BD Writing
Authors and assembles prose from the principal's material, audits AI patterns, and scores with Pangram. USE WHEN assemble a transcript, shape dictated notes, organize rough spoken material, write as me, write in my voice, sound like me, this does not sound like me, fix the voice, remove AI-isms, audit writing, clean up AI writing, check for AI patterns, sound less like AI, show me the draft, preview the article, open the draft properly. NOT FOR detection only (DetectAI), fiction (WriteStory), placing a finished blog post, or writing social posts.
Promises prose assembly from raw material, AI pattern auditing, and Pangram scoring. Designed for reshaping dictation, organizing rough notes, matching your voice. Contains 16 files and 5 scripts with 86 quality score. No critical errors, but process score sits at 47—logic works, documentation lags.
In use: takes messy text, removes AI-isms, flags unnatural phrasing, previews the result. Supports major platforms from Claude to DeepSeek. Won't help with AI detection or fiction. Worth installing if you dictate notes or assemble articles from fragments—it'll make your voice consistent and scrub out the machine.
Authors and assembles prose from the principal's material, audits AI patterns, and scores with Pangram.
As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- 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: 16. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - note
frontmatter-keyunknown frontmatter key "evals" - note
edit-residuethe text marks something as outdated (lines 24): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 47/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 4 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1437 tokens
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
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 552: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 4 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.