SKILLEMALL.ai

BC mastery-assessment

A closed-book-first probing protocol that assesses how well the learner actually holds a module's claims — asking before telling, probing the gap, detecting misconceptions, then confirming. Tags each probe with a Bloom level, places the learner in a Dreyfus mastery band (unfamiliar/aware/functional/proficient/fluent), scores the session, and schedules the next spaced-repetition surfacing on expanding intervals (1d, 3d, 7d, 16d, 35d) — pulling the next date earlier on a miss. Writes an assessment-session note to assessments/log/ and proposes a review-due update on each evergreen note touched. Use when checking mastery of a module, running a spaced-repetition review, or gating phase progression. Trigger keywords — assess mastery, review-due, spaced repetition, Bloom check, mastery band, am I ready to move on.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 2 476 tokens Open the sourcegithub.com↗ analyzed 16 h ago

A closed-book-first probing protocol that assesses how well the learner actually holds a module's claims — asking before telling, probing the gap, detecting…

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

AnalyzerLearningAI and agentsPeople and hiringtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
When it triggers w 12
20
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 60/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 23 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2476 tokens
    • 100Progress reporting. Reports progress

    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)
    • +3Description length 818: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 23 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.