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 cyberbird2048/gbrainmcp-clean --skill data-researchgit clone --depth 1 https://github.com/cyberbird2048/gbrainmcp-cleanWrote 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/cyberbird2048/gbrainmcp-clean/data-research)<a href="https://agentmods.dev/skills/cyberbird2048/gbrainmcp-clean/data-research"><img src="https://agentmods.dev/badge/skills/cyberbird2048/gbrainmcp-clean/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/cyberbird2048/gbrainmcp-clean/data-research"><img src="https://agentmods.dev/badge/skills/cyberbird2048/gbrainmcp-clean/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 | $0.01124 |
| Opus 5 | $0.00024 | $0.00562 |
| Sonnet 5 | $0.00010 | $0.00225 |
| Haiku 4.5 | $0.00005 | $0.00112 |
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 8d 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 — 12 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. Use gbrain research init
to scaffold a new one.
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.
- 8d ago First seen · 139 lines · 49 tokens per session scan A 9dc34392e954
data-research is a skill published in the GitHub repository cyberbird2048/gbrainmcp-clean (0 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 1,124 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to data-research, differing in 12 lines, and is treated as a copy.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.