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 help-centergit 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/help-center)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/help-center"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/help-center/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/help-center"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/help-center.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 | $0.00152 | $0.03058 |
| Opus 5 | $0.00076 | $0.01529 |
| Sonnet 5 | $0.00030 | $0.00612 |
| Haiku 4.5 | $0.00015 | $0.00306 |
Grade A, and why
help-center 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Help Center
Produce Intercom-ready help-center / knowledge-base articles for one product, organized by a researched canonical collection taxonomy and scoped per user persona. The output is the canonical link target that onboarding emails, in-product tooltips, and Intercom Fin AI agents reference.
The taxonomy (which collections to include) is researched and codified — see the premium reference. The skill picks from a 12-module library based on product shape; it does not invent collections at runtime.
For the full template library by collection → the premium reference. For Intercom-native voice + structure rules → the premium reference. For persona scoping → the premium reference. For the JSON export schema + script → the premium reference.
Doctrine inherited (Step 7 — 0626 rollout, locked 2026-06-04)
Output complies with:
output-tenets.md— the seven tenetsoutput-simplicity.md— length caps, three-layer source placement, robot-tells bandoc-output-structure.md— GDoc/Notion structural defaults- Step 6 calibration: see [[feedback_execution_doctrine_refinements_step6]] (canonical R7 + R8 implementation)
Refinements applied to this skill (the R7 + R8 canonical implementation):
| Code | Refinement | How it lands in help-center |
|---|---|---|
| R1 | Source placement (three layers) | KB articles are end-customer-facing. No sources block. Source dataset (product PRDs, transcripts, win-loss) lives in working doc only. The article IS the surface. |
| R3 | Product-update tone | Capability framing — "[Product] does X" not "we are thrilled to introduce X." Even on launch-day articles. |
| R6 | CTA hierarchy | Educational overviews → sign-up at the close for prospects; product-action CTA for existing users. How-to articles → product-action only (user is in-product). |
| R7 | FAQ titles + two types + no sources block | Two article types only. Educational overviews (~500-800 words, FAQ titles: "What is [Product]?" / "What does [Product] do?"). Quick how-tos (≤4 bullets max, FAQ titles: "How to [task]"). Pattern: "How to issue virtual card" (right) vs "Issue your first virtual card in two minutes" (wrong). Sequential steps use numbered lists (1. 2. 3.), not dash-bullets. No sources block ever. |
| R8 | Entity-name headings | Section headings repeat the product name — "What [Product] does," "Who [Product] is for," "How [Product] is different." Pronoun headings ("What it does") disappear in skim-reading; entity headings surface. |
| R9 | Action-oriented section names | "How to start with [Product]" beats "Getting Started." "How to set up [Feature]" beats "Setup guide." |
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 · 224 lines · 152 tokens per session scan A f1a4db2c3682
help-center is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 152 tokens to every session and 3,058 once invoked, about $0.0008 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.
Other skills, from other repositories
gingiris-b2b-growth
🇺🇸 B2B SaaS Growth — PLG vs SLG Playbook — Diagnose whether your problem is distribution, pricing, or PMF. PLG/SLG selection by ACV and sales cycle, the 5-stage path from $0 to $10M ARR, NRR discipline, affiliate & channel motion, enterprise tiering. Built from HeyGen, Deel, Vercel, Supabase, Snowflake patterns.…
gr-b2b-growth
A guide to growing a business-to-business software product from early user research to large-scale sales. B2B software is sold to companies rather than individual consumers.
go-to-market-playbook
A reusable Go-to-Market strategy template for both B2B and B2C launches. Covers positioning, messaging, ICP definition, channel selection, and competitive analysis frameworks. By @WeiYipei.
gingiris-go-global
🇺🇸 AI Product / SaaS Go-Global Complete SOP — From competitor research to launch to monetization. A full-cycle playbook covering Phase 0-5 (market validation, positioning, first 100 users, user interviews, beta-to-growth) plus open-source launch, Product Hunt, Reddit, SEO/GEO, conversion, and org principles.…
gr-competitor-research
Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI…
ai-launch-playbook
Launch your AI product to global attention — the playbook behind Manus, Devin, and AFFiNE's breakout launches. Covers AI-specific GTM strategy, hype cycle management, waitlist tactics, and multi-market rollout for maximum day-one impact.