instrument-agent

instrument-agent is a skill for Claude Code, Codex from raindrop-ai/workshop. It costs 114 tokens per session (3,167 once invoked), scanned A, original, MIT.

A setup workflow for adding Raindrop AI tracing to an agent so its runs appear in Workshop, a local viewer for debugging agent activity. It discovers the project setup and uses supported Raindrop integrations.

In plain words
What is it for?
Use it to add tracing to an agent, connect the appropriate Raindrop SDK or integration, and verify that a meaningful run appears in Workshop.
Why use it?
It removes the guesswork from connecting an agent's model and tool activity to the local viewer. It also avoids inventing custom ingestion code when the project cannot be safely supported.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/raindrop-ai/workshop/instrument-agent
Any agent
npx skills add raindrop-ai/workshop --skill instrument-agent
Clone the repo
git clone --depth 1 https://github.com/raindrop-ai/workshop

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for instrument-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/raindrop-ai/workshop/instrument-agent.svg)](https://agentmods.dev/skills/raindrop-ai/workshop/instrument-agent)
Your own site
<a href="https://agentmods.dev/skills/raindrop-ai/workshop/instrument-agent"><img src="https://agentmods.dev/badge/skills/raindrop-ai/workshop/instrument-agent.svg" alt="Measured on agentmods" height="20"></a>
Per session 114 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,167 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00114 $0.03167
Opus 5 $0.00057 $0.01584
Sonnet 5 $0.00023 $0.00633
Haiku 4.5 $0.00011 $0.00317

Measured 4d ago against content hash af8d673a2a40, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

instrument-agent scanned grade A with 1 finding 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- Check whether Workshop is running: `curl -fsS http://localhost:5899/health`.
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/instrument-agent/SKILL.md · 263 lines

How it starts

The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are helping instrument an AI agent so its next meaningful run appears in Raindrop Workshop. Workshop is the local viewer; it does not run the agent. The user's agent app runs the workflow, the Raindrop SDK captures model/tool boundaries and context, and Workshop renders that telemetry as a debuggable run.

Use supported Raindrop SDK/integration paths. Do not hand-wire Workshop ingestion endpoints or invent SDK APIs. If the repo's telemetry setup is too custom to instrument safely, stop with a clear handoff to the Raindrop docs or team.

Use Docs First

Raindrop SDK and integration APIs move quickly. Before writing code, fetch docs in parallel: the docs index, the likely stack-specific page, and installed package README/types when available.

  • Docs index: https://raindrop.ai/docs/llms.txt
  • Introduction: https://raindrop.ai/docs/introduction
  • Integration overview: https://raindrop.ai/docs/integrations/overview
  • Core SDKs: https://raindrop.ai/docs/sdk/typescript, /sdk/python, /sdk/rust, /sdk/go, /sdk/http-api, /sdk/browser
  • Framework integrations: https://raindrop.ai/docs/integrations/<name> such as vercel-ai-sdk, claude-agent-sdk, langchain, openai-agents, pydantic-ai, google-adk, bedrock, azure-openai, vertex-ai

Use this skill for the workflow and Workshop-specific judgment. Use the docs and installed package README/types for exact install commands, imports, options, and signatures.

Core Rules

  • Give visible progress. Say what phase you are in, what you learned, and what you are about to edit. Do not silently research for minutes.
  • Report often but briefly: one or two sentences per update, focused on current phase, finding, and next step. Avoid long dumps unless you are blocked and need a decision.
  • Before editing any file, tell the user about the intended change, and give them an honest assessment for how risky it might be.
  • Instrument one real agent entry point first. If several are plausible, ask which one should appear in Workshop.
  • First get a minimal useful run into Workshop, then enrich it. Do not trace every helper/tool/sub-agent before Phase 1 works.
  • Respect existing telemetry ownership. If the repo already initializes OpenTelemetry, Sentry, Datadog, Honeycomb, Traceloop, LangSmith, or another provider, do not create a competing provider.
  • Updating the matching Raindrop SDK/integration to the latest available version is mandatory before instrumentation edits. Do not proceed on an older installed SDK just because it is already present.
  • Prefer installed package docs/types over memory. If an API is not present in the installed package, do not use it.
  • Verification is required. Success means Workshop shows a useful run, not just that dependencies installed.

Read the full file on GitHub · 263 lines

Changes

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.

  1. 4d ago First seen · 263 lines · 114 tokens per session scan A af8d673a2a40

Subscribe to this mod's changes

instrument-agent is a skill published in the GitHub repository raindrop-ai/workshop (1,067 stars, last pushed 12d ago), licensed MIT. It adds 114 tokens to every session and 3,167 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

trulens-instrumentation

Instrument LLM apps with TruLens OTEL-based tracing - from setup to debugging and optimization.

truera/trulens · 25 tokens

tracelet-instrument

Instrument an AI agent or LLM app with OpenTelemetry and watch its runs live in tracelet (local DevTools). Use when asked to add tracing/observability to an agent, or to debug what an agent actually did.

jnMetaCode/skillet · 51 tokens

mcporter

List, auth, and call MCP servers/tools from the terminal.

NousResearch/hermes-agent · 16 tokens

article-writing

Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.

affaan-m/ECC · 57 tokens

mem0-oss-to-platform

Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…

mem0ai/mem0 · 273 tokens

complete-partial-pr

Evaluate and complete an issue or PR where the submitted patch fixes only a narrow symptom of the reported pain point. Use when a contribution may miss adjacent integration surfaces, provider/spec semantics, roundtrip behavior, tests, docs, or historical maintainer decisions.

pydantic/pydantic-ai · 55 tokens