SKILLEMALL.ai

AD readai

Fetch and manage Read AI meeting data - summaries, transcripts, action items, and engagement metrics. Use when the user asks about meetings, meeting notes, meeting summaries, action items from meetings, who said what in a meeting, meeting transcripts, Read AI data, or wants to set up Read AI webhooks. Also use for searching past meetings, generating meeting digests, or pulling meeting analytics.

ClawHub Agent Skills author: Brandon Stewart v1.0.0 MIT-0 8 files body ≈ 762 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 8. 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 49/100

    • 0Result and completion. Does not say what the result is
    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 762 tokens

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 398: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 5 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    This skill handles useful meeting data, but it also reaches broad always-on conversation lifelogs and stores or forwards sensitive content with incomplete disclosure and controls.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026