SKILLEMALL.ai

BC reader-first-prose

Rewrites expository prose for a reader meeting the subject for the first time, not an insider who already shares the writer's knowledge. Enforces introduce-before-use (every entity, term and acronym defined on first mention, in an order a newcomer can follow), grounds or cuts every metaphor in the same breath it appears, converts compressed or nominalized logic into a plain if-then, and kills the two loudest machine tells, the em dash and the terse two-beat antithesis ("X. Not Y."). Carries an honesty pass so the prose never out-claims its evidence, marking inference as inference and preserving every number exactly. Use when a draft assumes knowledge the reader lacks, reads as insider or "clever", name-drops terms before defining them, leans on unexplained metaphor, or sounds AI-written; or when the user mentions first-time reader, plain language, define before use, sounds like AI, em dash, too clever, or jargon. Complements ladder-of-abstraction, slop-detector and narrative-fidelity-audit.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 1 833 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Rewrites expository prose for a reader meeting the subject for the first time, not an insider who already shares the writer's knowledge.

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 57/100

    • 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. 8 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 44 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1833 tokens
    • 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 1005: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 44 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.