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 agentmods add skills/timurgaleev/memex/data-researchnpx skills add timurgaleev/memex --skill data-researchgit clone --depth 1 https://github.com/timurgaleev/memexWrote 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/timurgaleev/memex/data-research)<a href="https://agentmods.dev/skills/timurgaleev/memex/data-research"><img src="https://agentmods.dev/badge/skills/timurgaleev/memex/data-research.svg" alt="Measured on agentmods" 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 | $0.00049 | $0.01241 |
| Opus 5 | $0.00024 | $0.00620 |
| Sonnet 5 | $0.00010 | $0.00248 |
| Haiku 4.5 | $0.00005 | $0.00124 |
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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 150 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 a scheduled recipe —
see
skills/cron-scheduler/SKILL.md)
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 brain pages at recipes/{name}.md (YAML in a fenced block,
read with page_get). Scaffold a new one by copying a built-in recipe
page and editing its fields via page_put.
Phase 2: Search Sources
Brain first (maybe we already have this data — search, then query
for structured filters). Then:
- Email via the agent's own email tooling: windowed queries (quarterly, monthly if truncated)
- Web via the agent's own web search tooling: 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 (Haiku utility tier). Log every LLM fallback for future regex improvement (fail-improve loop). Skip marketing, newsletters, noise based on the recipe's classification rules.
Phase 4: Extract Structured Data
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.
- 3d ago First seen · 150 lines · 49 tokens per session scan A ee5edbe34941
data-research is a skill published in the GitHub repository timurgaleev/memex (8 stars, last pushed 9d ago), licensed MIT. It adds 49 tokens to every session and 1,241 once invoked, about $0.0002 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 2026-08-31.
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