SKILLEMALL.ai

AC cogdx-feedback

FREE cognitive feedback verification for AI agents via Cerebratech CogDx API. Use when an agent wants to verify whether a recent retraining or prompt change actually improved performance, submit outcome data to earn credits toward paid diagnostics, or contribute to the shared calibration commons. No payment required. Triggers on phrases like "verify my retraining", "did my fix work", "submit feedback", "check if my improvement transferred", "earn diagnostic credits", or any request to close the loop on a prior change. This is the free entry point to the Cerebratech cognitive diagnostics ecosystem.

ClawHub Agent Skills author: Dr Amanda Kavner v1.0.1 MIT-0 3 files body ≈ 597 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 61/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, progress reporting

IntegrationAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
C
61/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "repository"

    Process rating: all ten parameters 61/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (bash, web) 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. 18 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 597 tokens
    • 100Running it twice. No mutating operations

    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)
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 604: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 18 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: clean
    This is an instruction-only feedback skill that openly sends AI evaluation data to Cerebratech, so it is coherent but should be used only with reviewed, non-sensitive payloads.
    LLM: benign (high) · VirusTotal: · 29 May 2026