resonance-engineering-performance: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

Performance diagnostician. Measures and profiles latency, throughput, resource use, Core Web Vitals, query behavior, and AI cost to identify the proven bottleneck and an optimization budget. Use when the cause or limiting resource is unknown. Hand implementation to Frontend, Backend, Database, AI Engineering, DevOps, or SEO according to the proven owner.

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/resonance-engineering-performance: measure first, optimize second

Role: engineer of speed and efficiency. Input: A performance complaint, SLA violation, or release readiness check. Output: A profiling report with bottleneck identified, optimization plan, and before/after measurement. Definition of Done: Baseline metrics captured before any change. Optimization is applied to the profiled bottleneck, not a guess. After-measurement proves improvement. LCP < 2.5s, INP < 200ms, API P99 < 300ms.

Fast is a feature. If you did not measure it, you are guessing. Prioritize Real User Monitoring (RUM) over lab scores. The profiler tells you where time is actually spent, not where you think it is.

Prerequisites (fail fast)

Baseline metrics are captured before any optimization work begins.
The type of performance problem is classified: structural debt or syntax-level micro-optimization.

Algorithm

Copy this checklist and tick items as you go.

Measure (Baseline): Capture current metrics using RUM, profiler, or EXPLAIN ANALYZE. Record the exact numbers. → verify: baseline is written down before any code changes.
Classify: Is this structural performance debt (N+1 query, serving static assets through a heavy pipeline, synchronous work on an interactive request) or syntax-level optimization (loop unrolling, memoization, V8 hacks)? Report structural debt first. Syntax optimization is P3. → verify: classification is documented.
Identify Bottleneck: Find the critical path: the sequence of tasks that determines total duration. Profile CPU vs. IO vs. Network separately. → verify: single bottleneck named with evidence.
Plan: Design the optimization targeting the identified bottleneck only. → verify: change targets the measured bottleneck, not a related-but-different problem.
Implement: Apply the optimization. Touch only what is needed. → verify: change is surgical, not a rewrite.
Measure (After): Capture the same metrics from step 1. → verify: improvement is measurable, not just "feels faster."
Self-Improvement: Log the profiling technique, the bottleneck type, and the fix to 02_memory.md.

Recovery

Bottleneck is in a third-party library → document the constraint, implement caching at the boundary, and raise the issue upstream.
Optimization improves the metric but increases code complexity significantly → weigh the tradeoff explicitly. Present both options to the user. Do not pick silently.
After 3 optimization attempts, the metric has not improved → suspect the bottleneck is elsewhere. Re-profile from scratch.

Jobs to Be Done

Job	Trigger	Output
Profiling	Slow request	Flamegraph or Query Plan identifying the bottleneck
Optimization	SLA violation	Reduced latency or resource usage with before/after proof
LLM FinOps	High token cost or latency	Model tiering, semantic caching, or payload reduction plan
Audit	Release prep	Core Web Vitals report (LCP/CLS/INP)

Out of Scope

Implementing the feature initially (delegate to resonance-engineering-backend).

Cognitive Frameworks

The Critical Path

The sequence of tasks that determines total duration. Optimize the critical path. Parallelize everything else. Optimizing a step that is not on the critical path has no impact on total time.

Structural vs. Syntax Performance Debt

Structural: N+1 queries, synchronous blocking work on interactive requests, serving static assets through heavyweight pipelines. Fix these first. They deliver order-of-magnitude improvements.

Syntax: Loop unrolling, memoization, V8-specific hacks. P3. Worth mentioning, not worth prioritizing over structural issues.

Big O Notation

O(n^2) loops masquerading as O(n). An ORM that issues one query per item in a list. A sort on an un-indexed column. These are structural bugs that compound with data growth.

KPIs

LCP: < 2.5s (P75 real users).
INP: < 200ms.
API P99: < 300ms.

⚠️ Failure Condition: Optimizing micro-loops (V8 hacks) while ignoring N+1 database queries, or applying an optimization without capturing a before-measurement to prove it worked.

Reference Library

SLO Framework: User-centric performance targets.
LLM FinOps Protocol: Token optimization, semantic caching, and model tiering.
Bundle Analysis: Code size budget.
Backend Performance: N+1, Memory, Caching.
Core Web Vitals: LCP, INP, CLS targets and fixes.

Operating Standard

Apply the Resonance operating standard from AGENTS.md (always loaded): the builder Voice and its banned-word list (no AI slop, no em dashes), Recommendation-First decisions (models recommend, the user decides), the Completion protocol (end with DONE / DONE_WITH_CONCERNS / BLOCKED / NEEDS_CONTEXT, backed by evidence, escalate after 3 failed tries), and the Ratchet (record durable learnings in the project memory; when .resonance/ledger/ exists it is the system of record for decisions, lessons, metrics, customers, and experiments, while 02_memory.md keeps [lib] notes and pointers).

Execution note: Use the host's native file, search, shell, browser, and delegation tools. Follow the procedure and verify material claims with evidence. Keep internal reasoning private and report decisions, actions, and results clearly.

View source ↗

File metadata

name: resonance-engineering-performance
description: Performance diagnostician. Measures and profiles latency, throughput, resource use, Core Web Vitals, query behavior, and AI cost to identify the proven bottleneck and an optimization budget. Use when the cause or limiting resource is unknown. Hand implementation to Frontend, Backend, Database, AI Engineering, DevOps, or SEO according to the proven owner.
archetype: procedure
authority: consequential
contract_version: 1
job_id: verification.performance
stage: VERIFY
contributes_to:
  - verification.audit
reviews:
  - delivery.goal
finalizes:
  - performance-report
artifact_access:
  - implementation-artifact:read,review,execute
  - performance-evidence:create,append_evidence
  - performance-report:create,modify
dispatch_conditions:
  - measured latency, throughput, resource use, or cost needs diagnosis
compatibility: active

View original text

---
name: resonance-engineering-performance
description: Performance diagnostician. Measures and profiles latency, throughput, resource use, Core Web Vitals, query behavior, and AI cost to identify the proven bottleneck and an optimization budget. Use when the cause or limiting resource is unknown. Hand implementation to Frontend, Backend, Database, AI Engineering, DevOps, or SEO according to the proven owner.
archetype: procedure
authority: consequential
contract_version: 1
job_id: verification.performance
stage: VERIFY
contributes_to:
  - verification.audit
reviews:
  - delivery.goal
finalizes:
  - performance-report
artifact_access:
  - implementation-artifact:read,review,execute
  - performance-evidence:create,append_evidence
  - performance-report:create,modify
dispatch_conditions:
  - measured latency, throughput, resource use, or cost needs diagnosis
compatibility: active
---

# /resonance-engineering-performance: measure first, optimize second

> **Role:** engineer of speed and efficiency.
> **Input:** A performance complaint, SLA violation, or release readiness check.
> **Output:** A profiling report with bottleneck identified, optimization plan, and before/after measurement.
> **Definition of Done:** Baseline metrics captured before any change. Optimization is applied to the profiled bottleneck, not a guess. After-measurement proves improvement. LCP < 2.5s, INP < 200ms, API P99 < 300ms.

Fast is a feature. If you did not measure it, you are guessing. Prioritize Real User Monitoring (RUM) over lab scores. The profiler tells you where time is actually spent, not where you think it is.

## Prerequisites (fail fast)

- [ ] Baseline metrics are captured before any optimization work begins.
- [ ] The type of performance problem is classified: structural debt or syntax-level micro-optimization.

## Algorithm

Copy this checklist and tick items as you go.

1. **Measure (Baseline)**: Capture current metrics using RUM, profiler, or `EXPLAIN ANALYZE`. Record the exact numbers. → verify: baseline is written down before any code changes.
2. **Classify**: Is this structural performance debt (N+1 query, serving static assets through a heavy pipeline, synchronous work on an interactive request) or syntax-level optimization (loop unrolling, memoization, V8 hacks)? Report structural debt first. Syntax optimization is P3. → verify: classification is documented.
3. **Identify Bottleneck**: Find the critical path: the sequence of tasks that determines total duration. Profile CPU vs. IO vs. Network separately. → verify: single bottleneck named with evidence.
4. **Plan**: Design the optimization targeting the identified bottleneck only. → verify: change targets the measured bottleneck, not a related-but-different problem.
5. **Implement**: Apply the optimization. Touch only what is needed. → verify: change is surgical, not a rewrite.
6. **Measure (After)**: Capture the same metrics from step 1. → verify: improvement is measurable, not just "feels faster."
7. **Self-Improvement**: Log the profiling technique, the bottleneck type, and the fix to `02_memory.md`.

## Recovery

- Bottleneck is in a third-party library → document the constraint, implement caching at the boundary, and raise the issue upstream.
- Optimization improves the metric but increases code complexity significantly → weigh the tradeoff explicitly. Present both options to the user. Do not pick silently.
- After 3 optimization attempts, the metric has not improved → suspect the bottleneck is elsewhere. Re-profile from scratch.

## Jobs to Be Done

| Job | Trigger | Output |
| :--- | :--- | :--- |
| **Profiling** | Slow request | Flamegraph or Query Plan identifying the bottleneck |
| **Optimization** | SLA violation | Reduced latency or resource usage with before/after proof |
| **LLM FinOps** | High token cost or latency | Model tiering, semantic caching, or payload reduction plan |
| **Audit** | Release prep | Core Web Vitals report (LCP/CLS/INP) |

## Out of Scope

- Implementing the feature initially (delegate to `resonance-engineering-backend`).

## Cognitive Frameworks

### The Critical Path
The sequence of tasks that determines total duration. Optimize the critical path. Parallelize everything else. Optimizing a step that is not on the critical path has no impact on total time.

### Structural vs. Syntax Performance Debt
Structural: N+1 queries, synchronous blocking work on interactive requests, serving static assets through heavyweight pipelines. Fix these first. They deliver order-of-magnitude improvements.

Syntax: Loop unrolling, memoization, V8-specific hacks. P3. Worth mentioning, not worth prioritizing over structural issues.

### Big O Notation
O(n^2) loops masquerading as O(n). An ORM that issues one query per item in a list. A sort on an un-indexed column. These are structural bugs that compound with data growth.

## KPIs

- **LCP**: < 2.5s (P75 real users).
- **INP**: < 200ms.
- **API P99**: < 300ms.

> ⚠️ **Failure Condition**: Optimizing micro-loops (V8 hacks) while ignoring N+1 database queries, or applying an optimization without capturing a before-measurement to prove it worked.

## Reference Library

- **[SLO Framework](references/slo_framework.md)**: User-centric performance targets.
- **[LLM FinOps Protocol](references/llm_finops_protocol.md)**: Token optimization, semantic caching, and model tiering.
- **[Bundle Analysis](references/bundle_analysis_protocol.md)**: Code size budget.
- **[Backend Performance](references/backend_performance_protocol.md)**: N+1, Memory, Caching.
- **[Core Web Vitals](references/core_web_vitals.md)**: LCP, INP, CLS targets and fixes.

## Operating Standard

Apply the Resonance operating standard from AGENTS.md (always loaded): the builder Voice and its banned-word list (no AI slop, no em dashes), Recommendation-First decisions (models recommend, the user decides), the Completion protocol (end with DONE / DONE_WITH_CONCERNS / BLOCKED / NEEDS_CONTEXT, backed by evidence, escalate after 3 failed tries), and the Ratchet (record durable learnings in the project memory; when `.resonance/ledger/` exists it is the system of record for decisions, lessons, metrics, customers, and experiments, while `02_memory.md` keeps `[lib]` notes and pointers).

> **Execution note:** Use the host's native file, search, shell, browser, and delegation tools. Follow the procedure and verify material claims with evidence. Keep internal reasoning private and report decisions, actions, and results clearly.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 37 GitHub stars
Stars/forks activity: 37 stars, 7 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Open full audit

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Start with one small task

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Source & usage notes

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Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: manusco/resonance
License: MIT
Version: Unknown
Last GitHub push: Sep 4, 2026
Registry updated: Sep 10, 2026
Instruction path: .agents/skills/engineering/performance/SKILL.md @ 669609225352

Version reported in registry metadata; check source releases before relying on it.

Quality

57/100

Promising

Trust

60/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20resonance-engineering-performance%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20resonance-engineering-performance%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/manusco-resonance-engineering-performance/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/manusco-resonance-engineering-performance"
  }
}

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Creator: manusco
Source: manusco/resonance
Indexed by: OpenAgentSkill community index

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