Magnus HedemarkandGitHub
1291f9576e
* fix: SkillOpt epoch 1 for AI operating economics Promote cold-load entry points, quick-start reference routing, the minimum decision-record contract, and trigger-oriented progressive disclosure. Signed-off-by: Magnus Hedemark <magnus919@pm.me> * fix: SkillOpt epoch 2 for AI operating economics Add review-depth selection, evidence-to-disposition guidance, and scenario-led routing across adjacent skills. Signed-off-by: Magnus Hedemark <magnus919@pm.me> * fix: SkillOpt epoch 3 for AI operating economics Expose a minimum claim ledger and explicit closure conditions for every bounded disposition. Signed-off-by: Magnus Hedemark <magnus919@pm.me> * fix: resolve SkillOpt review consistency findings Align entry-point paths, canonical step routing, claim-ledger fields, and triage disposition wording. Signed-off-by: Magnus Hedemark <magnus919@pm.me> * fix: resolve final SkillOpt disposition wording Keep review-depth outputs inside the canonical disposition set and distinguish supported claims from permitted language. Signed-off-by: Magnus Hedemark <magnus919@pm.me> * fix: complete SkillOpt routing correction Route triage through the outcome-map step and identify the evidence-classification step explicitly. Signed-off-by: Magnus Hedemark <magnus919@pm.me> * fix: complete AI economics review template Add the minimum decision-record fields required by the optimized skill routing contract. Signed-off-by: Magnus Hedemark <magnus919@pm.me> --------- Signed-off-by: Magnus Hedemark <magnus919@pm.me>
AI Operating Economics
A decision method for determining whether an AI-enabled workflow is creating value at an acceptable cost, quality, and human-impact boundary.
Why Install This Skill
AI pilots often produce an attractive number: faster handling time, more tasks completed, lower apparent cost, or high adoption. That number is rarely enough to decide whether the intervention should scale. It may omit review work, infrastructure, quality loss, worker differences, customer effects, or the cost of changing the surrounding process.
This skill helps an agent connect those dimensions into one accountable decision. It distinguishes measured workflow evidence from vendor claims, separates speed from value, inspects who benefits and who bears the cost, and produces a bounded recommendation: scale, constrain, redesign, hold, retire, or exception.
After installing it, your agent can prepare an AI initiative evidence record, challenge weak ROI claims, design a value-realization review, and tell you exactly what evidence is missing before more authority or spend is granted.
What You Get
| File | What it provides |
|---|---|
SKILL.md |
Core routing, operating principles, nine-step workflow, evidence classes, dispositions, pitfalls, and verification checklist |
references/evidence-method.md |
Detailed comparison design, cost-boundary, worker-impact, uncertainty, and learning-loop method |
references/source-index.md |
Primary and independent sources with claim scope, caveats, and permitted use |
templates/ai-initiative-evidence-record.md |
Fillable record for one AI workflow or use case |
templates/ai-economics-review.md |
Executive or lifecycle review template for one or more initiatives |
evals/evals.json |
Six output-quality cases covering ROI claims, heterogeneous effects, incomplete TCO, vendor evidence, authority, and retirement |
Quick Start
No setup, API keys, or runtime dependencies are required.
Ask your agent:
Review this AI pilot and tell me whether we should scale it. Separate measured outcomes, cost, quality countermetrics, worker effects, evidence gaps, and the authority we should grant next.
For a durable review, ask it to use templates/ai-initiative-evidence-record.md and save the completed record in your normal project documentation system.
Triggers
Load this skill when you need to:
- Evaluate an AI use case, pilot, agent, or automation for value realization
- Decide whether to scale, constrain, redesign, hold, or retire an AI workflow
- Review AI productivity, savings, adoption, or transformation claims
- Connect AI cost attribution to workflow outcomes and quality
- Assess worker, customer, user, or distributional effects of an AI intervention
- Prepare an AI business case or post-launch value review
Requirements
- No runtime dependencies or external services
- A stated workflow and decision owner are strongly recommended
- Financial, statistical, analytics, evaluation, governance, and runtime details are routed to adjacent skills