Content
86%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, highly actionable reference skill with executable examples and clean progressive disclosure into real bundle files. Minor conciseness trims and an explicit validation/retry loop for realtime streaming would push it higher.
Suggestions
Tighten explanatory section intros (e.g., the Keyterm Prompting and Diarization preambles) to pure action descriptions.
Add an explicit validate/retry checkpoint for the realtime streaming workflow (e.g., handle `error` events, reconnect on disconnect) to strengthen workflow clarity.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is largely lean and code-driven, assuming Claude's competence, but a few section intros add light explanation (e.g., "Help the model recognize specific words it might otherwise mishear...") that could be trimmed. | 4 / 5 |
Actionability | It provides copy-paste-ready, executable examples across Python, JavaScript, and cURL covering the common cases (basic transcription, timestamps, diarization, keyterms, language detection, realtime streaming). | 5 / 5 |
Workflow Clarity | The main transcription flow is an unambiguous single call and the realtime section has a clear connect-stream-handle-event sequence, but there are no explicit validation checkpoints or error-recovery feedback loops for the streaming workflow. | 4 / 5 |
Progressive Disclosure | The body keeps core usage inline and points to six real, one-level-deep reference files (installation, transcription-options, realtime client/server, commit strategies, events) via a clearly signaled References section, all of which exist on disk. | 5 / 5 |
Total | 18 / 20 Passed |