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 linkedin-commentgit 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/linkedin-comment)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/linkedin-comment"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-comment/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/linkedin-comment"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-comment.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 | $0.00021 | $0.02273 |
| Opus 5 | $0.00010 | $0.01137 |
| Sonnet 5 | $0.00004 | $0.00455 |
| Haiku 4.5 | $0.00002 | $0.00227 |
Grade A, and why
linkedin-comment 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Comment
Generates strategic LinkedIn comments that build relationships through audience merging — high-value engagement that drives reciprocity and positions Matteo as a B2B SaaS GTM expert. Uses a 3-Sentence Framework (VALIDATE → EXPAND → HOOK) and the 100 Posts Test to filter generic AI-flavoured comments.
How it differs from /linkedin-content: that skill writes posts; this skill writes comments on other people's posts — relationship building, not broadcast. Comments shape your member embedding (algorithm-side) and trigger 5.2x amplification when discussion threads form between commenters.
Doctrine inherited (Step 7 — 0626 rollout, locked 2026-06-04)
Output complies with output-tenets.md, output-simplicity.md, ai-speak-anti-patterns.md (all 12 banned patterns enforced — generic praise openers, parallel-3 rhythm, X-not-Y, engagement-farming closers all banned). Step 6 calibration: see [[feedback_execution_doctrine_refinements_step6]].
Refinements applied: R1 (comment body is end-customer-facing — no source tags), R3 (operator-direct comment voice), R9 (3-sentence VALIDATE→EXPAND→HOOK structure verb-led).
Reply style — lead with the move (locked 2026-06-16)
Calibrated on the GTME Cohort 4 thread-feedback batch. Four rules that override the validate-first habit when writing thread replies (and sharpen ordinary comments too):
- Lead with the constructive suggestion, not validation. No name-opener, no opening acknowledgement or "you nailed X" stroke — those are the scripted-flattery tells. Just read, acknowledge briefly (woven in mid-reply, never fronted), and lead with something they can implement.
- First-person operator POV. "this is what I'd do", "I'd rather focus on", "I'd build X first" — not "X is the right call" / "great instinct" / "you nailed it". You're sharing your own move, not awarding marks from the front of the room.
- Different structure per reply across a batch. When a run produces more than one reply, every one takes a distinct shape — reframe-led, imperative-led, question-led, risk-led, scope-led, peer-amplify, sequencing-led. Same skeleton across a batch is itself an AI fingerprint the 100 Posts Test can't catch (it tests one comment in isolation, blind to cross-batch repetition).
- Still anchored. Open on a specific phrase from their post, name one real bottleneck (not the obvious one), reference real peer replies by name where they exist, close on one concrete move. Voice anchors for cohort feedback:
projects/courses/gtme-school/course/0626-c4-exercise-1-feedback.md+0626-c4-week3-wip-feedback.md.
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 · 125 lines · 84 tokens per session scan A 7c106015dae6
linkedin-comment is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 2,273 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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