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 social-sellinggit 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/social-selling)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/social-selling"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/social-selling/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/social-selling"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/social-selling.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 | $0.00024 | $0.02585 |
| Opus 5 | $0.00012 | $0.01293 |
| Sonnet 5 | $0.00005 | $0.00517 |
| Haiku 4.5 | $0.00002 | $0.00259 |
Grade A, and why
linkedin-social-selling 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Social Selling
Signal-based LinkedIn outreach that turns content engagement into pipeline. 12 tactical plays leveraging intent signals to maximize response rates. Apollo + Exa for prospect research; per-prospect Apollo enrichment gated per .claude/rules/apollo-credits.md.
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 banoutbound-research-hygiene.md— dated signals, current-company-only hooks, no invented statsai-speak-anti-patterns.md— the 12 patterns DMs and comments can't carrylinkedin-cold-dm-doctrine.md— the cold-start motion: 9 tactics, connection/InMail envelope, "easy no" cadence (powers the cold-start plays)- Step 6 calibration: see [[feedback_execution_doctrine_refinements_step6]]
Refinements applied to this skill:
| Code | Refinement | How it lands in linkedin-social-selling |
|---|---|---|
| R1 | Source placement (three layers) | DMs and comments are end-customer-facing. No source tags inline. Internal play-tracking + research evidence lives in working notes only; never surfaces in the DM body. |
| R3 | Product-update tone | When a play references our product, frame as "I shipped X" or "we ship X" — never "we are thrilled to announce." Reads as ad copy, not as a human reaching out. |
| R6 | CTA hierarchy | DM closes name the next step appropriate to the signal — discovery-call for cold signal (profile view), trial sign-up for content engagement, demo for high-intent (pricing-page visit). Blog as fallback when the prospect isn't yet sign-up-ready. |
| R9 | Action-oriented section names | "Spot the signal / Open the DM / Land the value / Close on next step" — verb-led across every play. |
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 · 244 lines · 102 tokens per session scan A fe7d90ca87cf
linkedin-social-selling is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 2,585 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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