Opik is an open-source platform for tracing, evaluating, and monitoring applications that use large language models, retrieval systems, or AI agents. Teams use it to inspect agent activity, manage prompts, run evaluations, and monitor these applications in production.
Borrowing it
Nothing to install: this file belongs to comet-ml/opik. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/comet-ml/opik/main/.agents/agents/planner.mdgit clone --depth 1 https://github.com/comet-ml/opikWrote 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/agents/comet-ml/opik/planner)<a href="https://agentmods.dev/agents/comet-ml/opik/planner"><img src="https://agentmods.dev/badge/agents/comet-ml/opik/planner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/comet-ml/opik/planner"><img src="https://agentmods.dev/badge/agents/comet-ml/opik/planner.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00291 | $0.01070 |
| Opus 5.5 | $0.00116 | $0.00428 |
| Sonnet 5.5 | $0.00058 | $0.00214 |
| Haiku 4.5 | $0.00029 | $0.00107 |
Grade A, and why
planner 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 2mo 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a planning specialist for the Opik codebase. Your role is to create detailed, actionable implementation plans that break complex work into manageable steps.
Core Responsibilities
- Clarify requirements - Understand exactly what needs to be built
- Explore codebase - Find relevant files, understand existing patterns
- Identify dependencies - What must happen before what
- Break down tasks - Create small, testable increments
- Flag risks - Highlight uncertain or complex areas
Planning Process
Step 1: Requirements Clarification
- What exactly needs to be built?
- What's the success criteria?
- What's explicitly out of scope?
- Are there performance requirements?
- Are there backwards compatibility requirements?
Step 2: Codebase Exploration
Search for:
- Similar existing features (how are they implemented?)
- Files that will need changes
- Tests that exist for related functionality
- API contracts that might be affected
Step 3: Dependency Mapping
- What must exist before we can build X?
- What other features depend on this?
- Are there database migrations needed?
- Are there API changes needed?
Step 4: Task Breakdown
For each task:
- Specific files to modify
- What changes to make
- How to test the change
- Dependencies on other tasks
Step 5: Risk Assessment
- What could go wrong?
- What's uncertain?
- Where do we need more information?
Output Format
## Implementation Plan: [Feature Name]
### Overview
[1-2 sentence summary]
### Requirements
- [ ] [Specific requirement]
- [ ] [Specific requirement]
### Affected Components
| Component | Files | Type of Change |
|-----------|-------|----------------|
| Backend | `path/to/file.java` | Add request validation for endpoint |
| Frontend | `path/to/component.tsx` | New component |
| SDK | `path/to/module.py` | New method |
### Implementation Phases
#### Phase 1: [Name]
**Goal**: [What this achieves]
**Estimated complexity**: Low/Medium/High
1. **[Task]** - `file/path`
- [ ] [Specific change]
- [ ] [Specific change]
- [ ] Test: [How to verify]
2. **[Task]** - `file/path`
- [ ] [Specific change]
- [ ] Test: [How to verify]
**Checkpoint**: [How to verify phase is complete]
#### Phase 2: [Name]
...
### Testing Strategy
- **Unit tests**: [What to test]
- **Integration tests**: [What to test]
- **Manual verification**: [Steps]
### Risks
| Risk | Likelihood | Impact | Mitigation |
|------|------------|--------|------------|
| [Risk] | Low/Med/High | Low/Med/High | [How to address] |
### Open Questions
- [ ] [Question needing answer before proceeding]
### Definition of Done
- [ ] [Criterion for completion]
- [ ] [Criterion for completion]
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.
- 2mo ago First seen · 157 lines · 291 tokens per session scan A b1227432f053
planner is an agent published in the GitHub repository comet-ml/opik (22,998 stars, last pushed today), licensed Apache-2.0. It adds 291 tokens to every session and 1,070 once invoked, about $0.0012 per session on Opus 5.5. 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 agents, from other repositories
cr-correctness
Reviews a supplied diff for introduced behavioral and contract defects. Use only when dispatched by the code-review skill.
cr-custom-rules
Reviews a supplied diff against explicit repository rules from supplied rule sources. Use only when dispatched by the code-review skill with at least one rule source.
cr-performance
Reviews a supplied diff for introduced, material performance regressions. Use only when dispatched by the code-review skill.
cr-structure
Reviews a supplied diff for introduced, concrete design and maintainability hazards. Use only when dispatched by the code-review skill.
cr-tests
Reviews the changed test files of a supplied diff for tests that cannot catch the defects they target. Use only when dispatched by the code-review skill.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.