# 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 agent_insights_issue entities, then fills the gaps with list (filters, sort, a time window); without the MCP it reads the same via the SDK (agent_insights and search_traces), so it works with no MCP. Returns a ranked shortlist, each item ready to hand to the explain skill. Use for "what is broken in production", "which traces need attention", "find failing or slow traces", "which tool calls are failing", "triage my agent". Not for offline experiment results (use evaluate or compare) and not for root-causing one trace (use explain).

- Skill: `comet-ml/opik-diagnose` (Agent Skill)
- Install (CLI): `npx skillmds@latest add comet-ml/opik-diagnose`
- Raw SKILL.md: https://api.skillmd.com/api/skills/comet-ml/opik-diagnose/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: comet-ml (https://skillmd.com/u/comet-ml)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/comet-ml/opik-diagnose

---


# Diagnose — Surface the Traces Worth Attention

**Definition of done:** a **ranked shortlist** of the online/production traces (and Diagnostics issues) worth attention, each carrying the **signal that flagged it** and its **trace id**, scoped to a project and a recent window, and ready to hand to `/opik-explain`. "Worth attention" means errored, slow, regressed, or low online-eval score — not a dump of every trace, and never offline experiment results. If the project can't be read, stop at the first genuine blocker and return one next step.

Operate: **rank by real signal over live data, surface the few things worth a look, hand the top one to `/opik-explain` — and change no code.** This skill is read-only by design.

## Inputs

The entry point is `/opik-diagnose` (the current project), `/opik-diagnose <project>`, or `/opik-diagnose <what to look for>` (e.g. "slow traces", "errors today"). Infer the rest; treat these as **optional overrides**:

- project (default: inferred from config/repo) · window (default: recent) · signal focus (default: all — errors, latency, regressions, low scores) · shortlist size (default: a handful).

Ask only at a genuine, non-inferable blocker (see **Blockers**).

## Activation — the only in-scope work

### 1. Resolve scope
Project (from config/repo) + a recent window. Confirm Opik is reachable: if `~/.opik.config` exists or `OPIK_API_KEY` is set, use it. Otherwise → **Blocker** ("run `opik configure`, then rerun").

### 2. Start from Diagnostics issues
Opik's Diagnostics already groups a project's recurring failures into ranked issues, each with a severity, occurrence counts, a cause, a suggested fix and example traces. Read that list first — it is the answer to "what is broken" the UI already computed, so do not rebuild it from raw traces.

**With the Opik MCP connected**, the `agent_insights_issue` entity is the primary path:

```text
list('agent_insights_issue', project_name='<project>')        # open issues, ranked as the Diagnostics page ranks them
read('agent_insights_issue', '<issue id>', project_name='<project>')
#  → {issue: {name, cause, suggested_fix, severity, status, …}, example_trace_ids: [...], details: [...],
#     url: '<the issue's Diagnostics page>', trace_url_template: '<…/logs?trace={trace_id}>'}
```

Each open issue becomes one shortlist item with `signal=diagnostics`: `trace_id` comes from the first `example_trace_ids` entry (read the top few issues to get them) and `trace_url` from `trace_url_template` with that id filled in; mention the issue's `url` so the user can open the Diagnostics page itself. `why` carries the issue name, severity, `latest_count` and the cause. The link fields are absent when the server cannot name the UI base or workspace — fall back to the trace redirect URL then. Keep the list's order — it is the Diagnostics page's ranking (most recently seen first, then most occurrences). Counts are all-time to match the UI; pass `since` (e.g. `"7d"`) to narrow to the window.

**Without the MCP**, the SDK REST client reads the same issues:

```python
import opik
client = opik.Opik()
# Needs the project_id (a uuid), not the name — read it off any trace from
# search_traces (trace.project_id), or resolve it from the project name first.
issues = client.rest_client.agent_insights.find_agent_insights_issues(project_id=project_id)
```

### 3. Pull candidate traces to fill the gaps — MCP first, SDK fallback
Diagnostics reports what its last run grouped. Anything newer, or below its grouping threshold — a single latency outlier, one low online-eval score, a regression versus the prior window — still needs a scan. Skip traces already covered by an issue's `example_trace_ids`; they are on the shortlist under that issue.

- **MCP connected:** one `list` call per signal. The backend does the filtering and ordering, so each call returns a short, already-ranked page — no SDK, no client-side sorting. `since` takes `"1h"`, `"24h"`, `"7d"`; `filters` is an OQL string; `sort` is `"<field> [asc|desc]"` (desc by default). Trace lists hide evaluator/playground/experiment traces (`source = "sdk"`) unless you name `source`.

  ```
  list(entity_type="trace", project_name="<project>", since="24h",
       filters="error_info is_not_empty", sort="start_time desc")         # errored
  list(entity_type="span",  project_name="<project>", since="24h",
       filters='type = "tool" AND error_info is_not_empty', sort="start_time desc")  # failed tool calls
  list(entity_type="trace", project_name="<project>", since="24h",
       sort="duration desc")                                              # latency outliers (ms)
  list(entity_type="trace", project_name="<project>", since="24h",
       filters="feedback_scores.<metric> < 0.5", sort="feedback_scores.<metric> asc")  # low online-eval score
  list(entity_type="trace", project_name="<project>", since="7d", until="24h",
       sort="duration desc")                                              # prior window, for regressions
  ```

  The table carries `duration`, `error_type` and cost by default plus every field you sorted or filtered on. A rejected filter comes back with what fixes it; `schema("list.trace")` is the full field reference.

- **No MCP:** fall back to the SDK.

  ```python
  traces = client.search_traces(project_name="<project>", max_results=200)  # recent window
  # Narrow server-side with filter_string='error_info is_not_empty' when the volume is
  # large; otherwise rank client-side (step 4). Each trace carries the fields you rank
  # on: error info, duration, feedback_scores.
  ```

Skip traces already covered by an issue's `example_trace_ids`; they are on the shortlist under that issue.

### 4. Rank the remaining traces by signal
Score each remaining candidate and keep the top few. Priority order:
1. **Errored** — the trace or a span captured an exception.
2. **Tool-call failures** — a `tool` span errored, returned an error-shaped result, or repeated the same call (a retry loop). Agents fail here often, so surface it as its own signal: use `has_tool_spans` to find candidates, then scan their `tool` spans for a non-empty error, an output that reads like an error/refusal, or duplicate consecutive calls.
3. **Latency outliers** — duration well above the project's typical (use the p90/p99 as the bar).
4. **Low online-eval score** — a feedback score below its threshold (Answer Relevance, Hallucination, etc.).
5. **Regressions** — a signal that worsened versus the prior window.

Append these after the Diagnostics items, in the signal order above. Give each shortlisted item the one signal that flagged it and a short why. Prefer a short, ranked list over a long one.

### 5. Stay in scope
Online/production **trace** signal only. Do **not** surface offline experiment results — those are the output of `/opik-evaluate` and `/opik-compare`, not rediscovered here.

### 6. Report
Return the ranked shortlist and one next step. Give each item as a **clickable Opik UI link** (the trace redirect URL Opik emits, e.g. `.../session/redirect/...?trace_id=THE_ID`), never a bare id, so the user can open it and deep-dive. Each item is ready for `/opik-explain`; the natural next step is "explain the top trace" (see **Output**). This skill surfaces and hands off; it does not root-cause (that is `/opik-explain`) and it changes no code.

## Blockers

Stop at the **earliest** blocker and return **exactly one** next step:
- "Run `opik configure`, then rerun `/opik-diagnose`."
- "Which project should I scan? Pass `/opik-diagnose <project>` or set it in the Opik config."
- "This environment can't reach Opik — open the project's traces view, sort by errors/duration, or run where Opik is configured."

## Output

**User-facing:** a short human message — the ranked shortlist (a clickable Opik UI link per trace + its signal + one-line why, worst first), then the single next step. Not a raw dump of every trace, not JSON.

**Underneath** (for composition / evals), one shape:
- `status`: `found` | `empty` | `blocked`
- `scope`: `project`, `window`
- `shortlist`: list of `{trace_id, trace_url (the Opik UI link), signal (error|tool_call|latency|low_score|regression|diagnostics), why, rank}`
- `source`: `sdk` | `mcp`
- `next_step`: exactly one (typically "explain the top trace")

Invariants: `found` carries a non-empty `shortlist`, each item with a `signal`, a `trace_id`, and a clickable `trace_url`; `empty` = the read succeeded but nothing crossed a threshold; `blocked` carries exactly one `next_step`; the shortlist never contains offline experiment results; every path leaves the codebase unchanged.

## Examples

**Triage a project, MCP connected.** `/opik-diagnose`. `list('agent_insights_issue', project_name=…)` returns two open issues: a high-severity tool-call loop (12 occurrences yesterday) and a low-severity empty-answer issue. `read` on the first gives the cause, three example trace ids and the links. `list('trace', since="24h", sort="duration desc")` then finds one trace 5x the p90 that no issue covers. Shortlist = the tool-call loop (rank 1, `diagnostics`, first example trace), the latency outlier (2, `latency`), the empty-answer issue (3, `diagnostics`); next step = "explain the top trace". `source=mcp`. → **`found`**.

**Triage a project, SDK only.** `/opik-diagnose`. `find_agent_insights_issues` returns nothing yet; `search_traces` on the project finds two errored traces, one 5x the p90 duration, one scored 0.2 on Hallucination. Shortlist = the two errors (rank 1-2), the latency outlier (3), the low-score trace (4), each with its signal; next step = "explain the top trace". `source=sdk`. → **`found`**.

**Nothing wrong.** `/opik-diagnose`. Reads fine, but no trace errored, ran slow, or scored low. → **`empty`**: "No traces crossed a threshold in the recent window."

**Blocked — no config.** `/opik-diagnose`. No `~/.opik.config`, no `OPIK_API_KEY`. → **`blocked`**: "run `opik configure`, then rerun `/opik-diagnose`." (No code touched.)

## Anti-patterns
Dumping every trace instead of a ranked shortlist; rebuilding the Diagnostics ranking from raw traces when `agent_insights_issue` (or the SDK `agent_insights` client) already lists the issues; surfacing offline experiment/`evaluate` results (out of scope); requiring the MCP (the SDK `agent_insights` path needs none); root-causing a trace here (hand it to `/opik-explain`); **editing code** (this skill only surfaces); ranking by recency instead of signal.

## References

SDK and observability detail live in the `opik` skill, installed beside this one. Read the files directly — paths are relative to this file: `../opik/SKILL.md` (**Searching traces** — the OQL filter grammar shared by the MCP `list` tool and `search_traces`), `../opik/references/production.md` (`search_traces`, Diagnostics, online-eval scores, error/latency analysis), `../opik/references/tracing-python.md` (SDK read APIs), `../opik/references/observability.md` (span/score model). If your host lays skills out differently, locate the `opik` skill's `references/` directory.

If the `opik` skill isn't installed, say so in the report and use <https://www.comet.com/docs/opik/> rather than working from memory.