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 commands/pydantic/skills/instrumentgit clone --depth 1 https://github.com/pydantic/skillsWrote 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/commands/pydantic/skills/instrument)<a href="https://agentmods.dev/commands/pydantic/skills/instrument"><img src="https://agentmods.dev/badge/commands/pydantic/skills/instrument.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 | $0.00015 | $0.00696 |
| Opus 5 | $0.00008 | $0.00348 |
| Sonnet 5 | $0.00003 | $0.00139 |
| Haiku 4.5 | $0.00002 | $0.00070 |
Grade A, and why
instrument 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 4d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/instrument
Add Logfire observability to the current project. Supports Python, JavaScript/TypeScript, and Rust - including polyglot projects.
Workflow
-
Detect languages and frameworks: Scan the project root for language indicators and dependency files:
pyproject.toml,requirements.txt- Pythonpackage.json- JavaScript/TypeScriptCargo.toml- Rust- A project may use multiple languages (e.g., Python backend + JS frontend). Instrument each.
-
For each detected language, follow the appropriate setup:
Python
- Identify instrumentable libraries from dependencies (FastAPI, httpx, asyncpg, SQLAlchemy, PydanticAI, OpenAI, Django, Flask, etc.)
- Install logfire with matching extras:
uv add 'logfire[<detected-extras>]'. Check foruv.lock(uv),poetry.lock(poetry), orPipfile.lock(pipenv) to pick the right package manager. - Find the application entry point and add:
import logfireat the toplogfire.configure()- must come before anyinstrument_*()callslogfire.instrument_<library>()calls for each detected framework- Web framework instrumentors (
instrument_fastapi,instrument_django,instrument_flask) need the app instance. HTTP client and database instrumentors are global.
JavaScript / TypeScript
- Check
package.jsonfor framework (Express, Next.js, Fastify, etc.) and runtime (Node.js, Cloudflare Workers, Deno). - Install the appropriate package:
- Node.js:
npm install @pydantic/logfire-node - Cloudflare Workers:
npm install @pydantic/logfire-cf-workers logfire - Next.js / generic:
npm install logfire
- Node.js:
- Add instrumentation based on runtime:
- Node.js: Create
instrumentation.tswithimport * as logfire from '@pydantic/logfire-node'andlogfire.configure(). Add--require ./instrumentation.jsto the start script. - Cloudflare Workers: Wrap the handler with
instrument()from@pydantic/logfire-cf-workers. - Next.js: Set
OTEL_EXPORTER_OTLP_TRACES_ENDPOINTandOTEL_EXPORTER_OTLP_HEADERSin.env.local.
- Node.js: Create
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.
- 4d ago First seen · 56 lines · 15 tokens per session scan A f41cd594def7
instrument is a command published in the GitHub repository pydantic/skills (127 stars, last pushed 5d ago), licensed MIT. It adds 15 tokens to every session and 696 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.