Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/cynthiajones34/GBrainnpx agentmods add skills/cynthiajones34/gbrain/article-enrichmentWrote 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/article-enrichment)<a href="https://agentmods.dev/skills/cynthiajones34/gbrain/article-enrichment"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/article-enrichment/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/article-enrichment"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/article-enrichment.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 | $0.00050 | $0.01437 |
| Opus 5 | $0.00025 | $0.00718 |
| Sonnet 5 | $0.00010 | $0.00287 |
| Haiku 4.5 | $0.00005 | $0.00144 |
Grade A, and why
article-enrichment 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 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.
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
100% identical to article-enrichment — 0 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
article-enrichment — From Raw Dumps to Useful Brain Pages
Convention: see conventions/quality.md for citation rules, verbatim-quote requirements, and back-link enforcement.
Convention: see _brain-filing-rules.md for filing rules. Article pages live under
media/articles/for raw ingest; personalized one-of-one synthesis output uses the sanctionedmedia/articles/<slug>-personalized.mdexception.
What this does
Takes an article brain page that's a wall of raw extracted text and rewrites it as a structured page with:
- Executive Summary — 2-3 sentences, the ONE thing worth remembering
- Why It Matters — connects to the user's specific projects + interests (read from brain context, not assumed)
- Quotable Lines — 3-5 VERBATIM quotes worth referencing in essays
- Key Insights — actual insights, not topic labels
- Surprising or Counterintuitive — what makes this content unique
- See Also — standard markdown links to related brain pages
Raw source content is preserved in a collapsed <details> section so the
original is never lost.
When to invoke
- New article page lands in the brain via media-ingest with
needs_enrichment: true - Existing article page is a wall of text under a
## Contentheader with no synthesis - User says a brain page is useless, boring, or a dump
- An LLM-judge brain-quality eval fails on quotability or actionability for an article page
The pipeline
1. READ → Open the article brain page; parse frontmatter + body.
2. SCAN → Look for ## Content (raw dump) and absence of ## Executive Summary.
3. CONTEXT → gbrain query the article's key entities to ground "Why It Matters".
4. ENRICH → Sonnet (default) or Opus (for high-value content) restructures.
5. WRITE → Replace ## Content with the structured sections; preserve raw
source in <details>; clear needs_enrichment in frontmatter.
6. CROSS-LINK→ Add back-links from referenced people/companies pages
(Iron Law per conventions/quality.md).
What ships with it
1 file 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.
- 9d ago First seen · 150 lines · 50 tokens per session scan A fcdbce0f250a
article-enrichment is a skill published in the GitHub repository cynthiajones34/GBrain (0 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 1,437 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to article-enrichment, differing in 0 lines, and is treated as a copy.
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