BB asr-transcribe-to-text
Transcribe audio/video to speaker-labeled text — who-said-what by default, plain-text opt-out; MLX-local on Apple Silicon or remote; local files, media URLs. Use for transcribing recordings/podcasts/lectures/meetings, ASR, speech-to-text, 转录, 语音转文字, 录音转文字, speaker diarization/说话人分离/识别/谁在说话, timestamps 字幕/时间戳/音画对齐, CAM++ voiceprint ID. This skill ALSO owns audio PREPROCESSING for ASR as a first-class trigger, even without transcription: convert any audio/video into an ASR-ready file (转换成适合 ASR 的格式, 转格式, convert/prepare audio for ASR, 音频预处理), downsample to 16kHz mono 16-bit (降采样, 重采样, 单声道, 归一化), merge multi-segment recorder dumps (多段合并/拼接, DJI TX01/TX02), transcode to small M4A + pitch-preserved speedup to cut metered-ASR billed minutes (转 M4A, 压缩上传, 加速, 1.3x, 飞书妙记/Feishu Minutes). Trigger even when it looks like a trivial one-line ffmpeg — the skill owns sample-rate/bit-depth/channel, merge-order, speed-vs-WER, format choices + a blessed prepare_asr_input.py.
Transcribe audio/video to speaker-labeled text — who-said-what by default, plain-text opt-out; MLX-local on Apple Silicon or remote; local files, media URLs.
As a process B 69/100 · Nearly there — weak spots: inputs and preconditions, execution cost
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 21. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 13094 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 753, 877, 1010, 1018): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 69/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 40Execution cost. Instruction body is 13094 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 100Steps. 77 steps
- 100Failures and branches. 6 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 17 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (5 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 972: 120–800 characters recommended
- -2localhost URLs: will not work for another user
- -31 of 14 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +4Structure: 26 headings
- +3Step-by-step instructions: 77 items
- +3Output format is stated explicitly
- +4Has examples (43 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.