SKILLEMALL.ai

AC streaming-buddy

Personal streaming assistant with learning preferences. Tracks what you're watching, learns your taste, and suggests what to watch next based on your services, mood, and preferences. Use when asked about movies, TV shows, streaming services, what to watch, or tracking viewing progress. Triggers: /stream, 'what should I watch', 'recommend something', mentioning Netflix/Prime/Disney+/Apple TV+, asking about series/seasons/episodes, mood-based requests like 'something exciting'.

ClawHub Agent Skills author: udiedrichsen v2.0.0 6 files · 1 script body ≈ 1 442 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
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: 6. 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 51/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 34 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1442 tokens
    • low 12 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (4 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 480: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 34 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: suspicious
    This streaming assistant mostly does what it claims, but it deserves review because it stores a TMDB API key and personal viewing profile locally while sending viewing-related requests to TMDB.
    LLM: suspicious (high) · VirusTotal: benign · 10 Sept 2026