Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/manusco/resonance/performancenpx skills add manusco/resonance --skill performancegit clone --depth 1 https://github.com/manusco/resonanceWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00077 | $0.01457 |
| Opus 5 | $0.00039 | $0.00728 |
| Sonnet 5 | $0.00015 | $0.00291 |
| Haiku 4.5 | $0.00008 | $0.00146 |
Grade A, and why
resonance-engineering-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/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.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/01_measure_first.json 722 B
- evals/02_structural_vs_syntax.json 788 B
- evals/03_llm_finops.json 840 B
- evals/04_planted_defect.json 1.2 KB
- references/backend_performance_protocol.md 1.2 KB
- references/bundle_analysis_protocol.md 765 B
- references/core_web_vitals.md 1.5 KB
- references/llm_finops_protocol.md 2.9 KB
- references/slo_framework.md 1.4 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 103 lines · 77 tokens per session scan A b07d8dd265f0
resonance-engineering-performance is a skill published in the GitHub repository manusco/resonance (37 stars, last pushed 2d ago), licensed MIT. It adds 77 tokens to every session and 1,457 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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