SKILLEMALL.ai

AC defragmenter

Structural knowledge defragmentation for OpenClaw workspaces. Finds information, rules, and operational facts that are spread across the wrong files or embedded in the wrong layer, then rewrites them into the proper files without deleting source material. Use when the workspace feels fragmented, logic is embedded in cron/jobs instead of flow files, preferences are scattered, or facts exist but are not assembled into one coherent working structure.

ClawHub Agent Skills author: Ivan Balukov v1.0.1 MIT-0 2 files body ≈ 1 255 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
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: 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 58/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 48 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1255 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 451: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 48 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This skill only provides instructions for reorganizing workspace knowledge and includes clear dry-run, approval, and write-scope limits.
    LLM: benign (high) · VirusTotal: · 29 May 2026