Telemetry logo

Telemetry

Organization
ai-driven-dev
telemetry

Owns what a session cost and who it was for, under src/contexts/telemetry/ — reading a tool's own local files, attributing a figure to a person, a task, a flow or a step, and the sink that keeps records per machine. Use when adding a reader for another tool's transcript format, changing how a figure is attributed or reported, touching the sink or the identity store, or wiring a telemetry port. Do NOT use for what a tool declares about being measured — that is `kernel/measurement.ts`, read via the `tools` skill. Do NOT use for installing or removing the hook that writes the run journal into a project — that record belongs to `framework`.

Overview

Publisherai-driven-dev
Repositoryframework
Skill nametelemetry
Stars
515
Forks
54
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 ai-driven-dev on GitHub. Read the source before you install it.

Installation

Install the Telemetry 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.
    https://github.com/ai-driven-dev/framework/tree/main/cli/.claude/skills/telemetry
  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/ai-driven-dev/framework.git /tmp/framework
mkdir -p .claude/skills
cp -r /tmp/framework/cli/.claude/skills/telemetry .claude/skills/telemetry
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Telemetry 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 Telemetry 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 Telemetry 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.

Telemetry

telemetry answers two questions about work already done: what did it cost, and whose was it. It never causes the work and never installs anything on a project's behalf. Everything it reads already exists on disk because a tool wrote it, so the whole context is a set of readers, a set of attribution rules, and one sink.

Where the sink and the run journal live, what the report renders, and what each tool declares about being measured are in aidd_docs/memory/telemetry.md. Read that for the facts; this page says where new code goes and what it must not do.

What goes in

ConceptLocation
A rendered answer's shapedomain/cost-report.ts, domain/cost-report-envelope.ts
How a figure is tied to somethingdomain/step-attribution.ts, domain/task-attribution.ts, domain/flow-attribution.ts
Reading one tool's own file formatdomain/formats/ (one module per tool's transcript or export)
A stored record and what happens to it over timedomain/telemetry-sink-record.ts, domain/telemetry-sink-retention.ts
Who a session was for, and how strongly that is knowndomain/person-resolution.ts, domain/ports/person-identity-reader.ts, domain/ports/person-identity-store.ts
Something telemetry needs from outside itselfdomain/ports/ — declared here, satisfied at the composition root
The concrete reader behind one of those portsinfrastructure/
One question the aidd telemetry command asksapplication/

How

  • A tool declares, telemetry reads. What a route was measured to supply, and where a tool's transcripts live, are declared in kernel/measurement.ts and filled in per tool under contexts/tools/domain/profiles/<tool>/. Every field there is required on purpose: a default would be a capability nobody measured, quietly asserted for a tool nobody looked at. Never add a branch on a tool id inside this context.
  • What telemetry needs from another context, it declares as its own port. installed-plugins-reader.ts and ignore-entries.ts are the pattern: telemetry states the question, runtime/wiring/telemetry.ts hands it an answer, and no context reaches into telemetry in return.
  • A figure with no established denomination is not an amount. A zero whose denomination was never established, a credit and a premium request are each their own thing; conflating them is how a report lies without ever being wrong about a number.
  • An interval derived from the run journal is this CLI's inference, not the tool's statement. Keep the two distinguishable in whatever you add — toolStatedStep exists for exactly that.
  • Follow the use-case and port/adapter rules in .claude/rules/00-architecture/.

Public surface

tests/architecture/context-boundary.arch.test.ts holds the list (PUBLIC_MODULES.telemetry): the use cases the telemetry command drives, the shapes a rendered answer is made of, the commit-trailer format the git adapter writes, and the two ports a caller wires a concrete adapter into (domain/ports/telemetry-sink.ts, domain/ports/version-control.ts). Nothing else, and no adapter. Rendering happens in presentation/display/, never here.

How it's tested

  • tests/contexts/telemetry/ mirrors src/contexts/telemetry/ — a format reader and an attribution rule are unit-tier; an adapter against a real temp filesystem is integration-tier.
  • A reader for a new tool's format needs a fixture captured from that tool's real output. A format module tested only against a fixture this repository wrote proves the parser, not the format. See the test skill before touching a golden snapshot.

Frequently asked questions

What does the Telemetry AI skill do?

Owns what a session cost and who it was for, under src/contexts/telemetry/ — reading a tool's own local files, attributing a figure to a person, a task, a flow or a step, and the sink that keeps records per machine. Use when adding a reader for another tool's transcript format, changing how a figure is attributed or reported, touching the sink or the identity store, or wiring a telemetry port. Do NOT use for what a tool declares about being measured — that is `kernel/measurement.ts`, read via the `tools` skill. Do NOT use for installing or removing the hook that writes the run journal into...

Why use Telemetry on TypingMind?

Because you install it once and use it with any model. Telemetry 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 Telemetry in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ai-driven-dev/framework/tree/main/cli/.claude/skills/telemetry. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Telemetry?

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 Telemetry?

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

Is the Telemetry AI skill free?

Yes. It is published on GitHub by ai-driven-dev 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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