SKILLEMALL.ai

AC loom-workflow

AI-native workflow analyzer for Loom recordings. Breaks down recorded business processes into structured, automatable workflows. Use when: - Analyzing Loom videos to understand workflows - Extracting steps, tools, and decision points from screen recordings - Generating Lobster workflow files from video walkthroughs - Identifying ambiguities and human intervention points in processes

ClawHub Agent Skills author: G9Pedro v1.0.1 7 files body ≈ 614 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

AnalyzerMedia and videoInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
98
Quality 40%
81
Run on models
none yet
Process rating
C
58/100
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Obfuscation obf-base64-blob test-output/video.info.json:1
      Long base64-looking blob (quoted — discussed, not commanded)
      {"id": "a79e…edd", "duration": 3943, "chapters": [{"start_time": 0.0, "title": "Introdução ao Workflow", "end_time": 101.0}, {"start_time": 101.0, "title": "Diferença de Tipos 
      quoted
    • low Secrets in code secret-high-entropy-token test-output/video.info.json:1
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      {"id": "a79e…edd", "duration": 3943, "chapters": [{"start_time": 0.0, "title": "Introdução ao Workflow", "end_time": 101.0}, {"start_time": 101.0, "title": "Diferença de Tipos 
      detector

    Files scanned: 7. 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 58/100

    • 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. 1 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 25 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 614 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
    • -33 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 385: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: suspicious
    This skill has a coherent Loom workflow purpose, but it under-discloses sensitive video data handling and can generate unsafe executable workflows from untrusted analysis content.
    LLM: suspicious (high) · VirusTotal: suspicious · 10 Sept 2026