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Full evaluation workflow - launch a run, watch progress, and summarize results. Use for end-to-end agent testing.
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Basé sur la classification professionnelle SOC
| name | quick-eval |
| description | Full evaluation workflow - launch a run, watch progress, and summarize results. Use for end-to-end agent testing. |
| argument-hint | [agent] [test-set] |
Run a complete evaluation for $ARGUMENTS: launch, monitor, and summarize results.
List and confirm resources:
coval agents list
coval test-sets list
coval personas list
Confirm with user:
coval runs launch \
--agent-id <agent_id> \
--persona-id <persona_id> \
--test-set-id <test_set_id> \
--name "Quick Eval - $(date +%Y%m%d-%H%M)"
Capture the run ID from output.
coval runs watch <run_id>
Wait for completion.
coval runs get <run_id> --format json
coval simulations list --run-id <run_id> --format json
Present a summary:
## Evaluation Complete
**Run:** <run_id>
**Agent:** <agent_name>
**Test Set:** <test_set_name>
**Duration:** X minutes
### Results
- Total Simulations: N
- Completed: N
- Failed: N
### Sample Simulations
[List 3-5 simulation IDs for review]
### Next Steps
- View full results: `coval simulations list --run-id <run_id>`
- Download audio: `coval simulations audio <sim_id> -o recording.wav`
- Get transcript: `coval simulations get <sim_id>`
End-to-end Coval adversarial / red-team testing workflow. Builds one adversarial test set (~10 bad-actor scenarios, each with an expected-behavior checklist), creates a persistent "Adversarial User" persona and a Composite Evaluation metric that scores each scenario against its own expected behaviors, launches a multi-iteration run against the agent (voice or chat), polls for completion, builds a per-scenario pass/fail scorecard, and creates a saved report grouped by Test Case. Use when a user wants to follow the Adversarial & Red-Team Testing cookbook (https://docs.coval.dev/guides/adversarial-red-team-testing) without doing each step by hand. Triggers: "adversarial test set", "red team my agent", "jailbreak / prompt-injection testing", "test my agent against bad actors".
Analyze a Coval adversarial / red-team testing report and turn it into an agent-hardening plan. Use when a user provides a Coval report URL, report export, run IDs, screenshots, or a per-scenario scorecard from an adversarial sweep and wants evidence-backed next steps such as prompt/guardrail changes, refusal hardening, verification fixes, escalation routing, or expanded attack coverage.
Derive a SET of simulation personas for an agent from product artifacts — backend payloads, UI screenshots, journey/product docs, and sample real user messages — instead of designing one persona by hand. Identifies who actually interacts with the agent and how they behave, then creates the personas via the CLI. Best for text/chat agents and for new agents with no interaction history. Use when the user says "make personas from these screenshots/payloads", "who are my users", "create a set of personas", "derive personas from my product", "build a persona library", or "I have backend data, turn it into personas".
Turn a large dataset (an existing oversized Coval test set, an export of past conversations, or a CSV/JSON of cases) into a small, high-signal Coval test set by removing duplicates, identifying unique scenarios, and selecting a representative, failure-weighted subset — then bulk-loading it with no row cap. Use when the user says "I have thousands of cases", "dedupe my test set", "my test set is too big", "turn this dataset into a test set", "pick representative scenarios", or "my CSV import only kept 10 / uploaded everything".
Analyze a Coval accent testing report from runs across different speaker accents. Use when a user provides a Coval report URL, report export, run IDs, screenshots, or metric summary and wants evidence-backed next steps such as prompt changes, STT/confirmation adjustments, accent-robust routing, or expanded accent coverage.
End-to-end Coval accent testing workflow. Creates one persona per accent (each using a distinct accent voice and mirroring your Standard Customer behavior), launches one run per accent against the same voice agent + test set + metrics, polls for completion, builds a per-persona comparison table from the results, and creates the saved multi-run report (grouped by Persona) via the public API. Use when a user wants to follow the Testing Across Accents cookbook (https://docs.coval.dev/guides/testing-across-accents) without doing each step by hand.