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Signal Detector

CommunityPopular
garrytan
signal-detector

Opt-in ambient signal capture. After explicit enablement, applies on substantive inbound messages to detect original thinking and entity mentions. Use an authorized sub-agent where supported; otherwise detect inline. Aim to never block the main response.

Overview

Publishergarrytan
Repositorygbrain
Skill namesignal-detector
Stars
30.1K
Forks
4.5K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by garrytan on GitHub. Read the source before you install it.

Installation

Install the Signal Detector AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/garrytan/gbrain.git /tmp/gbrain
mkdir -p .claude/skills
cp -r /tmp/gbrain/plugin-variants/gbrain-coding/skills/signal-detector .claude/skills/signal-detector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Signal Detector in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Signal Detector on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Signal Detector is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Signal Detector — Ambient Brain Capture

After the user enables automatic capture, apply this lightweight pass to substantive inbound messages within their chosen scope. It watches for:

  1. Original thinking — the user's ideas, observations, theses, frameworks
  2. 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:

  • Applies only after explicit automatic-capture opt-in (skips: no recorded choice, capture off, chat-only requests, and purely operational messages)
  • Spawns as a sub-agent where the harness supports it; otherwise runs the detection inline before composing the reply. Never blocking the response is the intent, not a runtime contract
  • Checks the user's stored capture choice before writing and honors narrower per-message instructions; a first-fire announcement is not consent
  • 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

Ambient routing is a harness convention that a well-behaved agent follows, not a mechanical guarantee; nothing in the gbrain runtime blocks a reply if the skill never loads. On harnesses without per-message ambient routing (Claude Code, Codex), apply this skill as an agent convention or wire it via a prompt-submit hook. When the operator has enabled memory.auto_writeback (off by default; gbrain config set memory.auto_writeback salient), the MCP server's initialize instructions and the managed bootstrap instruction blocks carry the ambient-writeback contract to the agent, and on Claude Code a Stop-hook extraction backstop catches turns the convention missed. That is still a convention on the agent side — server-delivered instructions plus a backstop, not a mechanical guarantee.

Convention: See skills/conventions/quality.md for Iron Law back-linking.

Every time this skill creates or updates a brain page that mentions a person or company:

  1. Check if that person/company has a brain page
  2. If yes → add a back-link FROM their page TO the page you just created/updated
  3. Format: - **YYYY-MM-DD** | Referenced in [page title](path) — brief context
  4. An unlinked mention is a broken brain.

Enablement before capture

Automatic capture is off by default. Before writing, establish an explicit user choice for this brain and capture scope. A stored opt-in or an explicitly enabled memory.auto_writeback mode can supply that choice; off, a missing or unreadable choice cannot. Reading this skill, installing GBrain, an available API key, silence, or an announcement that capture is on does not authorize it.

If the user asks to enable capture, explain what will be retained and record the accepted choice. Honor an existing choice without asking again. A request to remember one fact authorizes that fact, not future ambient capture. Delegation and paid enrichment are separate capabilities: capture opt-in alone does not authorize spawning workers or making paid provider calls.

After activation, confirm the chosen scope once and explain how to turn it off. Do not capture the current turn while awaiting a required answer.

Per-User Storage Policy

If the user turns capture off, record the requested setting and stop capture: no content pages, links, or timeline entries. A chat-only instruction suppresses capture for that message without changing the standing setting; do not save the chat-only content as a preference. Re-enable only on request. Explicit remembering and relevant recall remain available when capture is off.

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)

  1. Extract entity mentions (people, companies, media titles)
  2. For each entity:
    • gbrain search "name" — does a page exist?
    • If NO page → check notability. If notable, save the supplied information with provenance.
    • If page exists but THIN → enrich only within separately authorized capabilities and spending
    • If page exists and RICH → no action
  3. For new FACTS with specific dates → call gbrain timeline-add <slug> <date> "<summary>"

Auto-link (v0.10.1): When you write/update an originals or ideas page that references a person or company, the auto-link post-hook on put_page automatically creates the link from the new page to that entity. You don't need to call gbrain link manually. Timeline entries still need explicit calls.

Phase 3: Signal Logging

Always log a one-line summary:

  • Signals: 0 ideas, 0 entities, 0 facts (skipped: operational)
  • Signals: 0 ideas, 0 entities, 0 facts (skipped: capture off)
  • Signals: 1 idea (captured → originals/x), 2 entities (enriched → people/y, companies/z)

This makes the ambient capture loop debuggable.

Output Format

After opt-in, report observed writes and their provenance. Keep the signal log brief and verify captured content with an actual readback. Without opt-in, perform no capture writes; a skipped-capture diagnostic is not a reason to interrupt every response or ask for enablement repeatedly.

Anti-Patterns

  • Blocking the main response to wait for signal detection to complete
  • Paraphrasing the user's original thinking instead of capturing exact phrasing
  • Creating pages for non-notable entities (one-off mentions)
  • Skipping back-links after creating/updating pages
  • Running on purely operational messages ("ok", "thanks", "do it")
  • Capturing without explicit opt-in, after capture was disabled, or on a chat-only turn
  • Treating a first-fire announcement, one explicit memory, or an API key as standing authorization
  • Starting paid enrichment or delegation just because capture is enabled

Tools Used

  • search — check if entity page exists
  • query — semantic search for related context
  • get_page — load existing entity pages
  • put_page — create/update brain pages
  • add_link — cross-reference entities
  • add_timeline_entry — record events on entity timelines

Frequently asked questions

What does the Signal Detector AI skill do?

Opt-in ambient signal capture. After explicit enablement, applies on substantive inbound messages to detect original thinking and entity mentions. Use an authorized sub-agent where supported; otherwise detect inline. Aim to never block the main response.

Why use Signal Detector on TypingMind?

Because you install it once and use it with any model. Signal Detector is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Signal Detector in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/garrytan/gbrain/tree/master/plugin-variants/gbrain-coding/skills/signal-detector. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Signal Detector?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Signal Detector?

As many as you like. As long as a model supports skills, you can use Signal Detector with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Signal Detector AI skill free?

Yes. It is published on GitHub by garrytan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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