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 vasilyu1983/AI-Agents-public --skill product-help-centergit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/product-help-center)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/product-help-center"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/product-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/vasilyu1983/ai-agents-public/product-help-center"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/product-help-center.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 | $0.00032 | $0.03385 |
| Opus 5 | $0.00016 | $0.01692 |
| Sonnet 5 | $0.00006 | $0.00677 |
| Haiku 4.5 | $0.00003 | $0.00338 |
Grade A, and why
product-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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Help Center Design
Design public help centers, in-app self-service, and AI-consumable documentation systems.
Use this skill when the user needs one of these outcomes:
- pick or compare a help center, docs, or support-AI platform
- design or audit taxonomy, navigation, article standards, and governance
- plan retrieval-first support AI with citations, tool permissions, and escalation
- make docs easier for humans, search, and AI agents to consume
Workflow
- Classify the surface
- Support help center, developer docs portal, internal knowledge base, in-app guidance, or hybrid.
- Define audience and risk
- End users, admins, developers, agents, regulated customers, multilingual audiences.
- Choose the operating model
- Human-authored docs only, retrieval-first support AI, or agentic support with approved tools.
- Design information architecture
- Category structure, navigation, search strategy, metadata, URL rules, and versioning.
- Standardize content
- Article types, writing rules, visual rules, and reusable templates.
- Instrument quality
- Search analytics, self-service outcomes, citation quality, handoff quality, and freshness signals.
- Run knowledge operations
- Owners, review cadences, release-driven updates, and stale-content remediation.
Expected outputs:
- help center or docs platform recommendation with rationale
- taxonomy map, metadata schema, and article backlog
- support AI design with sources, escalation policy, and guardrails
- operating model for ownership, QA, and measurement
ASCII Flow
Help center or support-docs request
-> Classify surface: help center, developer docs, KB, in-app, or hybrid
-> Define audience, risk, locale, and support context
-> Choose operating model
+-- human-authored docs
+-- retrieval-first support AI
+-- agentic support with approved tools
-> Design IA, taxonomy, metadata, URLs, search, and versioning
-> Standardize article types and templates
-> Add measurement: search, self-service, citations, handoff, freshness
-> Assign owners, review cadence, migration plan, and stale-content loop
What ships with it
16 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.
- agents/openai.yaml 331 B
- data/sources.json 8.2 KB
- learnings.consolidated.md 595 B
- learnings.md 376 B
- references/accessibility-standards.md 12 KB
- references/ai-consumable-docs.md 4.3 KB
- references/ai-integration.md 8.4 KB
- references/article-templates.md 12 KB
- references/content-migration-guide.md 9.3 KB
- references/knowledge-ops.md 6.9 KB
- references/learning-paths.md 13 KB
- references/metrics-optimization.md 6.2 KB
- references/multilingual-support.md 11 KB
- references/platform-guides.md 7.0 KB
- references/taxonomy-patterns.md 9.4 KB
- scripts/validate_sources.py 4.8 KB runs code
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 · 299 lines · 32 tokens per session scan A beda6ba58036
product-help-center is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 32 tokens to every session and 3,385 once invoked, about $0.0002 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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