novasynth-assist

NovaSynth assist

Two jobs, run independently or back-to-back:

1. Validate scenarios — schema + structural sanity check on generated scenarios before upload.
2. Audit a batch run (steps 5–7) — fetch transcripts → check each call against the PRD → cross-check the platform scorers.

This skill exists because the analysis half of the loop is full of gotchas (REST quirks, oversized payloads, and scorers that systematically misjudge hard compliance gates). Encode the rules below; don't rederive them.

Usage

/novasynth-assist validate <scenarios.json>                          # job 1 only
/novasynth-assist audit <batchRunId> --prd <PRD.txt> --project <projectId>   # jobs 5-7
/novasynth-assist audit <batchRunId> --prd <PRD.txt> --project <projectId> --run <runId>  # one run only

Setup — auth & base URL

• REST base: https://api.noveum.ai/api/v1
• Bearer key precedence: $NOVEUM_API_KEY (environment or a local .env) → a project-scoped <PROJECT>_NOVEUM_API_KEY for multi-project setups → the noveum MCP config in ~/.claude.json (mcpServers.noveum headers/env). Never hardcode the key in this file.
• Run-detail URL shown to the user: https://noveum.ai/app/<org>/projects/<projectId>/novasynth/runs/<batchRunId>/<runId>

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Job 1 — Validate scenarios

Input: a scenarios.json (list of scenario objects, each with events = nodes with id / parent_id / action / condition / fixed and scenario-level metadata).

Checklist per scenario:

1. Schema — every event has id + action, ids unique; parent_id is null for the root; condition is optional (backward-looking; null when gated only by the parent edge); every non-root parent_id resolves to an existing id (no orphans, no cycles).
2. Reachability — every event is reachable from the root via parent_id.
3. Intent match — the events actually exercise what the scenario name/metadata claims (e.g. a "third_party_pickup" tag must have a third-party event, not happy-path events). This is the check that caught the broken S24 last time.
4. Coverage tag — metadata (e.g. loop:N_handle_concerns:many, pb:PB_third_party_pickup) is consistent with the events.

Fan out one subagent per scenario for large sets; report a table of scenario | PASS/FAIL | issue. Do NOT upload failing scenarios.

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Jobs 5–7 — Audit a batch run

Step 5 — Fetch the batch + runs (mind the REST quirks)

BASE=https://api.noveum.ai/api/v1 ; KEY=<from config> ; PROJ=<projectId> ; BATCH=<batchRunId>
mkdir -p MOFSL/audit_runs   # or any scratch dir

• Batch detail (status, run counts, persona/scenario mapping): GET $BASE/novasynth/batch-runs/$BATCH → gives scenarios[].scenarioId, personas[], runSummary.
• Per-run detail — GOTCHA: runs/{id} 400s without projectId. The MCP tool getApiV1NovasynthRunsById does NOT pass it and fails. Use curl: curl -s "$BASE/novasynth/runs/$RID?projectId=$PROJ" -H "Authorization: Bearer $KEY" Save each to a file. Key fields: .run.status, .run.transcriptNormalized.turns, .run.traceId, .run.executionTimeMs, .run.error.
• Batch analysis (scorer results) — GOTCHA: payload is huge (~90k+ chars), blows the token limit. Fetch via MCP getApiV1NovasynthBatch-analysisByBatchRunId (it auto-saves to a file) or curl to a file, then jq — never inline. Per-run scorers live at .body.analyses[] | select(.runId==$RID) | .scorerResults (object keyed by scorer name → {score, passed, reasoning, scorerType}).

Map each runId → scenario name (via batch scenarios[].scenarioId), then extract clean transcripts and per-run scorer files:

# clean transcript (works for Hindi/Devanagari too)
jq -r '.run.transcriptNormalized.turns[] | "[\(.speaker)] \(.message)"' run_$RID.json > transcript_$NAME.txt
# per-run scorers
jq --arg r "$RID" '.body.analyses[]|select(.runId==$r)|{overall:.score,status,scorers:(.scorerResults|to_entries|map({name:.key,score:.value.score,passed:.value.passed,type:.value.scorerType,reasoning:.value.reasoning}))}' analysis.json > scorers_$RID.json

A run with status:failed and turns:0 is an infra failure (LiveKit auth/audio init, timeout at ~600s, traceId:null) — no transcript, report it as needs-re-run, not a bot fault.

Step 6 — Audit each transcript against the PRD

Fan out one subagent per run (keeps context lean; transcripts are long and often Hindi). Give each subagent: the PRD path, its transcript_$NAME.txt, its scorers_$RID.json, and the specific PRD rule the scenario tests. Ask each for:

1. ~5-line plain-English summary of the call.
2. Did the bot violate the rule under test? Quote exact turns (with translation if non-English).
3. VERDICT: PASS/FAIL for the bot, one-line reason.
4. Scorer cross-check (Step 7 rules below) — does each relevant scorer's verdict match the independent read?

Step 7 — Scorer cross-check (the cheatsheet)

The platform's overall pass/fail is not trustworthy for compliance gating. Apply these known patterns:

FALSE PASS — LLM-judge scorers under-penalize hard binary gates. instruction_adherence_scorer (and content_safety, answer_refusal) will detect a gate/disclosure violation in their own reasoning, then call it a "minor deviation" and score 8–9 PASS. Seen repeatedly: skip-qualifying-gate before specialist transfer, and disclosing lead info before identity confirmation. A hard gate (e.g. "all 3 qualifying Qs + PAN before transfer") cannot be a soft judge score — read the reasoning, not the number, and override to FAIL when the reasoning admits the violation. (See the user's project_judge_model_calibration memory: weak judges cluster high.)

FALSE FAIL / not-a-bot-fault — discount these:

• item_summary_scorer = -1 → "fewer than 3 summary points" infra error, not a bot fault. Often the thing that flips a run to status:failed despite clean content.
• Audio scorers = -1 (mos, gibberish, stt_efficacy, tone_clarity, pronunciation) → "length limit exceeded" / Converse infra failures.
• sentiment_csat / appropriate_call_termination / drop_off_node low on a refusal or adversarial call is EXPECTED — a graceful "customer said no" call should score low on satisfaction/resolution without that being a bot defect. Distinguish "bot handled refusal correctly" from "low CSAT."
• assistant_average_pitch flags ~247 Hz against a 100–220 Hz band → band is miscalibrated for a female voice; false fail.
• assistant_latency is a TTS/infra metric, not conversational logic.

TRUST these when they fail: instruction_adherence failing on a tip-leak/over-promise, is_harmful_advice, hallucination, content_moderation, answer_relevancy (for genuine incoherence/digression). Cross-check their reasoning still points at a real bot turn.

Final report format

Per run: one-line issue + bot PASS/FAIL + run URL. Then a synthesis that separates:

• Hard compliance failures (gate skips, trade-tip/guarantee leaks, disclosure) — the real story.
• Soft/debatable (under-engagement, barge-in/interruptions — note that an interrupted user turn is the bot interrupting, not a sim artifact).
• Scorer trust note — explicitly call out any run the dashboard PASSED that is actually a FAIL (the dangerous false-passes), so the user doesn't ship on a green dashboard.