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 discoverygit 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/discovery)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/discovery"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/discovery/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/discovery"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/discovery.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 | $0.00129 | $0.01917 |
| Opus 5 | $0.00064 | $0.00958 |
| Sonnet 5 | $0.00026 | $0.00383 |
| Haiku 4.5 | $0.00013 | $0.00192 |
Grade A, and why
client-discovery 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research source (Exa)
Default: Exa, per .claude/rules/exa-protocol.md (auto-loaded for research, audit, competitor, ICP, AEO, content sourcing, sales prospecting work).
Primary Exa tools for this skill: company_research_exa, web_search_exa.
Use case: pre-call account intel for prospect-prep.
Tool surface during the migration window:
- New plugin (preferred):
mcp__plugin_exa_exa__company_research_exa(afterclaude plugin i exa@claude-plugins-official). - Legacy MCP (still mounted):
mcp__exa__company_research_exa. - Both backends route to the same Exa API — they don't double-bill.
Citation: every Exa-derived claim uses [VERIFIED: exa_search, {url}, accessed {YYYY-MM-DD}] per .claude/rules/ontology.md.
Quality gate (research outputs): ≥3 sources per major claim, ≥50% [VERIFIED] confidence, date filter for any "recent / latest" claim, no fallback to WebSearch without flagging the data gap.
Worked examples + tool catalog: .claude/skills/meta-skills/exa/.
Client discovery
Generate tailored discovery call scripts based on prospect context. Maps research to targeted questions and listening cues for qualification.
Doctrine inherited (Step 7 — 0626 rollout, locked 2026-06-04)
Output complies with output-tenets.md, output-simplicity.md, outbound-research-hygiene.md. Step 6 calibration: see [[feedback_execution_doctrine_refinements_step6]].
Refinements applied: R1 (discovery script is internal rep-facing — inline cites stay for QA), R3 (question framing operator-direct), R9 (verb-led script section names).
Process at a glance
INPUT VALIDATION → RESEARCH PROSPECT → GENERATE QUESTIONS → ADD CUES → REVIEW & CHAIN
Three steps:
- Research prospect website + LinkedIn + sent materials, build opening playbook
- Generate tailored questions across 8 categories
- Add listening cues + qualification criteria
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 · 206 lines · 129 tokens per session scan A 78f2b3fcff41
client-discovery is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 129 tokens to every session and 1,917 once invoked, about $0.0006 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.
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