Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/terrylica/cc-skillsnpx agentmods add skills/terrylica/cc-skills/multi-agent-performance-profilingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/terrylica/cc-skills/multi-agent-performance-profiling)<a href="https://agentmods.dev/skills/terrylica/cc-skills/multi-agent-performance-profiling"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/multi-agent-performance-profiling.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00032 | $0.03162 |
| Opus 5 | $0.00016 | $0.01581 |
| Sonnet 5 | $0.00006 | $0.00632 |
| Haiku 4.5 | $0.00003 | $0.00316 |
Grade A, and why
multi-agent-performance-profiling 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 — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Performance Profiling
Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.
Overview
Prescriptive workflow for spawning parallel profiling agents to comprehensively identify performance bottlenecks across multiple system layers. Successfully discovered that QuestDB ingests at 1.1M rows/sec (11x faster than target), proving database was NOT the bottleneck - CloudFront download was 90% of pipeline time.
When to Use This Skill
Use this skill when:
- Performance below SLO (e.g., 47K vs 100K rows/sec target)
- Multi-stage pipeline optimization (download → extract → parse → ingest)
- Database performance investigation
- Bottleneck identification in complex workflows
- Pre-optimization analysis (before making changes)
Key outcomes:
- Identify true bottleneck (vs assumed bottleneck)
- Quantify each stage's contribution to total time
- Prioritize optimizations by impact (P0/P1/P2)
- Avoid premature optimization of non-bottlenecks
Core Methodology
1. Multi-Layer Profiling Model (5-Agent Pattern)
Agent 1: Profiling (Instrumentation)
- Empirical timing of each pipeline stage
- Phase-boundary instrumentation with time.perf_counter()
- Memory profiling (peak usage, allocations)
- Bottleneck identification (% of total time)
Agent 2: Database Configuration Analysis
- Server settings review (WAL, heap, commit intervals)
- Production vs development config comparison
- Expected impact quantification (<5%, 10%, 50%)
Agent 3: Client Library Analysis
- API usage patterns (dataframe vs row-by-row)
- Buffer size tuning opportunities
- Auto-flush behavior analysis
Agent 4: Batch Size Analysis
- Current batch size validation
- Optimal batch range determination
- Memory overhead vs throughput tradeoff
Agent 5: Integration & Synthesis
- Consensus-building across agents
- Prioritization (P0/P1/P2) with impact quantification
- Implementation roadmap creation
What ships with it
4 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.
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 · 422 lines · 32 tokens per session scan A d1704d3cdeec
multi-agent-performance-profiling is a skill published in the GitHub repository terrylica/cc-skills (62 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 3,162 once invoked, about $0.0002 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 2026-09-05.
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