SKILLEMALL.ai

AB claw-self-improving-plus

Turn raw mistakes, corrections, discoveries, and repeated decisions into structured learnings and promotion candidates. Use when the user wants a conservative self-improvement workflow that captures lessons, scores reuse value, deduplicates similar learnings, drafts anchored candidate patches for SOUL.md, AGENTS.md, TOOLS.md, or MEMORY.md, reviews them through an approval step, and keeps human control before any long-term file edits.

ClawHub Agent Skills author: TimothySong v1.0.2 MIT-0 22 files body ≈ 1 307 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 73/100 · Nearly there — weak spots: inputs and preconditions

ProcedureInfrastructureAI and agentsLearningtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
B
73/100
Nearly there
Inputs and preconditions w 11
0
Tools and files w 18
60
Result and completion w 14
60
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: 22. 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 73/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 81 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1307 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 437: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 81 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (3 of 5)
    • +3All 15 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: clean
    This is a local, review-first learning workflow that can update agent memory files only after explicit approval, with no evidence of hidden network, credential theft, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026