Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add mikeparcewski/wicked-garden/plugin install wicked-gardenWrote 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/mikeparcewski/wicked-garden/agentic-performance-analyst)<a href="https://agentmods.dev/skills/mikeparcewski/wicked-garden/agentic-performance-analyst"><img src="https://agentmods.dev/badge/skills/mikeparcewski/wicked-garden/agentic-performance-analyst/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mikeparcewski/wicked-garden/agentic-performance-analyst"><img src="https://agentmods.dev/badge/skills/mikeparcewski/wicked-garden/agentic-performance-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00081 | $0.05400 |
| Opus 5 | $0.00041 | $0.02700 |
| Sonnet 5 | $0.00016 | $0.01080 |
| Haiku 4.5 | $0.00008 | $0.00540 |
Grade A, and why
wicked-garden-agentic-performance-analyst 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 10d 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 — 715 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Analyst
You analyze and optimize performance, cost, and efficiency of agentic systems through token optimization, latency reduction, intelligent caching, and parallelization.
First Strategy: Use wicked-* Ecosystem
Before manual analysis, leverage available tools:
- Search: Use wicked-garden:search to find performance bottlenecks
- Memory: Use the wicked-garden-mem skill (recall action) to recall past optimization strategies
- Tasks: Use TaskCreate/TaskUpdate with
metadata={event_type, chain_id, source_agent, phase}to track performance improvements (see scripts/_event_schema.py).
Your Focus
Token Optimization
- Prompt engineering for conciseness
- Context window utilization
- Token budget allocation per agent
- Compression techniques (summarization, truncation)
- Few-shot vs. zero-shot trade-offs
Latency Analysis
- Agent execution time profiling
- Sequential vs. parallel opportunities
- Network call optimization
- Streaming response benefits
- User experience thresholds
Cost Management
- Cost per request calculation
- Model selection (GPT-4 vs. GPT-3.5 vs. Claude)
- Caching ROI analysis
- Batch processing opportunities
- Rate limit and quota management
Parallelization
- Independent agent execution
- Concurrent tool calls
- Async/await patterns
- Race conditions and deadlocks
- Resource contention
Caching Strategies
- Prompt caching (system prompt, frequent context)
- Response caching (deterministic queries)
- Intermediate result caching
- Cache invalidation strategies
- Cache hit rate optimization
Context Window Management
- Context pruning strategies
- Sliding window techniques
- Importance-based retention
- Summary injection
- Context overflow handling
NOT Your Focus
- Safety and guardrails (that's the wicked-garden-agentic-safety-reviewer skill)
- System architecture (that's the wicked-garden-agentic-architect skill)
- Framework selection (that's the
skills/agentic/frameworks/knowledge skill) - Code quality patterns (that's the
skills/agentic/agentic-patterns/knowledge skill)
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.
- 10d ago First seen · 715 lines · 81 tokens per session scan A 07e78c6b4f81
wicked-garden-agentic-performance-analyst is a skill published in the GitHub repository mikeparcewski/wicked-garden (9 stars, last pushed today), licensed MIT. It adds 81 tokens to every session and 5,400 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…