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 organvm-iv-taxis/a-i--skills --skill corpus-persona-extractiongit clone --depth 1 https://github.com/organvm-iv-taxis/a-i--skillsWrote 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/organvm-iv-taxis/a-i--skills/corpus-persona-extraction)<a href="https://agentmods.dev/skills/organvm-iv-taxis/a-i--skills/corpus-persona-extraction"><img src="https://agentmods.dev/badge/skills/organvm-iv-taxis/a-i--skills/corpus-persona-extraction/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/organvm-iv-taxis/a-i--skills/corpus-persona-extraction"><img src="https://agentmods.dev/badge/skills/organvm-iv-taxis/a-i--skills/corpus-persona-extraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00101 | $0.01494 |
| Opus 5 | $0.00051 | $0.00747 |
| Sonnet 5 | $0.00020 | $0.00299 |
| Haiku 4.5 | $0.00010 | $0.00149 |
Grade A, and why
corpus-persona-extraction 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 13d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Corpus Persona-Extraction
Distill how a specific voice thinks and speaks from a pile of session logs, and persist it as a machine-usable lexicon that downstream agents can write against.
Why this exists
Agents that work with a long-running human collaborator keep re-learning the same voice from scratch: the coinages, the load-bearing metaphors, the sentence shapes that mean "yes, proceed" versus "you've missed the point." A lexicon file makes that knowledge durable and composable — voice-enforcement gates can score against it, drafting skills can imitate it, and translation layers (persona storefronts) can project from it.
Core doctrine
- User coinages outrank standard vocabulary. When the corpus shows an idiosyncratic term for a concept ("vacuum", "organ", "cartridge", "liturgy"), the lexicon records the coinage as primary and the standard term as gloss — never the reverse.
- Append-only. Each extraction run adds a dated block. Prior blocks are evidence of drift, not errors to clean up.
- Separate speakers ruthlessly. A transcript interleaves human, assistant, and tool text. Only the persona's own turns feed frequency analysis; assistant paraphrase contaminates the signal.
Workflow
1. Assemble and filter the corpus
- Gather session files; record provenance (paths, date range, message counts) in the extraction header.
- Parse each format to (speaker, timestamp, text) tuples; keep only
speaker == persona_idturns. - Strip quoted/pasted material (code blocks, tool output, copied text) — the persona's own prose only.
- Redact before analysis: run the corpus through secret/PII patterns first; a lexicon must be shareable.
2. Vocabulary frequency
- Tokenize; compute frequency ranks for unigrams/bigrams/trigrams.
- Diff against a general-English baseline: the interesting set is terms whose corpus rank vastly exceeds baseline rank (keyness), not raw frequency.
- Bucket results: domain terms (shared with field), coinages (absent from baseline entirely), and inflections (standard words used in non-standard senses).
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.
- 13d ago First seen · 107 lines · 101 tokens per session scan A e904ef340d4b
corpus-persona-extraction is a skill published in the GitHub repository organvm-iv-taxis/a-i--skills (17 stars, last pushed 16d ago), licensed Apache-2.0. It adds 101 tokens to every session and 1,494 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
agent-communication-protocol
Open protocol for AI agent interoperability enabling standardized communication between agents, applications, and humans across different frameworks.
agent-creation-skill
Procedural guide for creating new agents from templates when project complexity requires delegation.
code-research
Use when produce structured understanding of unfamiliar codebases — architecture, data flows, dependencies, and conventions. Use when joining a new project, tracing feature implementations, or mapping system architecture.
market-research-agent
Use when analyze markets, competitors, user segments, and trends to produce evidence-based business intelligence. Use when evaluating market opportunities, pricing strategy research, or due diligence for investments. Use when analyzeing markets, competitors, user segments, and trends to produce evidence-based.
semantic-persona-routing
Erstellt und verwendet einen anbieterneutralen semantischen Routing-Graphen aus Personas, koordinierenden Rollen, Experten und aktiven Skill-Endpunkten. Verwenden, wenn ein LLM eine Anfrage von einer Leitrolle über einen Experten zu einem Skill routen, aus einem vorhandenen Agentensystem einen portablen Persona-Router…
geoffrey-hinton
Agente que simula Geoffrey Hinton — Godfather of Deep Learning, Prêmio Turing 2018, criador do backpropagation e das Deep Belief Networks.