Apply this skill when deciding whether an AI agent, workflow automation, internal tool, or operational integration is economically worth building, expanding, replacing, or retiring by comparing human touch and wait time, exception density, stable machine-readable boundaries, expected cost per accepted outcome, human alternatives, supervision, failure recovery, maintenance, break-even volume, throughput value, effective lifetime, NPV, and independent safety gates.
설치
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Apply this skill when deciding whether an AI agent, workflow automation, internal tool, or operational integration is economically worth building, expanding, replacing, or retiring by comparing human touch and wait time, exception density, stable machine-readable boundaries, expected cost per accepted outcome, human alternatives, supervision, failure recovery, maintenance, break-even volume, throughput value, effective lifetime, NPV, and independent safety gates.
Decide whether automation produces cheaper accepted outcomes, useful capacity, or strategic value
after supervision, failure recovery, maintenance, redesign, and safety costs are included. Prevent a
cheap model call or impressive throughput demo from being mistaken for a viable operating system.
Use When
A team asks whether to build, buy, expand, replace, or retire an AI agent, workflow automation,
integration, internal tool, or operational pipeline.
A proposal compares automation cost with human work, headcount, outsourcing, queue delay,
conversion, revenue, SLA, error cost, or throughput.
The decision needs expected cost per accepted outcome, break-even volume, implementation and
maintenance cost, effective lifetime, scenario analysis, discounting, or NPV.
A claimed saving excludes supervision, retries, manual review, incident response, rejected output,
redesign, vendor changes, security, compliance, or rollback cost.
A team needs to rank repetitive work by human touches, handoffs, queue delay, error exposure,
exception rate, stable data access, or maintenance burden before choosing an automation candidate.
Do Not Use When
The task only optimizes LLM token spend, prompt caching, model routing, or per-request inference
cost inside an already-approved product; use llm-token-cost-control-review.
The task only analyzes cloud infrastructure spend; use cloud-cost-guardrail-review.
The task only selects a vendor, framework, runtime, or platform; use
technology-stack-selection and use this skill only for the investment model.
The task only evaluates technical agent quality or safety; use the matching agent, eval, security,
privacy, or rollout skill. Economic return never overrides a failed safety gate.
Required Inputs
Outcome contract: one accepted business outcome, rejection or correction rule, quality floor,
latency or SLA, volume, demand pattern, current backlog, and value owner.
Human comparator: labor time and loaded cost, tooling, management, training, queue delay, error and
rework rates, review, escalation, coverage, turnover, and capacity constraints.
Workflow-friction ledger: human touches, clicks, copies, downloads, renames, reauthentication,
handoffs, approvals, waiting states, interruption cost, backlog age, and customer or revenue delay.
Reliability ledger: success, accepted without correction, accepted after correction, rejected,
silent failure, duplicate or harmful effect, rollback or compensation, incident probability, and
cost distribution.
Safety gate: legal, privacy, security, authorization, irreversibility, human-oversight, audit, and
rollback requirements that must pass independently of ROI.
Preconditions
Define accepted outcome and the current alternative before comparing model or vendor prices.
Refresh price, wage, contract, volume, error, and lifecycle assumptions from current authoritative
sources when the decision is current or material.
Label unknowns and ranges. Do not turn a vendor benchmark, example number, or one pilot into a
universal production rate.
Keep command execution under .mustflow/config/commands.toml; this skill does not authorize
purchases, contracts, staffing changes, production rollout, or releases.
Allowed Edits
Add or refine decision records, assumptions, cost models, scenario tables, break-even analysis,
NPV models, evidence links, sensitivity analysis, safety gates, tests for calculation logic, docs,
route metadata, and synchronized templates.
Separate measured, quoted, estimated, inferred, and unknown values.
Do not embed universal numeric thresholds, vendor prices, wage rates, error rates, discount rates,
or useful-life assumptions in reusable policy.
Do not convert throughput into value unless demand, queue, revenue, SLA, risk, or another binding
constraint makes extra completed work valuable.
Procedure
Map human contact and waiting before pricing the automation. Count every copy, click, file move,
system hop, reauthentication, handoff, approval, follow-up, and manual verification. Separate
active labor from elapsed queue delay and interruption cost; a short decision surrounded by
repeated system handling may be a stronger candidate than a longer uninterrupted task.
Classify the decision surface. Measure the normal-path share and enumerate exception families.
Prefer automating a stable normal lane and routing bounded exceptions to a person over pretending
that every case follows one rule. If each case requires materially different judgment, price the
work as a new decision system rather than a small automation.
Require observable boundaries. Name machine-verifiable start and completion evidence and the
stable data path that carries the work. Treat browser clicking, screen scraping, manual exports,
mutable page text, and shared login state as maintenance debt unless no durable API, event,
database, queue, file, or schema boundary is available.
Define one accepted outcome. Count the business result that passes the quality and safety bar,
not a model response, tool call, attempted task, or superficially completed workflow.
Model automation expected variable cost per accepted outcome. Include ordinary execution,
supervision, review, retries, rejected work, failure recovery, rollback or compensation, and the
probability-weighted tail of incidents. Preserve distributions where rare failures dominate.
Add fixed and step-fixed costs. Include design, implementation, integration, data work, evals,
security, compliance, rollout, training, change management, monitoring, maintenance, vendor and
model migrations, and decommissioning.
Build the human or current-system comparator on the same outcome contract. Include loaded labor,
tools, review, rework, delay, error cost, escalation, management, training, and capacity limits.
Do not compare automation's perfect-path variable cost with a human's fully loaded cost.
Separate substitution, assistance, and new capability. An assistant may save minutes without
removing a role; a new service may create demand rather than replace cost; a constrained queue may
make throughput valuable even when unit cost is higher.
Value throughput only when a binding constraint exists. Tie added capacity to observed backlog,
avoided SLA penalties, faster cash collection, additional conversion or revenue, reduced risk,
or another evidence-backed outcome. Otherwise report it as unused headroom.
Calculate break-even with ranges. Compare fixed investment with the expected per-accepted-outcome
advantage across conservative, base, and optimistic volume, quality, supervision, and failure
scenarios. Report when the denominator is zero, negative, or too uncertain for a meaningful
break-even point.
Use effective lifetime, not accounting optimism. Estimate how long the automation remains useful
before a product redesign, policy change, provider change, data drift, integration replacement,
or maintenance burden requires material reinvestment.
Discount scenario cash flows when timing matters. Show NPV and payback under explicit assumptions,
including ramp-up, delayed benefits, recurring maintenance, redesign, migration, and exit cost.
Run sensitivity and threshold analysis. Identify which assumptions can reverse the decision:
accepted-outcome rate, review time, incident cost, volume, demand value, maintenance burden,
vendor pricing, lifetime, or discount rate. Ask for evidence on those first.
Keep safety as a hard independent gate. Reject, narrow, or keep human execution when the design
cannot meet authorization, privacy, legal, irreversible-effect, audit, reconciliation, or
rollback requirements, even if the expected financial return is positive.
Choose a reversible next step. Prefer a bounded shadow comparison that records the human result,
automation result, exception class, correction effort, and accepted outcome without committing
external effects. Define the evidence required to move from shadow to recommendation, approval,
bounded automatic execution, expansion, redesign, pause, or retirement.
Postconditions
Automation and the current alternative use the same accepted-outcome definition.
Human touches, wait cost, exception density, machine-verifiable boundaries, and maintenance owner
are visible before the investment decision.
Variable, fixed, supervision, failure, maintenance, redesign, and exit costs are visible.
Break-even, throughput value, effective lifetime, and NPV use explicit ranges and evidence levels.
Safety and legal feasibility remain hard gates independent of financial return.
Verification
Use configured oneshot command intents when available: changes_status, changes_diff_summary,
docs_validate_fast, test_release, and mustflow_check. Use narrower configured calculation,
data-quality, or decision-record checks when available. Do not infer purchasing, billing, staffing,
production, deployment, or vendor commands.
Failure Handling
If accepted outcome, comparator, volume, or safety gate is missing, report the case as
non-decision-ready instead of manufacturing a return.
If one uncertain assumption dominates the result, provide the threshold at which the decision
changes and propose the smallest measurement that can resolve it.
If benefits depend on unproven demand or queue value, separate operational capacity from financial
benefit.
If start or completion remains a human impression, or the only input path is unstable UI
automation, classify the proposal as rule-definition or integration work and price that missing
boundary explicitly.
If a positive case requires ignoring rare catastrophic loss, keep the tail risk explicit and hand
the safety decision to the named authority.
Output Format
Accepted outcome and current comparator
Fixed, variable, supervision, failure, maintenance, redesign, and exit costs
Volume, quality, demand, lifetime, and discount assumptions with evidence levels
Break-even, payback, NPV, sensitivity, and throughput-value findings
Independent safety-gate result
Reversible next experiment and stop or expansion criteria
