BC voice-matched-content
Extract someone's authentic writing voice from samples, build a complete Voice DNA profile, then generate content that sounds like them — not AI. Covers confidence calibration, energy mapping, transition patterns, audience adaptation, and platform-specific voice tuning. Triggers on: capture my voice, write like me, voice guide, brand voice, sound like me, voice profile, my writing style, content in my voice, doesn't sound like me, too AI.
Extract someone's authentic writing voice from samples, build a complete Voice DNA profile, then generate content that sounds like them — not AI.
As a process C 63/100 · Has gaps — weak spots: result and completion, failures and branches, consistency
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 63/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 4 mutating operations with no state check
- 40Consistency. Frontmatter name (voice-matched-content) differs from the folder (voice-matched-content-system)
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 103 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 3134 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 442: enough signal without eating the budget
- +4Structure: 32 headings
- +3Step-by-step instructions: 103 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.