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 swan-gtm/gtm-skills --skill linkedin-executive-presence-auditgit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/linkedin-executive-presence-audit)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-executive-presence-audit"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-executive-presence-audit/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/swan-gtm/gtm-skills/linkedin-executive-presence-audit"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-executive-presence-audit.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 | $0.00068 | $0.00676 |
| Opus 5.5 | $0.00027 | $0.00270 |
| Sonnet 5.5 | $0.00014 | $0.00135 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
linkedin-executive-presence-audit 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 7d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Judge whether an executive's LinkedIn activity builds authority with the audience that matters, rather than reach among peers.
Inputs to load
- The profile headline, about section and featured items.
- The last 20 to 30 posts with their reactions, comments and saves.
- The business goal behind the presence: pipeline, hiring, partnerships or category positioning.
- The target audience, described by role and company type.
Run five tests
- Positioning clarity. A stranger must be able to say in one sentence what this person is known for after reading the headline and the last five posts.
- Hook strength. Read only the first two lines of each post. Weak hooks warm up, announce or thank. Strong hooks state a claim, a result or a tension.
- Theme concentration. Count distinct themes. More than three means the audience cannot learn what to expect.
- Proof density. Split posts into own data and own experience versus borrowed ideas and generic advice. Authority comes from the first group.
- Audience quality. Sample who comments and who saves. Estimate the share of engagement coming from the target audience versus peers, recruiters and engagement groups.
Check the conversion path
Follow one reader from a post to the profile to a next step. If the profile does not tell a buyer, candidate or partner what to do next, the content cannot convert, however well it performs.
Verdict
- Authority building: clear positioning, concentrated themes, high proof density, engagement from the target audience.
- Visible but generic: reach is healthy, proof density is low, engagement comes mostly from peers.
- Active but unfocused: strong individual posts across too many themes.
- Invisible: unclear positioning, inconsistent cadence, weak hooks.
Each verdict ends with the single change that moves the profile up one level.
What good looks like
- The expert's tell: the ratio of own proof to borrowed ideas, and the quality of the first line. Both predict authority better than impressions.
- The common mistake: optimizing for reach among peers while the buyers never see or trust the content. Generic motivation, long warm ups and announcement posts are the usual symptoms.
- A good output gives one clear verdict, three prioritized changes with expected effect, and a proposed theme set of no more than three.
- Engagement is read by who, not how many. A smaller audience of the right buyers beats a large audience of peers.
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.
- 7d ago First seen · 52 lines · 68 tokens per session scan A c957f677055e
linkedin-executive-presence-audit is a skill published in the GitHub repository swan-gtm/gtm-skills (221 stars, last pushed 8d ago), licensed MIT. It adds 68 tokens to every session and 676 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-21.
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