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speech-generation

Convert text to speech using ElevenLabs voice AI through Letta. Use when generating audio from text, sending a voice message, reading text aloud, creating voiceovers, speaking in your own voice, or synthesizing speech in 90+ languages.

Quality

92%

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SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable, well-structured reference: complete executable examples for every feature, explicit success validation and error-recovery guidance, and a clean one-level-deep split of voice-setting detail into a real bundle file. The only notable flaw is minor duplicated explanation in the Voice Settings section that costs tokens without adding information.

Suggestions

Remove the duplicated stability/similarity_boost re-explanation after the Voice Settings JSON example (lines 99-102) since the bullets above already define both parameters.

Trim the "Direct delivery with audio tags such as..." aside in the Models v4 note, since the dedicated Audio Tags section covers it in full.

DimensionReasoningScore

Conciseness

The body is dense with decision tables and copy-paste requests and never explains concepts Claude already knows, but the Voice Settings section explains stability and similarity_boost twice (prose bullets, then the lines "`stability`: lower = more expressive... / `similarity_boost`: higher = closer to the reference voice" after the JSON). These minor duplicated explanations could be trimmed, fitting the efficient-with-minor-overexplanation anchor 4 rather than the every-token-earns-its-place anchor 5.

4 / 5

Actionability

The Quick Start is a complete, executable curl command with env-var handling, header dump, and status output, and every feature section (voice settings, language code, text normalization, stitching, output formats) ships a ready-to-send JSON body. Error handling maps concrete status codes to concrete responses, matching the fully-executable copy-paste anchor.

5 / 5

Workflow Clarity

The single-action flow is unambiguous with explicit validation (a 200 with audio content type means success; otherwise read the JSON error) and feedback loops for recovery (retry once on 500, swap an audio tag and regenerate on a missed delivery). It is not a destructive or batch operation, so no cap applies, and this exceeds the simple-skill baseline.

5 / 5

Progressive Disclosure

The body is a well-sectioned overview, and the one bundle reference, references/voice-settings.md, is real, exactly one level deep (it points only to ../LICENSE), and clearly signaled both inline and in a References section. Detail like parameter ranges and use-case presets is appropriately split out of the main file, matching the clear-overview anchor.

5 / 5

Total

19

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description with an explicit what-plus-when structure and multiple natural trigger phrases that mirror the rubric's top completeness example. The only room for improvement is enumerating a broader set of capabilities and adding trigger synonyms such as "TTS" or "narration".

Suggestions

Add one or two more capability phrases to the what clause (e.g. "tune voice settings and pick latency/quality models") to round out capability coverage.

Include a couple of missing natural trigger synonyms, such as "TTS" or "narration", alongside the existing use-when list.

DimensionReasoningScore

Specificity

"Convert text to speech" names the core action, and the use-when list enumerates concrete tasks ("sending a voice message", "creating voiceovers", "speaking in your own voice"). Several specific actions are present, but capability coverage has minor gaps (no mention of tuning voice settings or choosing models/latency tradeoffs), so it sits below the comprehensive anchor 5 and above the 1-2-actions anchor 3.

4 / 5

Completeness

It explicitly answers both parts: "Convert text to speech using ElevenLabs voice AI through Letta" (what) and an explicit "Use when" clause with six concrete trigger phrases (when), matching the top anchor's structure.

5 / 5

Trigger Term Quality

Natural trigger phrases are well covered: "text to speech", "voice message", "reading text aloud", "voiceovers", "synthesizing speech in 90+ languages". A few common terms users would say are missing (e.g. "TTS", "narration", "audiobook"), which keeps it just below the comprehensive-synonym anchor 5.

4 / 5

Distinctiveness Conflict Risk

The text-to-speech niche is clear and triggers ("voice message", "voiceover", "reading text aloud") are distinct from adjacent audio/ speech skills, so the risk of firing the wrong skill is minimal.

5 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

letta-ai/letta-code

Path

src/skills/builtin/speech-generation/SKILL.md

Reviewed

Table of Contents

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