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-use-case-discovery)<a href="https://agentmods.dev/skills/geledek/enterprise-ai-transformation-skills/general-use-case-discovery"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/general-use-case-discovery/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-use-case-discovery"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/general-use-case-discovery.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 | $0.00144 | $0.02110 |
| Opus 5 | $0.00072 | $0.01055 |
| Sonnet 5 | $0.00029 | $0.00422 |
| Haiku 4.5 | $0.00014 | $0.00211 |
Grade A, and why
general-use-case-discovery 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
General — Use-Case Discovery
Most enterprises drown in candidate ideas and pilot the wrong one. MIT's 95/5 finding shows 95% of GenAI pilots return zero P&L impact — the failure is upstream, in selection. Stanford's 51-deployment study shows winners cluster around narrow, repetitive, measurable workflows. Run the four roles below before committing budget.
Anchor: McKinsey's three-objective mix (productivity / growth / transformation), Andrew Ng's three moats, BCG 10/20/70 effort split.
Per-candidate verdict vocabulary (stable output contract): Greenlight / Stage-and-watch / Park / Reject.
Role 1: Value-Pool Mapper
Where is the dollar-or-hour pain, by sub-function? Do not start from "what can AI do" — start from where the bleeding is.
- Where do FTE-hours concentrate? (Pull headcount × time-on-task by sub-process. Top 3 buckets only.)
- Where is the rework / error / SLA-miss tax? (Quality cost, not just labor cost.)
- Where is revenue leaking? (Conversion drop-offs, abandoned carts, slow quoting, missed renewals.)
- What is the customer-experience pain that shows up in NPS verbatims? (External, not internal.)
- Map each pool to McKinsey's 3-objective mix. (Productivity = cost-out, Growth = topline, Transformation = new model. Aim ~50/30/20 across the portfolio — consult
mckinsey-3-objective-mix.md.)
Refuse to advance any candidate without a quantified pool (≥$500K/yr or ≥2 FTE-equivalents or ≥5pt NPS).
Output: SUB_FUNCTION | PAIN_TYPE | ANNUAL_VALUE_POOL | OBJECTIVE_BUCKET | EVIDENCE_SOURCE
Role 2: Capability Archetype Classifier
Match the friction shape to a capability archetype. Do not let vendors pick the archetype for you.
Five archetypes (Stanford 51-deployments taxonomy — consult stanford-51-deployments.md):
- Chatbot / Q&A — single-turn, low-stakes, deflection plays.
- RAG / Knowledge-surfacing — retrieval over owned corpus, expert assist.
- Workflow co-pilot — embedded in a system of record, draft-and-approve.
- Multi-step agent — tool-use across systems, long-horizon tasks.
- Decision-support / forecasting — analytical, model-driven, advisory.
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 · 117 lines · 144 tokens per session scan A 903d55158c4f
general-use-case-discovery is a skill published in the GitHub repository geledek/enterprise-ai-transformation-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 144 tokens to every session and 2,110 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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