SKILLEMALL.ai

AC spaced-repetition-teaching

Adaptive spaced repetition engine using the FSRS-6 algorithm (Free Spaced Repetition Scheduler, Ye et al. 2024). Manages flashcard reviews with scientifically optimal intervals based on memory research. Triggers on: study sessions, flashcard reviews, "what's due today", "review cards", spaced repetition scheduling, and study session management. Developed through the Formation Fellowship technical interview prep program.

ClawHub Agent Skills author: tylerbittner v1.0.0 MIT-0 6 files body ≈ 1 188 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

AnalyzerLearningtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Failures and branches. 2 branches
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 21 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1188 tokens
  • 100Running it twice. Mutating operations check current state

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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 423: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 21 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

External checks

ClawHub: clean
This appears to be a flashcard scheduling skill that updates a user’s own markdown study file, which is expected for its purpose but should be clearly understood before use.
LLM: benign (medium) · VirusTotal: · 29 May 2026