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Get Started Free →Optimize AssemblyAI costs through model selection, feature budgeting, and usage monitoring. Use when analyzing AssemblyAI billing, reducing transcription costs, or implementing usage monitoring and budget alerts. Trigger with phrases like "assemblyai cost", "assemblyai billing", "reduce assemblyai costs", "assemblyai pricing", "assemblyai budget".
.claude/skills/jeremylongshore-assemblyai-cost-tuning/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 29% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 96% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 97% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 114% | 0% |
Control spend with dated prices and measured workload usage. Keep quality, privacy, reliability, credentials, and destructive state as separate gates.
Pre-recorded billing follows successfully processed audio duration and selected models or add-ons; published guidance says failed transcripts are not charged. Streaming follows session duration, so termination matters. LLM Gateway has separate economics. Prices are time-sensitive and belong in reviewed billing inputs.
For live work, inject ASSEMBLYAI_API_KEY from an approved secret manager and send the raw value only in the AssemblyAI Authorization header to the configured first-party host. Never print, commit, place in a URL, or expose it to an untrusted client. Callback secrets and temporary streaming tokens are separate credentials.
Use Read, Glob, and Grep to inspect repository code, configuration, fixtures, and evidence. Use Write and Edit only for approved implementation or documentation changes. Do not call AssemblyAI, upload audio, open a streaming session, mint a token, replay a callback, deploy, rotate a key, or delete a transcript merely because this skill was invoked.
Require an accountable owner before live audio processing, production credential or endpoint changes, paid model or capacity changes, content retention, callback replay, deployment, or deletion. Read-only repository inspection and synthetic offline validation do not authorize live vendor actions.
Return the operation scope, environment, region, contract surface, authorization class, model and feature decisions, deterministic validation results, content-free identifiers, risks, cleanup or rollback state, and a concise pass/fail receipt. Exclude credentials, signed URLs, audio, transcript text, prompts, and customer-derived content.
Rerun the smallest relevant deterministic check, compare actual state with the requested outcome and current first-party contract, verify sensitive fields are absent from evidence, and confirm rollback, termination, or deletion state before reporting success.
Review the dated first-party evidence map before relying on any model, parameter, limit, price, region, or lifecycle claim.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-09 | pass→pass | 9,398 | 7,917 | -16% | 1 | 1 | 0% | 1,874 | 3,980 | +112% | 0 | 0 | — |
case-01 | fail→pass | 16,332 | 13,618 | -17% | 1 | 1 | 0% | 3,671 | 5,340 | +45% | 0 | 0 | — |
case-02 | fail→pass | 19,283 | 13,945 | -28% | 1 | 1 | 0% | 4,259 | 5,480 | +29% | 0 | 0 | — |
case-03 | fail→pass | 9,128 | 5,752 | -37% | 1 | 1 | 0% | 1,780 | 3,486 | +96% | 0 | 0 | — |
case-04 | pass→pass | 5,525 | 4,106 | -26% | 1 | 1 | 0% | 1,210 | 3,137 | +159% | 0 | 0 | — |
case-05 | pass→pass | 6,117 | 3,025 | -51% | 1 | 1 | 0% | 1,137 | 2,830 | +149% | 0 | 0 | — |
case-06 | pass→pass | 9,197 | 6,514 | -29% | 1 | 1 | 0% | 1,573 | 3,530 | +124% | 0 | 0 | — |
case-07 | fail→pass | 10,207 | 5,190 | -49% | 1 | 1 | 0% | 1,697 | 3,348 | +97% | 0 | 0 | — |
case-08 | fail→pass | 7,654 | 4,351 | -43% | 1 | 1 | 0% | 1,467 | 3,146 | +114% | 0 | 0 | — |
case-10 | fail→fail | 16,459 | 13,073 | -21% | 1 | 1 | 0% | 3,607 | 5,416 | +50% | 0 | 0 | — |
case-11 | pass→pass | 17,071 | 11,435 | -33% | 1 | 1 | 0% | 2,725 | 4,542 | +67% | 0 | 0 | — |
case-12 | fail→pass | 11,651 | 8,810 | -24% | 1 | 1 | 0% | 2,130 | 4,019 | +89% | 0 | 0 | — |
case-13 | pass→pass | 15,817 | 11,376 | -28% | 1 | 1 | 0% | 3,039 | 4,769 | +57% | 0 | 0 | — |
case-14 | pass→pass | 14,624 | 12,033 | -18% | 1 | 1 | 0% | 2,516 | 4,633 | +84% | 0 | 0 | — |
case-15 | fail→pass | 7,708 | 4,145 | -46% | 1 | 1 | 0% | 1,522 | 3,195 | +110% | 0 | 0 | — |
case-16 | fail→pass | 11,231 | 6,163 | -45% | 1 | 1 | 0% | 1,934 | 3,424 | +77% | 0 | 0 | — |
case-17 | fail→pass | 16,323 | 11,782 | -28% | 1 | 1 | 0% | 2,658 | 4,507 | +70% | 0 | 0 | — |
case-18 | pass→pass | 8,612 | 7,482 | -13% | 1 | 1 | 0% | 1,584 | 3,823 | +141% | 0 | 0 | — |
case-19 | pass→pass | 7,318 | 3,571 | -51% | 1 | 1 | 0% | 1,322 | 2,891 | +119% | 0 | 0 | — |
case-20 | fail→pass | 14,600 | 10,746 | -26% | 1 | 1 | 0% | 2,579 | 4,307 | +67% | 0 | 0 | — |
case-21 | fail→fail | 7,761 | 10,389 | +34% | 1 | 1 | 0% | 1,469 | 4,551 | +210% | 0 | 0 | — |
case-22 | pass→fail | 19,957 | 17,779 | -11% | 1 | 1 | 0% | 4,043 | 5,803 | +44% | 0 | 0 | — |
case-23 | pass→pass | 17,262 | 12,791 | -26% | 1 | 1 | 0% | 3,532 | 4,980 | +41% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +39 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
The publisher has shipped newer versions since this run, so these numbers describe v1, not the version currently listed.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.