SKILLEMALL.ai

AC autoloop-controller

When continuous automated improvement of a Skill is needed. Wraps improvement-orchestrator in a persistent loop with convergence detection (plateau/oscillation), cost control, and cross-session state persistence. Not for single-shot improvement (use improvement-orchestrator) or quality scoring (use improvement-learner).

ClawHub Agent Skills author: _silhouette v1.1.1 MIT-0 15 files · 1 script body ≈ 3 184 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
When it triggers w 12
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: autoloop-controller (ClawHub)

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: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "triggers"

    Process rating: all ten parameters 63/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3184 tokens
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • -33 of 5 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 321: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 22 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is a disclosed local automation loop for improving skills, with clear budget and stop controls, but it should only be run with explicit targets and limits.
    LLM: benign (high) · VirusTotal: · 29 May 2026