SKILLEMALL.ai

AB video-proof

Record video proof of implemented features after coding tasks complete. Use when a coding agent finishes work and needs to visually verify and demonstrate that the feature works. Generates screen recordings, screenshots, and test logs as PR artifacts. Integrates with any coding agent workflow — run after code is written to produce video evidence of working software. Trigger on "video proof", "record demo", "prove it works", "show me it working", or when PRDs include a proof/demo step.

ClawHub Agent Skills author: rikisann v1.0.2 8 files · 1 script body ≈ 1 356 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 67/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

IntegrationSoftware developmentMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
99
Quality 40%
95
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: video-proof (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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token scripts/package-lock.json:19
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…uWm+fFRcIOgKBMiOBP+eXiy…9ab+DDKA==",
      detector

    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 67/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 5 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1356 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 489: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 5 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.

    External checks

    ClawHub: suspicious
    The skill appears to do what it claims, but it gives proof specs broad command, network, install, and artifact-sharing authority without enough safeguards.
    LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026