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AB video-clip

Use when the user wants to trim, cut, or extract a specific segment from a video by time range — e.g. "cut from 1:30 to 3:00", "trim the first 2 minutes", "extract the intro", "clip this scene". Runs locally with ffmpeg, no API key required, instant results. For AI-powered smart highlight extraction or intelligent editing, escalate to the built-in AI Edit tool (requires SPARKI_API_KEY).

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files · 1 script body ≈ 1 554 tokens Open the sourcegithub.com↗ analyzed 3 d ago

g. "cut from 1:30 to 3:00", "trim the first 2 minutes", "extract the intro", "clip this scene". Runs locally with ffmpeg, no API key required, instant…

As a process B 74/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

IntegrationMedia and videotype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
90
Quality 40%
91
Run on models
none yet
Process rating
B
74/100
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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
    • medium Exfiltration net-credential-use SKILL.md:128
      Credential used in a network call (verify the destination is the intended service)
      ST=$(curl -sS "${SPARKI_API_BASE}/business/assets/${OBJECT_KEY}/status" -H "X-API-Key: $SPARKI_API_KEY" | jq -r '.data.status // "unknown"')
    • medium Exfiltration net-credential-use SKILL.md:154
      Credential used in a network call (verify the destination is the intended service)
      PRESP=$(curl -sS "${SPARKI_API_BASE}/business/projects/${PROJECT_ID}" -H "X-API-Key: $SPARKI_API_KEY")

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "display_name"

    Process rating: all ten parameters 74/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 9 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1554 tokens
    • low The response is described with custom markup (3 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

    • +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
    • +5Description quotes 4 example trigger phrases
    • +3Description length 389: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (5 code blocks)
    • +3All 1 scripts are documented

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