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resonance-engineering-performance
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, Back
Übersicht
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.
Dateimetadaten
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
Originaltext anzeigen
--- 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.
Quelle prüfen
Preis und Betriebskosten
- Skill beziehen
- Preis unbestätigt
- Ausführen
- Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
- Lizenz
- MIT
- Preis unbestätigt
- Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.
Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →
Skill-Quelle erfasst
Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.
Vor Installation prüfen: Automatische Installation vermeiden
Lizenz: 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
- KI-Prüffreigabe fehlt
- 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
Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.
Mit einer kleinen Aufgabe beginnen
- 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
- 2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
- 3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.
Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.
Quelle und Nutzungshinweise
ErfasstStatisch geprüft
Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.
- Quell-Repository
- manusco/resonance
- Lizenz
- MIT
- Version
- Unknown
- Letzter GitHub-Push
- 4. Sept. 2026
- Verzeichnis aktualisiert
- 10. Sept. 2026
Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.
Qualität
54/100
Prüfung nötig
Vertrauen
58/100
Do not auto-install
Audit
69/100
Prüfung nötig
- 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
- KI-Prüffreigabe fehlt
- 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
- Verified installs
- —
- Ergebnisse
- —
Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.
Agent-Zugang
Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.
Weitere Details
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"selected_skill": "manusco-resonance-engineering-performance (resonance-engineering-performance)",
"install_command": "npx skills add manusco/resonance --skill resonance-engineering-performance",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "manusco-resonance-engineering-performance",
"task": "Use resonance-engineering-performance in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/manusco-resonance-engineering-performance",
"api": "https://www.openagentskill.com/api/agent/skills/manusco-resonance-engineering-performance",
"audit": "https://www.openagentskill.com/skills/manusco-resonance-engineering-performance/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=manusco-resonance-engineering-performance&task=Use%20resonance-engineering-performance%20in%20an%20agent%20workflow&max_risk=medium",
"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"
}
}Für Ersteller
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- manusco
- Quelle
- manusco/resonance
- Indexiert von
- OpenAgentSkill Community-Index
Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.
Diesen Skill beanspruchenEigentümeranspruch
Diesen Skill-Eintrag beanspruchen
Dieser Registry-indexiert-Eintrag wird manusco zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.
Share-Kit
Creator-Backlink-Kit
Evidenz-Badges in deine README einfügen
Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.
[](https://www.openagentskill.com/skills/manusco-resonance-engineering-performance?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/manusco-resonance-engineering-performance?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/manusco-resonance-engineering-performance/audit)
[](https://www.openagentskill.com/skills/manusco-resonance-engineering-performance?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Community-Signal
Teile mit, ob dieser Skill für deinen Agent-Workflow nützlich ist. Zusammengefasstes Feedback verbessert das Ranking im Laufe der Zeit.