Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add geledek/enterprise-ai-transformation-skills/plugin install enterprise-ai-transformation-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/geledek/enterprise-ai-transformation-skills/people-frontline-engagement)<a href="https://agentmods.dev/skills/geledek/enterprise-ai-transformation-skills/people-frontline-engagement"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/people-frontline-engagement/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/geledek/enterprise-ai-transformation-skills/people-frontline-engagement"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/people-frontline-engagement.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 | $0.00167 | $0.03056 |
| Opus 5 | $0.00084 | $0.01528 |
| Sonnet 5 | $0.00033 | $0.00611 |
| Haiku 4.5 | $0.00017 | $0.00306 |
Grade A, and why
people-frontline-engagement 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
People — Frontline Augmentation Engagement
A five-role protocol for engaging fearful or skeptical frontline experts as co-designers of an augmentation workflow rather than targets of automation. Anchored on Dell'Acqua's HBS jagged-frontier finding (AI is uneven across sub-tasks of a single role), Edmondson's psychological-safety research, and Accenture's redeployment evidence that augmentation outperforms displacement on retention and ROI.
Stanford's 2025 enterprise study: 77% of the hardest costs in AI deployment are invisible — change management, redesign, trust. Skip this protocol and the pilot stalls inside the 95% non-impact band. Run all five roles in order. Carry every quote forward as state.
Verdict vocabulary (stable output contract): Co-designed / Imposed-with-resistance / Stalled.
Role 1: Empathic Listener
Sit with the expert. Do not pitch. Do not reassure. NAME THE FEAR with the expert's own words.
CLASSIFY THE FEAR — pick one or more, quote the language:
- Replacement — "they're going to make us redundant", "the AI will do my job"
- Deskilling — "I'll forget how to read a scan / draft a contract / triage a call"
- Accountability shift — "if the AI is wrong, who gets sued / struck off / disciplined?"
- Autonomy loss — "they'll watch every move I make", "I'll be following a script"
- Status loss — "junior staff with AI will outproduce me", "my expertise stops mattering"
QUOTE BACK. Read the fear back to the expert in their own words. Wait for "yes, that's it" before proceeding. Do not paraphrase into management-speak.
CONTEXT MARKERS — note which apply:
- High-stakes domain (clinical, legal, safety-critical, regulated): fear is rational, not irrational
- Senior worker, deep tradecraft: deskilling fear deserves real protection (see Role 5)
- Prior layoff or restructure in this org: trust is depleted; assume baseline distrust
- Public statements by leadership about "headcount efficiency": expert has heard them
Consult deloitte-cheerleader-to-champion.md: surface-level enthusiasm from leadership without behavior change is read by the frontline as a threat, not an invitation.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 184 lines · 167 tokens per session scan A 3dbd4b7f7e55
people-frontline-engagement is a skill published in the GitHub repository geledek/enterprise-ai-transformation-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 167 tokens to every session and 3,056 once invoked, about $0.0008 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 2026-08-31.
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