* test(evals): scope claims to harness model fixtures and revision Append the neckbeard claims-scoping sentence to one representative expected_output per per-skill manifest so every corpus member states VAL-EVL-032 scope (harness, model, fixtures, revision under test). Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(product-lifecycle): upgrade integrated launch trajectory Add an explicit launch-decision assertion to the new-product lifecycle case so the integrated product-launch scenario terminates in a launch decision recorded as a lifecycle evidence-ledger entry (VAL-CRP-010), and scope its expected_output claims per VAL-EVL-032. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(production-excellence): add integrated migration reconciliation failure case Add integrated-migration-reconciliation-failure: the production-excellence gate model returns No-go on a reconciliation mismatch, records the failure evidence, produces a rollback/roll-forward decision with an accountable owner, and does not proceed to launch (VAL-CRP-012). Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(agent-production-operations): add privacy boundary escalation case Add integrated-privacy-boundary-escalation (VAL-CRP-015): the runtime control plan halts a cross-boundary EU PII trace export before any data processing, names the privacy boundary, and escalates to jurisdiction- specific legal review and a human operator. Also add a tool-authority- health handoff assertion to the read-only contract case (VAL-CRP-016). Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(lifecycle-evals): add lifecycle evaluation corpus Add the #204 corpus home: run tooling (run-corpus.sh, fake adapter only), programmatic coverage validator (validate-corpus-coverage.py), machine- readable coverage index + human-readable coverage matrix, regression- detection and fixture/source notes, the bounded discovery brief, and a one-snapshot committed set of fake-adapter per-trial run artifacts with harness/model/date scoping fields. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> --------- Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
Capacity and Cost Engineering
Connect demand, performance, reliability, and spend into defensible capacity and cost decisions.
Why Install This Skill
Every service that serves users has a capacity limit and a cost. When your agent can model capacity, calculate unit cost, define budget controls, and require load-test evidence for capacity claims, it stops treating infrastructure as "someone else's problem" and starts making decisions that respect real-world constraints. This skill fills the gap between the financial team's P&L models (which don't know what a request costs in compute) and the platform team's infrastructure-as-code (which doesn't know why a specific SLO target was chosen or what it costs).
After installing this skill, your agent can: project capacity from growth forecasts with utilization targets and scaling triggers; calculate what one request or one user costs to serve at the infrastructure level; define budget thresholds with operational consequences (alert, throttle, deny); design load and soak tests as mandatory capacity evidence — not optional nice-to-haves; and resolve SLO-cost tradeoffs with explicit evidence, ownership, and accountability. When a product manager asks "what would it cost to serve 2x the users?", your agent has a structured answer instead of a guess.
What You Get
| Directory | What it provides |
|---|---|
SKILL.md |
Core methodology: connected dimensions (demand/performance/reliability/spend), working method with five steps, four named scenarios (growth, peak, degraded, cost-constrained), routing table to adjacent skills, and guardrails against generic cloud-cost tips and universal utilization targets |
README.md |
This human-facing overview |
references/discovery-brief.md |
Ownership boundary analysis comparing seven adjacent skills (financial-modeling, platform-engineering, site-reliability-engineering, product-analytics-and-measurement, production-readiness, product-roadmapping-and-portfolio, resilience-and-recovery) with explicit routing decisions |
templates/capacity-model.md |
Fillable capacity model: demand assumptions, capacity-unit mapping, utilization targets with rationale, scaling triggers, evidence sources, ownership, and tradeoffs |
templates/unit-economics-record.md |
Fillable unit-economics record: unit definition, cost numerator with allocation method, demand denominator, unit-cost calculation formula, cost-per-SLO comparison, and structured assumptions/evidence/ownership/tradeoffs fields |
templates/load-soak-test-plan.md |
Fillable load/soak test plan: objective, target throughput, duration, environment requirements, success criteria (latency percentiles, error rate, utilization), data collection, and evidence record |
templates/budget-quota-decision.md |
Fillable budget/quota decision: budget owner, period, thresholds (alert/soft/hard), quota/rate-limit configuration, enforcement mechanism, operational behavior at each threshold, cost attribution, and approval |
templates/slo-cost-tradeoff-record.md |
Fillable SLO-cost tradeoff record: SLO under discussion, current and projected cost, alternative SLO comparison, degradation path, error budget impact, accountable owner, and approval |
evals/evals.json |
Five output-quality evaluation cases covering growth forecast, peak event, SLO-cost conflict, quota decision, and misleading unit-cost calculation |
Quick Start
No setup required. The skill is pure methodology — no scripts, no API keys, no runtime dependencies.
To use: ask your agent to model capacity for a service, calculate unit cost, define budget controls, plan a load test, or resolve an SLO-cost tradeoff. The skill loads when the task matches its trigger conditions and provides step-by-step guidance plus fillable templates for each artifact.
Triggers
Load this skill when the task involves:
- Projecting capacity from a growth forecast
- Sizing capacity for a peak event (launch, seasonal, Black Friday)
- Calculating unit cost at the infrastructure level
- Defining budget thresholds, spending alerts, or hard caps
- Designing quota or rate-limit enforcement
- Planning or reviewing a load or soak test as capacity evidence
- Resolving an SLO-cost tradeoff or cost-constrained reliability decision
- Reviewing a cost anomaly or attributing cost to services/teams
Requirements
- No runtime dependencies, API keys, or system tools.
- No specific Python version, package, or service required.
- The skill references templates that any agent can fill; no special tooling is needed.