SKILLEMALL.ai

BC 10-days-to-faster-reading

Abby Marks Beale's "10 Days to Faster Reading" — a self-paced 10-day program to double or triple reading speed while maintaining or improving comprehension. Covers pacing techniques, reducing subvocalization/regression/mind-wandering, widening eye span, previewing, skimming, and flexible reading rates. Covers 5 use cases: ① Speed reading fundamentals — ("how to read faster" "speed reading" "double reading speed" "words per minute") ② The 3 passive habits — ("stop subvocalizing" "stop regression" "stop mind wandering" "mental talking while reading") ③ Pacing techniques — ("hand as pacer" "white card method" "pointer reading" "pacer methods") ④ Reading strategies — ("preview before reading" "skim" "scan" "read key words" "read phrases") ⑤ Flexible reading — ("when to speed up slow down" "reading rates" "purposive reading" "nonfiction reading strategies") Trigger when users say: "speed reading" "faster reading" "how to read faster" "improve reading speed" "comprehension" "subvocalize" "eye span" "preview reading" "skim" "Abby Marks Beale" "slow reader" "reading training"

ClawHub Agent Skills author: BestBooks v1.0.0 MIT-0 8 files body ≈ 2 000 tokens Open the sourceclawhub.ai analyzed 2 d ago

Abby Marks Beale's "10 Days to Faster Reading" — a self-paced 10-day program to double or triple reading speed while maintaining or improving comprehension.

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureLearningPersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
C
60/100
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

  1. Shorten the description to 1024 characters.
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: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1084 chars, limit 1024

Process rating: all ten parameters 60/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 44 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2000 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 1084: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 31 example trigger phrases
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 44 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (4 of 5)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: clean
This is a text-only speed-reading coaching skill, with only a minor risk of being invoked too broadly.
LLM: benign (high) · VirusTotal: · 9 Jun 2026