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 skills add comet-ml/opik-skills --skill opik-explaingit clone --depth 1 https://github.com/comet-ml/opik-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/skills/comet-ml/opik-skills/opik-explain)<a href="https://agentmods.dev/skills/comet-ml/opik-skills/opik-explain"><img src="https://agentmods.dev/badge/skills/comet-ml/opik-skills/opik-explain/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/skills/comet-ml/opik-skills/opik-explain"><img src="https://agentmods.dev/badge/skills/comet-ml/opik-skills/opik-explain.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 | $0.00113 | $0.02028 |
| Opus 5 | $0.00056 | $0.01014 |
| Sonnet 5 | $0.00023 | $0.00406 |
| Haiku 4.5 | $0.00011 | $0.00203 |
Grade A, and why
opik-explain 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 5d 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.
This is a copy
100% identical to opik-explain — 23 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explain — Root-Cause an Opik Trace and Ground It in the Code
Definition of done: a grounded root cause for the requested trace (or pattern), tied to specific evidence spans and paired with exactly one suggested next step. "Grounded" means the explanation names the failing/anomalous span and connects it to the code or data that produced it — not a restatement of the trace. If the target can't be fetched or read, stop at the first genuine blocker and return one concrete next step. A trace dump is not an explanation.
Operate: investigate over the real trace data, reason against the repo, commit to a single most-likely root cause with its evidence — and change no code. This skill is read-only by design.
Inputs
The entry point is /opik-explain <trace-id> (one trace) or /opik-explain <describe the behavior> (a pattern to find and explain). Infer the rest; treat these as optional overrides:
- project name (default: inferred from config/repo) · time window for a pattern (default: recent) · a known-good trace to compare against.
Ask only at a genuine, non-inferable blocker (see Blockers).
Activation — the only in-scope work
1. Resolve the target
- A trace id (uuid-shaped): explain that one trace.
- A behavior/pattern ("hallucinations since the prompt change", "slow responses"): search for the matching set (below), then explain the shared cause.
- Confirm Opik is reachable: if
~/.opik.configexists orOPIK_API_KEYis set, use it. Otherwise → Blocker ("runopik configure, then rerun").
2. Fetch the trace and spans — MCP first, SDK fallback
Check whether the hosted Opik MCP is connected and prefer it; fall back to SDK scripting when it isn't.
- MCP connected: use the MCP to
readthe trace andlist/readits spans. - No MCP: fall back to the SDK.
Either way, read every span's input/output/error/duration.
import opik
client = opik.Opik()
tid = "<trace_id>"
trace = client.get_trace_content(tid) # TracePublic: exposes project_id, input, output, error info — NOT project_name (accessing .project_name raises)
spans = client.search_spans(trace_id=tid) # spans come from a SEPARATE call, not from the trace object
# Reconstruct the tree via each span's parent_span_id (the root span has none).
# Your anchor is the first span that errored, returned wrong output, or dominates the duration.
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.
- 5d ago First seen · 107 lines · 113 tokens per session scan A d830954b0169
opik-explain is a skill published in the GitHub repository comet-ml/opik-skills (7 stars, last pushed 6d ago), licensed Apache-2.0. It adds 113 tokens to every session and 2,028 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to opik-explain, differing in 23 lines, and is treated as a copy.
Other skills, from other repositories
opik-diagnose
Surface the Opik traces worth a developer's attention, ranked by signal — Diagnostics issues first, then errors, failed tool calls, latency, regressions, and low online-eval scores. With the Opik MCP connected it lists the project's agentinsightsissue entities, then fills the gaps with list (filters, sort, a time…
opik-explain
Root-cause a specific Opik trace, or a pattern across traces, and return a grounded explanation. Uses the hosted Opik MCP when it is connected, and falls back to SDK scripting otherwise. Returns the root cause, the evidence spans as clickable Opik UI links, and one suggested next step. Use for "why did this trace…
tracely
Instrument AI agents with Tracely and turn their production traces into CI gates. Use when the user mentions Tracely, tracely-ai, tracelysdk, the tracely CLI, or asks to trace/observe an AI agent, add LLM evaluators or LLM-as-a-judge columns, debug why a trace or conversation isn't showing up, wire agent regression…
opik-instrument
Add Opik tracing to an existing app and verify a real trace lands. Installs the Opik package, detects the language and LLM framework, adds the minimum tracing, runs a safe representative path, confirms a trace in Opik, and returns the trace link. Use for "instrument my code", "add opik tracing", "add observability"…
opik
Reference for the Opik SDK — tracing, span types, framework integrations, threads, and the prompt library (Python, TypeScript, REST). Use for "what span types exist", "how do I flush", "trackopenai", "add OpikTracer", "version a prompt". To instrument a repo end to end, use the opik-instrument skill.
opik-evaluate
Build an LLM evaluation and run it against your app, returning an experiment with scores. Covers datasets, LLM judges, RAG evaluation, synthetic data, error analysis, and validating evaluators against human labels. Use when the user wants to measure or improve AI product quality, or asks about evals, judges, or…