CC digital-twin
Build a digital twin of yourself, meaning photoreal stills of you in your own real room, talking video clips of you, and lines in your own cloned voice, that people who know you accept as you. It's a proven workflow (160+ numbered stills over two dozen rounds, 20+ talking drafts and a voice taken from 8.7 to 9.2/10 on one creator's twin, Nov 2026). It covers consent, picking references from your own recordings, writing an identity prompt, the 4-part prompt stack on Seedream 5 Pro, your-real-room-only with the camera as the only variable, 3 images at a time, cinematic looks taken from reference videos as words, measuring face proportions, the audit and paste-back fix, an instant voice clone with an audio tag, a speaker-similarity scorer, Seedance 2.5 draft talking clips, and the traps (voice verification lockouts, iMacs, grey hair, wide mouths, other people's faces bleeding in). Use it whenever someone wants AI images, video or voice of THEMSELVES, even if they only say "my digital twin", "AI headshots of me", "a video of me talking", "clone my voice", "make me a presenter clip", "an AI avatar of me", "me at my desk", "a talking-head intro without filming" or "creator or founder content with my face". Only for your own face and voice, or someone who gave documented consent.
Build a digital twin of yourself, meaning photoreal stills of you in your own real room, talking video clips of you, and lines in your own cloned voice, that…
As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
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".
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
- Shorten the description to 1024 characters.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
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medium Exfiltration
net-credential-usescripts/voice/el.sh:11Credential used in a network call (verify the destination is the intended service)curl -sS -X "$m" -H "xi-api-key: $ELEVENLABS_API_KEY" "https://api.elevenlabs.io$p" "$@"
Files scanned: 39. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1293 chars, limit 1024 - warning
body-longSKILL.md body ≈ 5146 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 2 mutating operations with no state check
- 60Tools and files. Uses tools (web, python, node) that frontmatter does not declare
- 70Execution cost. Instruction body is 5146 tokens
- 100Steps. 63 steps
- 100When it triggers. States when to use and when not to
- 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 14 top-level sections: this looks like several domains in one skill
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
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)
- +3Description length 1293: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +5Description quotes 9 example trigger phrases
- +4Structure: 15 headings
- +3Step-by-step instructions: 63 items
- +4Reference files are cited in the instructions (13 of 13)
- +3All 12 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 54.