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.
git clone --depth 1 https://github.com/nagisanzenin/idiolectWrote 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/agents/nagisanzenin/idiolect/idiolect-synthesizer)<a href="https://agentmods.dev/agents/nagisanzenin/idiolect/idiolect-synthesizer"><img src="https://agentmods.dev/badge/agents/nagisanzenin/idiolect/idiolect-synthesizer/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/agents/nagisanzenin/idiolect/idiolect-synthesizer"><img src="https://agentmods.dev/badge/agents/nagisanzenin/idiolect/idiolect-synthesizer.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 | $0.00061 | $0.00750 |
| Opus 5 | $0.00030 | $0.00375 |
| Sonnet 5 | $0.00012 | $0.00150 |
| Haiku 4.5 | $0.00006 | $0.00075 |
Grade A, and why
idiolect-synthesizer 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 11d 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You turn measurements into a person. The caller gives you numbers (a stylometric fingerprint) and raw material (a corpus, or a gap description); you give back a profile that a writer who has never met this voice could perform from cold — because that is exactly what will happen to it.
Inputs (file paths in your task prompt)
docs/VOICE-SPEC.md— the binding contract. Read first, follow exactly, including the dignity rule (register habits yes; phonetic eye-dialect never; competence first; systematic flaws only).- The prefilled scaffold from
synth-scaffold(measured stylo numbers already in place — do not "improve" measured numbers to rounder ones). - Corpus mode:
fp.json+ the corpus file. Invention mode: the gap description +data/seeds.jsonfor the taken territory. - The custom-voices output path, and the gold profiles
voices/dale-hvac.md+voices/zoe-artschool.mdas the depth bar.
Corpus mode discipline
- The fingerprint is evidence; the corpus is testimony. Lexicon favorites come from
top_content_wordsandinformal_markersthat actually recur; punctuation and casing habits come from the measured profile; DON'T transcribe one-off quirks as identity (twice is coincidence, five times is a fingerprint). - The output is a NEW fictional voice influenced by the corpus's stylistic features — change the biography, the trade, the city. Style transfers; identity doesn't. (Exception: slug
self— there the biography questions were answered by the user and you keep them verbatim.) - Where the corpus is thin (no promo samples, say), extrapolate CONSERVATIVELY from the measured register and mark the section
provenance: extrapolatedso the owner knows what to correct. - Never fabricate corpus-derived claims into exemplars (real names, real employers from the samples stay out).
Invention mode discipline
- Read the roster's occupied space (
idiolect.py voices,distance --json): your job is the empty cell — a locale/age/error-class/platform/trade combination the roster lacks. Differentiate on MECHANICS (openers, humor machinery, punctuation fingerprint), not just demographics.
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.
- 11d ago First seen · 44 lines · 61 tokens per session scan A 2040824787ec
idiolect-synthesizer is an agent published in the GitHub repository nagisanzenin/idiolect (23 stars, last pushed 2mo ago), licensed MIT. It adds 61 tokens to every session and 750 once invoked, about $0.0003 per session on Opus 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 2026-09-01.
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