajones34/gbrain/data-research"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/data-research/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/cynthiajones34/gbrain/data-research"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/data-research.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 | $0.00049 | $0.01129 |
| Opus 5.5 | $0.00020 | $0.00452 |
| Sonnet 5.5 | $0.00010 | $0.00226 |
| Haiku 4.5 | $0.00005 | $0.00113 |
Grade A, and why
data-research 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 1mo 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
91% identical to data-research — 17 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Research
Structured research pipeline: search sources, extract structured data, archive raw, deduplicate, update canonical trackers, backlink entities.
Contract
One skill for any email-to-structured-data pipeline. The only differences between tracking investor updates, expenses, and company metrics are the search queries, extraction schemas, and tracker page format. All three use the same 7-phase pipeline with parameterized recipes.
When to Use
- User wants to track structured data from email, web, or API sources
- User says "research", "track", "extract from email", "build a tracker"
- User mentions investor updates, donations, company metrics, filings
- User wants to set up recurring data collection (with cron recipe)
Phases
Phase 1: Define Research Recipe
Ask the user what they want to track. Either:
- Pick a built-in recipe: investor-updates, expense-tracker, company-updates
- Define a custom recipe with: source queries, classification rules, extraction schema, tracker page path, tracker format
Recipes are YAML files at ~/.gbrain/recipes/{name}.yaml. Scaffold a new one by
copying a built-in recipe file and editing its fields.
Phase 2: Search Sources
Brain first (maybe we already have this data). Then:
- Email via credential gateway: windowed queries (quarterly, monthly if truncated)
- Web via search: public filings, press releases, regulatory data
- APIs: any structured data source the recipe defines
- Attachments: PDF extraction, HTML stripping
Phase 3: Classify
Deterministic first (regex patterns from recipe), LLM fallback. Log every LLM fallback for future regex improvement (fail-improve loop). Skip marketing, newsletters, noise based on recipe's classification rules.
Phase 4: Extract Structured Data
EXTRACTION INTEGRITY RULE:
- Save raw source immediately (before any extraction)
- Extract fields using deterministic regex first, LLM fallback
- When summarizing batch results: re-read from saved files
- Never trust LLM working memory after batch processing
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.
- 1mo ago First seen · 139 lines · 49 tokens per session scan A 990ccec01a23
data-research is a skill published in the GitHub repository cynthiajones34/GBrain (0 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 1,129 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to data-research, differing in 17 lines, and is treated as a copy.
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