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/general-peer-cases)<a href="https://agentmods.dev/skills/geledek/enterprise-ai-transformation-skills/general-peer-cases"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/general-peer-cases/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/general-peer-cases"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/general-peer-cases.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 | $0.00137 | $0.02352 |
| Opus 5 | $0.00068 | $0.01176 |
| Sonnet 5 | $0.00027 | $0.00470 |
| Haiku 4.5 | $0.00014 | $0.00235 |
Grade A, and why
general-peer-cases 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
General — Peer Case Library
Surface relevant named peer cases for the asker's situation. Output what worked, what failed, what they would do differently — and which single case is the closest analog.
Anchor base rate: 95% of GenAI pilots produce zero measurable P&L impact (MIT 2025). Only 5% break through. Every retrieved case must be situated against this floor — a "success story" pulled in isolation is misleading.
The library is bundled in references/. Pull cases by archetype match — not by name recall.
Output contract (stable): asker profile, case bundle, cross-case patterns, and a confidence state — including No-analog-found when fewer than 3 archetype matches exist.
Step 1: Asker Profile
Filter the library before retrieving. Without a profile, every case looks equally relevant — and nothing is actionable.
PROFILE QUESTIONS — answer each in one line:
- Vertical? (Healthcare / FinServ / Public sector / Manufacturing / Retail / Tech / Professional services / Other — name the sub-vertical if it matters, e.g. "tertiary hospital" not just "healthcare".)
- Org size and operating model? (Headcount band; centralized vs federated; regulated vs unregulated; geography.)
- Use-case archetype? (Customer service automation / knowledge-worker copilot / document extraction / agentic workflow / vertical AI product / internal search — pick one. If unclear, force it.)
- Maturity stage? (Experimenting / Piloting / Scaling / Operating — map against MIT-CISR's four stages. Consult
mit-cisr-4-stages.md.) - Decision being made? (Selection / build-vs-buy / scale-vs-kill / vendor swap / governance gate — the case bundle should be tuned to this decision.)
If the asker can answer fewer than 4 of these, stop. Surface the gap. Cases retrieved without profile are noise.
Output: VERTICAL | ORG SIZE | ARCHETYPE | MATURITY STAGE | DECISION TYPE
Step 2: Case Retrieval
Pull 3-5 named cases from the bundled library. Match on archetype first, vertical second, org-size third. Never pad with weak matches — 3 strong analogs beat 5 mixed ones.
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 · 160 lines · 137 tokens per session scan A c41e7671f180
general-peer-cases is a skill published in the GitHub repository geledek/enterprise-ai-transformation-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 137 tokens to every session and 2,352 once invoked, about $0.0007 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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