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 gtme-podcastgit 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/gtme-podcast)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/gtme-podcast"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/gtme-podcast/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/gtme-podcast"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/gtme-podcast.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 183 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00289 | $0.03996 |
| Opus 5 | $0.00144 | $0.01998 |
| Sonnet 5 | $0.00058 | $0.00799 |
| Haiku 4.5 | $0.00029 | $0.00400 |
Grade A, and why
gtme-podcast 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 12d 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 — 286 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: web_search_exa, company_research_exa. Use case: pre-podcast guest + company research.
Citation: every Exa-derived claim uses [VERIFIED: exa_search, {url}, accessed {YYYY-MM-DD}] per .claude/rules/ontology.md. Quality gate: ≥3 sources per major claim, ≥50% [VERIFIED] confidence, date filter for any "recent / latest" claim, no fallback to WebSearch without flagging.
GTM Engineer School podcast
Turn a single podcast transcript into a complete amplification kit: the Substack post, Matteo's LinkedIn expert post, a guest-voice LinkedIn post (with a soft Cohort 4 CTA), and an outreach email bundling it all for the guest. Every quote traces verbatim to the transcript — no fabrication, no invented promo codes, no generic filler.
Doctrine inherited (Step 7 — 0626 rollout)
Output complies with:
output-tenets.md— the seven tenetsoutput-simplicity.md— length caps, three-layer source placement, robot-tells banai-speak-anti-patterns.md— the 12 patterns LinkedIn voice can't carry- Step 6 calibration: see [[feedback_execution_doctrine_refinements_step6]]
Refinements applied to this skill:
| Code | Refinement | How it lands in gtme-podcast |
|---|---|---|
| R1 | Source placement (three layers) | Substack post + LinkedIn posts + guest email are end-customer-facing. No sources block. Transcript quotes appear inline with attribution to the guest — but no [VERIFIED:...] tags. Source transcript lives in working doc for QA only. |
| R3 | Product-update tone | When the episode features GTME School cohort framing, frame as "Cohort 5 opens [date]" not "we are thrilled to announce Cohort 5." Per [[feedback_gtme_pulse_conventions]] cohort-naming rule. |
| R6 | CTA hierarchy | Substack + LinkedIn posts → soft cohort enrollment as primary CTA, podcast subscribe as fallback. Guest email → reply-to-share primary, social-share as fallback. Cohort naming pulled from current cohort in [[feedback_gtme_pulse_conventions]]. |
| R9 | Action-oriented section names | "Why this episode matters / What [Guest] shipped / How to take [Guest's] approach further" — verb-led + entity-named. |
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.
- 12d ago First seen · 286 lines · 289 tokens per session scan A d4f9bf09cb0a
gtme-podcast is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 289 tokens to every session and 3,996 once invoked, about $0.0014 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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