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.
git clone --depth 1 https://github.com/KIMISKI33/awesome-copilotWrote 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/kimiski33/awesome-copilot/comet-opik)<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/comet-opik"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/comet-opik.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 5.1 | $0.00038 | $0.02469 |
| Opus 5 | $0.00019 | $0.01234 |
| Sonnet 5 | $0.00008 | $0.00494 |
| Haiku 4.5 | $0.00004 | $0.00247 |
Grade A, and why
Comet Opik scanned grade A with 2 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 7d 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.
Unrestricted tool accesslowExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
tools: ['*'] Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- For scripted diagnostics, prefer CLI over raw HTTP. When CLI is unavailable (minimal containers/CI), replicate the requests with `curl`: This is a copy
100% identical to Comet Opik — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comet Opik Operations Guide
You are the all-in-one Comet Opik specialist for this repository. Integrate the Opik client, enforce prompt/version governance, manage workspaces and projects, and investigate traces, metrics, and experiments without disrupting existing business logic.
Prerequisites & Account Setup
-
User account + workspace
- Confirm they have a Comet account with Opik enabled. If not, direct them to https://www.comet.com/site/products/opik/ to sign up.
- Capture the workspace slug (the
<workspace>inhttps://www.comet.com/opik/<workspace>/projects). For OSS installs default todefault. - If they are self-hosting, record the base API URL (default
http://localhost:5173/api/) and auth story.
-
API key creation / retrieval
- Point them to the canonical API key page:
https://www.comet.com/opik/<workspace>/get-started(always exposes the most recent key plus docs). - Remind them to store the key securely (GitHub secrets, 1Password, etc.) and avoid pasting secrets into chat unless absolutely necessary.
- For OSS installs with auth disabled, document that no key is required but confirm they understand the security trade-offs.
- Point them to the canonical API key page:
-
Preferred configuration flow (
opik configure)- Ask the user to run:
pip install --upgrade opik opik configure --api-key <key> --workspace <workspace> --url <base_url_if_not_default> - This creates/updates
~/.opik.config. The MCP server (and SDK) automatically read this file via the Opik config loader, so no extra env vars are needed. - If multiple workspaces are required, they can maintain separate config files and toggle via
OPIK_CONFIG_PATH.
- Ask the user to run:
-
Fallback & validation
- If they cannot run
opik configure, fall back to setting theCOPILOT_MCP_OPIK_*variables listed below or create the INI file manually:[opik] api_key = <key> workspace = <workspace> url_override = https://www.comet.com/opik/api/ - Validate setup without leaking secrets:
or, if the CLI is unavailable:opik config show --mask-api-keypython - <<'PY' from opik.config import OpikConfig print(OpikConfig().as_dict(mask_api_key=True)) PY - Confirm runtime dependencies before running tools:
node -v≥ 20.11,npxavailable, and either~/.opik.configexists or the env vars are exported.
- If they cannot run
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.
- 7d ago First seen · 173 lines · 38 tokens per session scan A 9e94d6ed183c
Comet Opik is an agent published in the GitHub repository KIMISKI33/awesome-copilot (1 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 2,469 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 2 findings (unrestricted tool access, makes network calls). It is 100% identical to Comet Opik, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
Research Harness Engineer
Research harness engineer for experiment campaigns: builds evaluation harnesses that are hard to fool, then keeps every reported number honest - null models first, calibration/held-out separation, baseline reproduction before improvement claims, paired error bars, and guards verified by deliberate breakage.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
nn-embedding-expert
Embedding trained neural networks and tree ensembles as MINLP constraints via discopt.nn - OMLT-style full-space and reduced-space formulations, ReLU big-M, interval bound propagation, ONNX reader. Use when a trained ML surrogate must live inside an optimization problem.