{"slug":"magnus919-ai-operating-economics","name":"ai-operating-economics","description":">-","long_description":"---\nname: ai-operating-economics\ndescription: >-\n  Use when deciding whether an AI-enabled workflow should be adopted, scaled,\n  constrained, redesigned, or retired, and the decision must connect business\n  outcomes, worker or user effects, quality guardrails, full operating cost,\n  telemetry, uncertainty, and accountable governance. Do not use for a standalone\n  financial model, infrastructure cost calculation, agent evaluation design,\n  runtime operations, or general AI governance; route those details to the\n  neighboring specialist skills.\nlicense: MIT\ncompatibility: Agent-agnostic methodology; no runtime dependency.\nmetadata:\n  tags: ai-economics, value-realization, ai-adoption, outcome-measurement, cost-attribution, worker-impact, evidence-led-decisions\n  source: \"Synthesized from primary and independent sources listed in references/source-index.md\"\n---\n\n# AI Operating Economics\n\n## Overview\n\nAI initiatives are operating interventions, not merely model purchases or ROI spreadsheets. Their value depends on what work changes, who benefits, what quality or risk changes with it, what the complete intervention costs, and whether the organization can observe and govern those changes.\n\nThis skill provides the cross-domain decision spine for evaluating an AI-enabled workflow. It does not replace financial modeling, product measurement, statistical inference, agent evaluation, runtime operations, or AI governance. It makes those inputs meet in one accountable decision record.\n\nThe core question is not “Did the model make people faster?” It is: “What changed in this workflow, for whom, at what full cost, with what outcome and countermetric evidence, and what authority should the organization grant next?”\n\n## Entry Points\n\n| Starting state | Start with | Primary artifact or route |\n|---|---|---|\n| Idea or proposed AI workflow | Steps 1–2 | `templates/ai-initiative-evidence-record.md` |\n| Existing pilot or outcome data | Steps 3–7 | `references/evidence-method.md` plus the evidence record |\n| Request for broader population or side-effect authority | Steps 7–8; load `references/evidence-method.md` section 7a for the governance packet | Governance evidence packet plus the evidence record |\n| Executive, portfolio, launch, or lifecycle review | Steps 8–9 | `templates/ai-economics-review.md`; route launch/runtime details onward |\n| Standalone financial, statistical, telemetry, runtime, or governance implementation task | When Not to Use | Named adjacent specialist skill |\n\n## When to Use\n\nLoad this skill when the user needs to:\n\n- Build an evidence-backed business case for an AI use case or agentic workflow.\n- Decide whether an AI pilot should scale, remain bounded, be redesigned, or stop.\n- Review claimed AI productivity, savings, adoption, or transformation results.\n- Design an AI value-realization or post-launch outcome review.\n- Connect model and tool spend to workflow outcomes and worker or customer effects.\n- Compare AI options while accounting for measurement uncertainty and non-comparable evidence.\n- Prepare an executive, product, portfolio, or lifecycle decision about an AI-enabled intervention.\n\n## When Not to Use\n\n| If the task is primarily... | Route to | This skill still contributes... |\n|---|---|---|\n| Financial statements, pricing, CAC/LTV, runway, or SaaS metrics | [financial-modeling](../financial-modeling/SKILL.md) | The AI workflow's outcome and cost evidence can feed the model |\n| Token, infrastructure, quota, capacity, or SLO-cost modeling | [capacity-and-cost-engineering](../capacity-and-cost-engineering/SKILL.md) | The economic decision can consume the resulting cost boundary |\n| Metric trees, event schemas, instrumentation QA, or product dashboards | [product-analytics-and-measurement](../product-analytics-and-measurement/SKILL.md) | The decision defines which outcome and countermetric evidence matters |\n| Experimental design, causal inference, statistical testing, or power analysis | [data-scientist](../data-scientist/SKILL.md) | The decision specifies the claim and comparison it must support |\n| Agent datasets, graders, traces, regression analysis, or telemetry implementation | [agent-evals-and-observability](../agent-evals-and-observability/SKILL.md) | The decision consumes verified evaluation and telemetry evidence |\n| Production rollout, runtime budgets, authority, fallback, escalation, or disablement | [agent-production-operations](../agent-production-operations/SKILL.md) | The decision sets the evidence and authority boundary |\n| Organization-wide AI risk, policy, compliance, or governance operating models | [ai-governance](../ai-governance/SKILL.md) | The initiative record supplies an operating case and unresolved gaps |\n| Launch-readiness packet or production go/no-go decision | [production-readiness](../production-readiness/SKILL.md) | The initiative disposition becomes one readiness input |\n| General product governance cadence without an AI-specific value question | [product-operations-and-governance](../product-operations-and-governance/SKILL.md) | Use this skill only for the AI-specific value and operating-economics question |\n\n## Non-Negotiable Reasoning Rules\n\n1. **Workflow evidence beats model evidence.** A benchmark, demo, or vendor claim does not establish value in the target workflow.\n2. **Speed is not value.** Time saved can be spent on lower-value work, offset by review and exception handling, or enable higher-value work. Measure the business or user outcome directly.\n3. **Averages are not enough.** Inspect worker, user, task, geography, tenure, risk, and quality slices. An aggregate gain can hide a subgroup loss.\n4. **Every benefit metric needs a countermetric.** Pair throughput or cost with quality, safety, customer, worker, privacy, or reliability measures appropriate to the workflow.\n5. **Token cost is not total cost.** Include model calls, tools, retrieval, storage, networking, observability, engineering, human review, change management, governance, and unused committed capacity when material.\n6. **Evidence classes must stay separate.** Label observed results, causal estimates, inferences, vendor-reported findings, stakeholder assertions, and normative requirements distinctly.\n7. **Missing evidence is a decision input.** Do not turn an unknown into a favorable assumption. Record the gap, owner, consequence, and next evidence needed.\n8. **Authority follows evidence.** A positive pilot does not justify unrestricted autonomy. Scale capability and authority in bounded slices with explicit reversal conditions.\n9. **Do not manufacture precision.** Use ranges, scenarios, sensitivity, and confidence where inputs are uncertain. Do not rank non-comparable studies or vendors.\n10. **The decision is reversible only if the artifact says how.** Record the stop trigger, rollback or containment path, decision owner, and review date.\n\n## Core Workflow\n\nUse this sequence for an AI initiative review. Load the detailed method and the evidence-record template when the task requires a durable artifact.\n\n### Quick Start by Need\n\n| Need | First action | Load next |\n|---|---|---|\n| Triage a claim | Name the workflow, decision, and evidence class | Steps 1–3; evidence classes are defined in Step 7 |\n| Build a durable record | Copy the initiative evidence record and complete the header first | `templates/ai-initiative-evidence-record.md` |\n| Investigate uncertain evidence | Freeze the claim table before drafting conclusions | `references/evidence-method.md` |\n| Prepare a review | Assemble evidence, slices, cost, gaps, and disposition | `templates/ai-economics-review.md` |\n\n### Choose Review Depth\n\n| Mode | Use when | Minimum evidence | Output |\n|---|---|---|---|\n| Triage | A claim or opportunity needs a bounded first decision | Workflow, value hypothesis, one outcome, one countermetric, known gaps | Hold, with a routing/evidence plan |\n| Standard | A pilot or workflow decision can change population or investment | Comparison, outcome/countermetrics, slices, cost boundary, owner, reversal path | Scale, constrain, redesign, or hold |\n| High-assurance | Authority, sensitive data, material user impact, or irreversible change is involved | Standard evidence plus governance packet, human oversight, incident/revalidation, and decommissioning evidence | Scale only within an explicit authority boundary, or Hold |\n\n### 1. Define the intervention and decision\n\nName the workflow, population, task boundary, intervention mode, baseline, decision sought, and decision owner. State whether the AI assists, recommends, routes, executes, or replaces/removes work. Define what remains human-controlled.\n\nDo not begin with the model name or a claimed percentage. Begin with the work that changes and the decision the evidence must support.\n\n### 2. State the value hypothesis\n\nWrite a falsifiable hypothesis:\n\n> For [population] doing [workflow], [intervention] will change [outcome] by [direction/range] without exceeding [countermetric boundary], at [full operating cost boundary], compared with [baseline], over [period].\n\nIf the proposed outcome is only “productivity,” decompose it into the actual customer, employee, operational, financial, or mission outcome. If the outcome cannot be observed or credibly proxied, mark the initiative measurement-incomplete rather than inventing a proxy.\n\n### 3. Build the outcome and countermetric map\n\nDefine:\n\n- Primary outcome: the result the initiative exists to improve.\n- Leading indicators: early evidence that the mechanism is operating.\n- Countermetrics: quality, safety, customer, worker, privacy, reliability, or equity measures that could worsen.\n- Adoption and substitution measures: who uses the system, what work changes, and what work is displaced or added.\n- Guardrail thresholds: contextual limits with an owner and response.\n\nRoute metric definitions and instrumentation plans to product analytics. Route statistical or causal design to data science. This skill owns the connection between the evidence and the decision, not the detailed statistical method.\n\n### 4. Establish the full economic boundary\n\nRecord both:\n\n- **Marginal economics:** what changes when one more task, user, or workflow unit is served.\n- **Fully loaded economics:** the costs required to make the intervention available and govern it.\n\nAt minimum consider inference, tool use, retrieval, storage, data transfer, observability, engineering, evaluation, human review, training, support, change management, governance, security, and committed capacity. Separate fixed, variable, step-function, and avoided costs. Define the denominator precisely: task, resolved case, completed workflow, active user, customer outcome, or another meaningful unit.\n\nRoute the detailed model to capacity-and-cost-engineering or financial-modeling. Never divide total spend by an undifferentiated request count when requests have materially different resource or outcome profiles.\n\n### 5. Design the evidence comparison\n\nChoose the strongest feasible comparison before interpreting results:\n\n- Randomized or staggered rollout when feasible.\n- Matched or difference-in-differences comparison when appropriate.\n- Within-workflow baseline with explicit pre-period and seasonality limits.\n- Controlled pilot with a documented task and population boundary.\n- Descriptive before/after evidence only when stronger designs are infeasible, labeled accordingly.\n\nRecord selection effects, learning effects, concurrent initiatives, task-mix changes, worker self-selection, quality measurement gaps, and changes in pay or incentives. If the comparison cannot support the requested claim, narrow the claim rather than upgrading the method rhetorically.\n\n### 6. Segment before aggregating\n\nReport the overall result and inspect slices that could change the decision:\n\n- Worker experience, skill, role, and training status.\n- Task complexity, risk, volume, and exception rate.\n- Customer or user segment.\n- Geography, language, accessibility, and rele","tagline":">-","category":"automation","commerce":{"type":"unknown","billing":"unknown","amount":null,"currency":null,"sourceUrl":null,"checkedAt":null,"runtime":"unknown","purchaseUrl":null,"checkout":"external","purchaseRequiresUserConsent":true},"tags":["agent-skill"],"author":"magnus919","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github candidate review","sourceDetail":"magnus919/agent-skills","creatorName":"magnus919","creatorUrl":"https://github.com/magnus919","sourceUrl":"https://github.com/magnus919/agent-skills/tree/main/ai-operating-economics","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/magnus919-ai-operating-economics#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals."},"stats":{"stars":76,"forks":7,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":31.21},"quality":{"score":60,"tier":"promising","label":"Promising","summary":"Useful candidate, but compare it with alternatives before adopting.","signals":[{"label":"GitHub stars","value":"76","tone":"neutral"},{"label":"Freshness","value":"28d ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":[]},"trust":{"version":"trust-score-v5","score":64,"base_score":72,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.","recommendedAction":"Run only in a sandbox and compare close alternatives before using it for real work.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["64/100 Trust Score v5","72/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is available","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":48,"weight":0.13,"status":"warn","detail":"76 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":43,"weight":0.08,"status":"warn","detail":"76 stars, 7 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"28d since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":70,"weight":0.14,"status":"info","detail":"Public metadata needs stronger README/SKILL.md context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":72,"weight":0.12,"status":"info","detail":"credential or environment access"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add magnus919/agent-skills --skill ai-operating-economics"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":74,"weight":0.07,"status":"info","detail":"secrets or environment access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/magnus919/agent-skills/tree/main/ai-operating-economics"},{"id":"review_status","label":"Review status","score":46,"weight":0.05,"status":"warn","detail":"AI review approval is missing"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"warn","label":"GitHub adoption","detail":"76 GitHub stars"},{"status":"warn","label":"Stars/forks activity","detail":"76 stars, 7 forks; 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require human review before any live investment decision.","Quality score needs review","GitHub adoption: 76 GitHub stars","Stars/forks activity: 76 stars, 7 forks; issue activity unavailable in current metadata","Review status: AI review approval is missing","No real agent outcome reports yet","Human review required before unattended installation"],"evidence":{"stars":"76 GitHub stars","repoActivity":"76 stars, 7 forks","lastPushed":"28d since push","license":"MIT","repository":"https://github.com/magnus919/agent-skills/tree/main/ai-operating-economics","install":"npx skills add magnus919/agent-skills --skill ai-operating-economics","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"human_review_before_install"},"installReadiness":{"ready":true,"command":"npx skills add magnus919/agent-skills --skill ai-operating-economics","policy":"human_review_before_install","label":"Human review before install","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","28d since push","Financial domain: human review is required before use in a live investment workflow.","Trust Score v5 requires review or sandbox-only use before install."]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["AI review approval is missing","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","GitHub adoption: 76 GitHub stars","Stars/forks activity: 76 stars, 7 forks; issue activity unavailable in current metadata"]},"outcomeEvidence":{"total":0,"successes":0,"failures":0,"notRelevant":0,"successRate":null,"installAttempts":0,"riskBlocked":0,"setupRequired":0,"installSuccessRate":null,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"recentSuccessRate":null,"recentFailureRate":null,"uniqueAgents":0,"agentProvenScore":0,"agentProvenLabel":"Needs first agent run","lastOutcomeAt":null,"label":"No agent outcome data yet"},"autoInstall":{"allowed":false,"sandboxRequired":true,"policy":"human_review_before_install","reason":"Compare alternatives before installing."},"outcome_loop":{"version":"openagentskill-agent-outcome-v4","required_after_install":true,"endpoint":"/api/agent/outcome","method":"POST","event_id_source":"feedback.event_id, install_receipt.resolve_event_id, or decision_packet.outcome_feedback.event_id","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"required_fields":["event_id","skill_slug","task"],"quality_fields":["task_success","output_quality","error_type","human_review_required","used_in_production","workspace","evidence_url","time_to_useful_ms","source_version"],"ranking_inputs_updated":["Trust Score v5 outcome confidence","Agent Proven Score","Resolve ranking task-fit evidence","Skill detail machine-readable metadata","Outcome leaderboard"]},"agent_contract":{"suited_tasks":["automation","agent-skill"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"install_command":"npx skills add magnus919/agent-skills --skill ai-operating-economics","trust_score":64,"trust_version":"trust-score-v5","risk_level":"medium","do_not_use_when":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace","Autonomous investment, trading, tax, or suitability decisions without a qualified human review"],"before_install":["Read the audit page and machine-readable metadata.","Confirm the install command, license, and permission surface fit the workspace.","Get explicit human approval or choose an alternative before installing."],"after_run":["Report the outcome to /api/agent/outcome using the resolve event id.","Include output_quality, workspace, human_review_required, and evidence_url when available.","Re-resolve before broad production rollout."]},"bestFor":["automation","agent-skill"],"doNotUseFor":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace","Autonomous investment, trading, tax, or suitability decisions without a qualified human review"],"knownRisks":["AI review approval is missing","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","GitHub adoption: 76 GitHub stars","Stars/forks activity: 76 stars, 7 forks; issue activity unavailable in current metadata","Review status: AI review approval is missing"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":72,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout."}}},"trust_score_v5":{"version":"trust-score-v5","score":64,"base_score":72,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.","recommendedAction":"Run only in a sandbox and compare close alternatives before using it for real work.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["64/100 Trust Score v5","72/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is available","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":48,"weight":0.13,"status":"warn","detail":"76 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":43,"weight":0.08,"status":"warn","detail":"76 stars, 7 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"28d since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":70,"weight":0.14,"status":"info","detail":"Public metadata needs stronger README/SKILL.md context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":72,"weight":0.12,"status":"info","detail":"credential or environment access"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add magnus919/agent-skills --skill ai-operating-economics"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":74,"weight":0.07,"status":"info","detail":"secrets or environment access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/magnus919/agent-skills/tree/main/ai-operating-economics"},{"id":"review_status","label":"Review status","score":46,"weight":0.05,"status":"warn","detail":"AI review approval is missing"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"warn","label":"GitHub adoption","detail":"76 GitHub stars"},{"status":"warn","label":"Stars/forks activity","detail":"76 stars, 7 forks; 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