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/comet-ml/opik-claude-code-plugin/instrumentgit clone --depth 1 https://github.com/comet-ml/opik-claude-code-pluginWhat 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 | $0.00017 | $0.02566 |
| Opus 5 | $0.00009 | $0.01283 |
| Sonnet 5 | $0.00003 | $0.00513 |
| Haiku 4.5 | $0.00002 | $0.00257 |
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 2d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Opik Observability
Add tracing to the user's code so their LLM application is observable in Opik.
User request: $ARGUMENTS
Step 1: Load the Skills
Use the Skill tool to load BOTH of these skills before doing anything else:
opik— Opik SDK reference: all integrations, tracing patterns, span types, code snippetsagent-ops— Agent architecture patterns, evaluation, what to trace and why
Load them both now. Do not proceed until both are loaded.
Step 2: Discover Frameworks from Dependencies (Do This FIRST)
Do NOT rely only on import statements. Code may use dynamic imports (__import__, importlib), factory patterns, or lazy loading that makes frameworks invisible to import scanning.
Instead, start by reading dependency manifests to build a checklist of frameworks that MUST be instrumented:
- Read dependency files — check
requirements.txt,pyproject.toml,setup.py,setup.cfg,Pipfile,package.json(for TypeScript/Node) - Build a framework checklist — for each dependency that has an Opik integration (OpenAI, Anthropic, LangChain, CrewAI, LlamaIndex, etc.), add it to your checklist
- Note ALL languages — if the project has both Python files AND TypeScript/JavaScript files (check for
package.json,tsconfig.json,*.ts,*.js), you must instrument BOTH languages
This checklist is your source of truth. Every framework on it must be accounted for by the end.
Step 3: Trace the Agent Flow
Now read the code to understand how it actually works. Follow the execution flow, don't just scan files in isolation:
- Find entry points — look for
if __name__ == "__main__", CLI commands, HTTP handlers, exported functions. There may be MULTIPLE entry points. - Trace the call graph — from each entry point, follow function calls to understand the full execution path. Read every file that gets called.
- Find where each framework on your checklist is actually used — it may be behind factories, registries, decorators, proxies, or dynamic imports. Search for:
- The framework's package name in strings (e.g.,
"openai","anthropic","crewai"as arguments to__import__()orimportlib.import_module()) - Class names from the framework (e.g.,
OpenAI,Anthropic,ChatOpenAI,Agent,Crew) - If you can't find where a dependency from the checklist is used, search the entire codebase for its package name as a string
- The framework's package name in strings (e.g.,
- Identify existing tracing — check if there's already tracing code. Verify it actually sends to Opik (not a homegrown stub or different tracing system). If it's fake or non-Opik, replace it.
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.
- 2d ago First seen · 134 lines · 17 tokens per session scan A 5d8087913660
instrument is a command published in the GitHub repository comet-ml/opik-claude-code-plugin (22 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 17 tokens to every session and 2,566 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
README
Git workflow and quality assurance commands for the claude-skills repository.
convert-to-todowrite-tasklist-prompt
Purpose: Transform verbose, context-heavy slash commands into efficient TodoWrite tasklist-based methods with parallel subagent execution for 60-70% speed improvements.
security-audit
Perform a comprehensive security audit of the codebase to identify potential vulnerabilities, insecure patterns, and security best practice violations.
statusbar-style
Switch the status-bar style (classic / capsule / hairline).
hunt
Active vulnerability hunt against a target by invoking tools/hunt.py (which calls vulnscanner.sh against recon/ /). Auto-runs recon first if no recon dir exists. Usage: /hunt target.com.
session-end
I'll summarize this coding session and update the memory system with our accomplishments.