SKILLEMALL.ai

AC memory-organizer

Organize, compress, and curate OpenClaw memory without polluting permanent memory. Use when the user wants to compress memory files, clean dated memory notes, reduce startup context, remove redundancy, or keep MEMORY.md focused on must-read long-term facts only. Preserve day-by-day memory files for historical detail, and promote only stable essentials such as user preferences, durable project configuration, and active cross-session todos.

ClawHub Agent Skills author: codeblackhole v1.2.1 5 files body ≈ 868 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ReferenceInfrastructurePersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
63/100
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

    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: 5. 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 63/100

    • 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. 3 mutating operations with no state check
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 45 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 868 tokens
    • 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)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +3Description length 442: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 45 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

    External checks

    ClawHub: clean
    The reviewed skill artifacts are purpose-aligned developer and ClawHub maintenance workflows, with sensitive actions disclosed and guarded by user confirmation or existing auth controls.
    LLM: benign (medium) · VirusTotal: · 29 May 2026