BC digen-ai
digen-ai is a ClawHub skill that brings directed intelligent generation to your video projects. Upload raw footage and let the AI analyze scene structure, pacing, and narrative flow to produce polished edits without manual timeline work. Key features include scene detection, auto-sequencing, style transfer, and caption injection. Whether you're a content creator, filmmaker, or marketer, digen-ai adapts to your creative intent through plain-language instructions. Supports mp4, mov, avi, webm, and mkv formats.
digen-ai is a ClawHub skill that brings directed intelligent generation to your video projects.
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 5
✓ No critical or high findings
Medium and low: 5
-
low Exfiltration
net-credential-useSKILL.md:89Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)$API = `${NEMO_API_URL:-https://mega-api-prod.nemovideo.ai}`, $TOKEN = `${NEMO_TOKEN}`, $WEB = `${NEMO_WEB_URL:-https://nemovideo.com}`.security skill -
low Exfiltration
net-credential-useSKILL.md:163Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)curl -s "$API/api/credits/balance/simple" -H "Authorization: Bearer $TOKEN" \
security skill -
low Exfiltration
net-credential-useSKILL.md:171Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)curl -s "$API/api/state/nemo_agent/me/<sid>/latest" -H "Authorization: Bearer $TOKEN" \
security skill
A further 2 matches are quotations in this security skill's documentation and are not counted as findings.
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "repository"
Process rating: all ten parameters 55/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 12 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 4037 tokens
- 100Steps. 15 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (5 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
- +1No license
- +2Single-language instructions
- +3Description length 513: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (8 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.