Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add DDS-Solutions/AI-TadPole-OS --skill digital-twin-voicegit clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OSWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/digital-twin-voice)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/digital-twin-voice"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/digital-twin-voice/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/digital-twin-voice"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/digital-twin-voice.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.01283 |
| Opus 5.5 | $0.00012 | $0.00513 |
| Sonnet 5.5 | $0.00006 | $0.00257 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
digital-twin-voice scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Agent Skills Registry / digital-twin-voice
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Tone drift, robotic/inauthentic persona output, uncalibrated brand voice, hallucinated SMB founder traits.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
Digital Twin Persona & Voice Calibration Protocol
Purpose: Capture the authentic brand voice and conversational idiosyncrasies of an SMB founder so customer interactions feel natural, trusted, and strictly on-brand.
📚 Progressive Disclosure & L3 Reference Index
Read REQUIRED core calibration standards below; consult deep L3 Reference Guides on demand:
| L3 Resource | Scope & Purpose | Trigger / When to Load |
|---|---|---|
voice_calibration_matrix.md |
4-axis tone spectrum, lexical blacklist, persona schema | In-depth voice calibration & persona tuning |
voice_calibrator.py |
Deterministic tone metric analyzer & persona generator | Automated sample analysis & artifact creation |
pii-redaction |
Zero-trust PII sanitizer | Prior to ingesting customer emails or chat logs |
smb-data-ingestion |
Structured data import protocol | Ingesting business records and CRM exports |
🧭 1. The 4-Phase Calibration Lifecycle
1. INGEST ➔ Collect 10-50 authentic sample communications (emails, chat logs, FAQs, founder notes).
2. SCRUB ➔ Execute `@[skills/pii-redaction]` to purge customer cards, SSNs, phone numbers, and addresses.
3. CALIBRATE ➔ Run `voice_calibrator.py` to extract formality, enthusiasm, directness, and signature phrases.
4. SYNTHESIZE➔ Generate the calibrated persona artifact (.tmp/twin-persona.md) for agent system prompt injection.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago Changed · +59 lines 5beb183d3637
- 2mo ago First seen · 32 lines · 30 tokens per session scan A 4ab78938712c
digital-twin-voice is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 1,283 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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