SKILLEMALL.ai

AB hizal-write

Persist what the agent learns as it builds. Self-triggering — fires whenever the agent makes a decision, discovers a pattern, learns a convention, hits a gotcha, or gains knowledge worth keeping. Use continuously, not just at the end. Triggers on phrases like "good to know", "I'll remember that", "worth noting", "that's a pattern", "lesson learned", "this is how we do it", or any moment the agent learns something it would otherwise forget.

ClawHub Agent Skills author: Parker v0.1.0 MIT-0 2 files body ≈ 893 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 66/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
B
66/100
Nearly there
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

    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: 2. 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 66/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 17 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 893 tokens
    • high The skill tells the model to perform an irreversible action with no human approval

    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 6 example trigger phrases
    • +3Description length 443: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 17 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: suspicious
    This skill has a clear memory-writing purpose, but it tells the agent to save information automatically and broadly without enough user control or sensitive-data safeguards.
    LLM: suspicious (high) · 28 May 2026