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 signal-detectorgit 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/signal-detector)<a href="https://agentmods.dev/skills/cynthiajones34/gbrain/signal-detector"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/signal-detector/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/signal-detector"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/signal-detector.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 | $0.00039 | $0.00994 |
| Opus 5 | $0.00019 | $0.00497 |
| Sonnet 5 | $0.00008 | $0.00199 |
| Haiku 4.5 | $0.00004 | $0.00099 |
Grade A, and why
signal-detector 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 11d 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
86% identical to signal-detector — 35 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Signal Detector — Ambient Brain Capture
Lightweight sub-agent that fires on every inbound message to capture TWO things with EQUAL priority:
- Original thinking — the user's ideas, observations, theses, frameworks
- Entity mentions — people, companies, media references
Original thinking is AT LEAST as valuable as entity extraction. Ideas are the intellectual capital. Entities are bookkeeping. Both compound over time.
Contract
This skill guarantees:
- Fires on every message (no exceptions unless purely operational)
- Runs in parallel (spawned, never blocks main response)
- Captures ideas with the user's EXACT phrasing (no paraphrasing)
- Detects entity mentions and creates/enriches brain pages
- Logs a one-line summary of what was captured
- Back-links all entity mentions (Iron Law)
- Citations on every fact written
Convention: See
skills/conventions/quality.mdfor Iron Law back-linking.
Every time this skill creates or updates a brain page that mentions a person or company:
- Check if that person/company has a brain page
- If yes → add a back-link FROM their page TO the page you just created/updated
- Format:
- **YYYY-MM-DD** | Referenced in [page title](path) — brief context - An unlinked mention is a broken brain.
Phases
Phase 1: Idea/Observation Detection (PRIMARY)
When the user expresses a novel thought, observation, thesis, or framework:
- If it's the user's original thinking (they generated it) → create/update
originals/{slug} - If it's a world concept they're referencing → create/update
concepts/{slug} - If it's a product or business idea → create/update
ideas/{slug}
Capture exact phrasing. The user's language IS the insight. Don't paraphrase.
Cross-linking (MANDATORY): Every original MUST link to related people, companies, meetings, and concepts. An original without cross-links is a dead original.
Phase 2: Entity Detection (SECONDARY)
- Extract entity mentions (people, companies, media titles)
- For each entity:
gbrain search "name"— does a page exist?- If NO page → check notability. If notable, create page with enrichment.
- If page exists but THIN → trigger enrich
- If page exists and RICH → no action
- For new FACTS with specific dates → call
gbrain timeline-add <slug> <date> "<summary>"
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.
- 11d ago First seen · 113 lines · 39 tokens per session scan A 64e4547f5a86
signal-detector is a skill published in the GitHub repository cynthiajones34/GBrain (0 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 994 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to signal-detector, differing in 35 lines, and is treated as a copy.
Other skills, from other repositories
memory-proactive
Proactive layered recall and generic domain-aware routing.
memory-archivist
A set of scripts for archiving conversations, syncing them to a knowledge graph, updating summaries, and managing stored memories over time. A knowledge graph is a linked collection of information and relationships.
memory-starter-kit
Historical starter note for the memory sidecar stack.
mind
Local project memory with recall, provenance, policy, and dreams.
personal-knowledge-graph
Use when maintaining a LoomKG/Obsidian knowledge graph.
shodh-memory
Persistent memory system for AI agents. Use this skill to remember context across conversations, recall relevant information, and build long-term knowledge. Activate when you need to store decisions, learnings, errors, or context that should persist beyond the current session.