Forge performance
Skill is-bo/fullstack-forge-skill/src/fullstack-forge/commands/forge-performance
A production full-stack engineering skill suite for AI coding agents—covering UI, UX, security, databases, auth, caching, testing, performance, and deployment.
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What its author says it does
Copied from the file, not written here
Measure and improve user- and system-visible latency, throughput, resource use, and stability without guessing. Activate automatically for performance-sensitive workflows when that concern is relevant to a software-engineering request.
SKILL.md
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forge-performance: Performance
Purpose
Measure and improve user- and system-visible latency, throughput, resource use, and stability without guessing.
This is an agent playbook, not a claim of standalone analyzer coverage. Apply
fullstack-forge/references/shared/module-contract.md
for common applicability, evidence, command-safety, mutation, verification, and completion rules.
Never hide failed checks or claim that an operation ran when it did not.
Automatic activation signals
Activate when a request or direct repository evidence involves performance, when
the user explicitly names forge-performance, or when discovery proves an applicable boundary.
- Performance-sensitive workflows
- Known regressions
- Release budgets
When not to activate
- No claimed or measured performance requirement
Automated support
Relevant discovery inputs are:
- performance budgets
- build artifacts
- profiles, traces, and load results
Available deterministic support, where present:
- Use
detect-project-commandsfor its bounded evidence when present; treat unavailable runtime evidence asNOT_VERIFIED. - Use
run-project-commandfor its bounded evidence when present; treat unavailable runtime evidence asNOT_VERIFIED.
Agent inspection procedure
- Establish the measured baseline first: collect Core Web Vitals (LCP, INP, CLS), API latency percentiles, and database timings from real tooling, never estimates.
- Profile the critical user flow and identify the dominant cost: network waterfall, bundle, rendering, query, or serialization.
- Inspect payloads: bundle composition, image and font delivery, compression, and response sizes.
- Trace the slowest database interactions to query plans and the cache hit ratios that matter.
- Verify mobile and slow-device behavior with throttled profiles, and record background-job throughput where it gates user-visible outcomes.
Manual inspection requirements:
- Validate workload realism and user impact
- Review production traces when authorized
Stack-specific guidance:
- Use production builds and framework profilers, never development timing as release proof
Evidence to collect
For formal findings, also follow fullstack-forge/references/PROTOCOL.md. Record the module's
inspected boundary, relevant tests, direct observations, and unavailable evidence.
Primary standards used as criteria, not proof of compliance:
- Core Web Vitals
- OpenTelemetry semantic conventions
Common production failures
- Define representative workloads, devices, networks, data sizes, and percentile budgets
- Measure frontend Core Web Vitals, bundle cost, server latency, database time, memory, CPU, I/O, and external calls as applicable
- Identify the dominant bottleneck before changing code and check cold starts, concurrency, leaks, and backpressure
Missing-control checks
For every applicable criterion below, attach direct evidence or record a reasoned
NOT_APPLICABLE, NOT_VERIFIED, or BLOCKED status. The list is a routing checklist, not
evidence by itself.
- LCP
- INP
- CLS
- Bundle size
- Images
- Fonts
- Network waterfalls
- API latency
- Database latency
- Cache behavior
- Memory
- CPU
- Startup time
- Payload size
- Compression
- Streaming
- Third-party latency
- Background-job throughput
- Rendering cost
- Large lists
- Mobile performance
- Slow-device behavior
Commands and tools
- Run
forge performance audit --jsonorfullstack-forge performance audit --jsonwhen an explicit audit is requested and the CLI is installed. Normal feature work does not require it. - Use the deterministic support named above only for its documented bounded evidence.
Safe fixes
- Remove proven duplicate work and add bounded pagination
- Declare dimensions and lazy-load noncritical assets after measurement
Approval-required changes
- Adding infrastructure, caches, denormalization, or behavior-changing approximations
Verification
- Repeat the same benchmark with uncertainty and environment recorded
- Confirm correctness and tail latency did not regress
Completion contract
Apply the shared module contract and the module-specific limitations below.
Known limitations
- Do not invent performance measurements or extrapolate from unrelated hardware
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.