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Build custom annotation UIs for human review of agent traces, LLM outputs, and labeled data. Generates a self-contained HTML interface for reviewing, labeling, comparing, and exporting judgments. For calibrating evals, auditing agent behavior, and building gold-standard datasets.
Passed 18 of 23 test cases with the skill, and 12 of 23 without it.
and 6 more in the full report
7 fixed11 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need to set up a human evaluation tool for a dataset containing pairs of model outputs stored in `llm_responses_paired.jsonl`. Can you build a standalone web review interface to determine which model response is superior? Make sure to hide model identities so reviewers don't know which output came from which system.
| Checked | Without skill | With skill |
|---|---|---|
| Output includes the `## Review Interface Generated` header section. | Missed | Passed |
| Specifies comparison mode for evaluating output pairs. | Passed | Passed |
| Configures blind mode to hide model identities. | Passed | Passed |
The instructions your model reads when the skill loads.
Build custom annotation UIs for human review of agent outputs and traces.
Generate a self-contained HTML annotation interface tailored to a specific review task. Supports reviewing LLM outputs, agent execution traces, side-by-side comparisons, and data labeling. The interface loads data from a JSONL file, presents items one at a time with the configured annotation controls, tracks progress, and exports labeled results. No server required — runs entirely in the browser from a single HTML file.
# Build review UI for eval outputs
/review-interface --data eval-results.jsonl --task "rate output quality" --labels pass,fail
# Build comparison UI (A vs B)
/review-interface --data comparisons.jsonl --task "which response is better" --mode compare
# Build trace review UI
/review-interface --data agent-traces.jsonl --task "identify failure point" --mode trace
# Custom annotation schema
/review-interface --data outputs.jsonl --schema annotation-schema.yaml
# Build with pre-filled labels (for review/correction)
/review-interface --data labeled.jsonl --labels pass,fail --prefilled
| Flag | Type | Default | Description |
|---|---|---|---|
--data | string | required | Path to JSONL data file to review |
--task | string | required | Description of the review task (shown in UI header) |
--labels | string | — | Comma-separated label options (e.g., pass,fail or good,okay,bad) |
--mode | enum | single | Review mode: single (one item), compare (A vs B), trace (step-by-step) |
--schema | string | — | Path to custom annotation schema (YAML) |
--prefilled | flag | false | Load existing labels for review/correction |
--output | string | review-interface.html | Output HTML file path |
--items-per-page | int | 1 | Items shown per page |
--randomize | flag | false | Randomize item presentation order |
test cases passed, vs 12 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.