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 matteotitta/genesys-skills --skill tov-guidelinesgit clone --depth 1 https://github.com/matteotitta/genesys-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/matteotitta/genesys-skills/tov-guidelines)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/tov-guidelines"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/tov-guidelines/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/matteotitta/genesys-skills/tov-guidelines"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/tov-guidelines.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 | $0.00027 | $0.01842 |
| Opus 5 | $0.00014 | $0.00921 |
| Sonnet 5 | $0.00005 | $0.00368 |
| Haiku 4.5 | $0.00003 | $0.00184 |
Grade A, and why
tov-guidelines 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 9d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TOV guidelines
Extract actionable editorial tone of voice guidelines from a company's existing content. Two-phase workflow: Phase 1 analyzes website content to surface evidence-based voice patterns; Phase 2 codifies those patterns into guidelines that downstream content skills consume. User review gate sits between the two phases.
When to run
- User says "tone of voice", "brand voice", "voice guidelines", "writing style guide", or asks to extract voice from a URL.
- Starting a new content program for a client and downstream skills (linkedin-content, landing-page-copy, product-messaging, outreach-emails) need a voice contract.
- Existing TOV is stale (>6 months) or content has drifted from documented voice.
Don't run when: user wants visual brand identity (use brand-kit), product messaging (use product-messaging), to write content directly (use the content skill with TOV as input), or just a quick voice description (answer directly).
Inputs
| Input | Required | Source |
|---|---|---|
| Company URL | yes | User |
| Founder context (transcript, notes, voice preferences) | optional | User / company-context |
| Existing brand guidelines (to update) | optional | User |
| Target audience | optional | icp-research / user |
Validate before proceeding: URL is accessible, site has homepage + 5-10 pages of content. If thin, flag sample-size limitation in output.
Research substrate (Exa): primary tool web_search_exa for founder voice harvesting via site:linkedin.com / site:twitter.com filters. Per .claude/rules/exa-protocol.md. Citation: [VERIFIED: exa_search, {url}, accessed {YYYY-MM-DD}]. Quality gate: ≥3 sources per major claim, ≥50% [VERIFIED].
Steps
Two phases. Phase 1 outputs tov-analysis.md. User reviews. Phase 2 outputs tov-guidelines.md.
Phase 1 — Analysis
- Discover site structure — fetch homepage, extract internal links, filter same-domain only, prioritize
/about,/manifesto,/values,/blog/*,/case-studies/*,/pricing,/faq. - Scrape 15-20 pages in priority order: homepage → about/values → blog (3-5) → case studies (2-3) → pricing → FAQ → landing pages.
- Extract sentence-level patterns — average length, person (1st/3rd), question frequency, imperative usage.
- Extract paragraph-level patterns — average length, openings, transitions, evidence placement.
- Extract word-level patterns — company vocabulary, customer/industry vocabulary, banned/avoided words, modifier frequency.
- Extract structural patterns — headers (sentence vs title case), CTA placement and phrasing, proof stacking, section organization.
- Score frequency — High (80%+ of pages), Medium (40-79%), Low (<40%), Conflict (contradictory).
- Build content-type voice mapping — table of person/formality/CTA per page type (homepage, blog, case study, pricing).
- Generate voice-in-action examples — generic → on-brand transformations, drawn from actual scraped text (never invented).
- Identify inconsistencies — flag conflicts (e.g., homepage uses "I", pricing uses "we") for Phase 2 resolution.
- Document gaps — what couldn't be determined; suggest founder interview questions.
- Write
tov-analysis.mdusing the premium reference (44-section canonical scaffold; compact alternative in same file). - Present to user — Review gate. User must confirm patterns, correct misidentifications, and answer gap questions before Phase 2.
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.
- 9d ago First seen · 129 lines · 169 tokens per session scan A bf7e4b426a7b
tov-guidelines is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 27 tokens to every session and 1,842 once invoked, about $0.0001 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 2026-09-03.
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