Optimization Suggest

CommunityPopular

hoangsonww

optimization-suggest

Suggest concrete optimizations for Claude Code usage based on historical session data. Covers cost reduction, speed improvement, error prevention, and workflow efficiency. Use for data-driven optimization planning.

Overview

Publisher	hoangsonww
Repository	`Claude-Code-Agent-Monitor`
Skill name	`optimization-suggest`
Stars	1.1K
Forks	260
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by hoangsonww on GitHub. Read the source before you install it.

Installation

Install the Optimization Suggest AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-insights/skills/optimization-suggest
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-insights/skills/optimization-suggest

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/hoangsonww/Claude-Code-Agent-Monitor.git /tmp/Claude-Code-Agent-Monitor
mkdir -p .claude/skills
cp -r /tmp/Claude-Code-Agent-Monitor/plugins/ccam-insights/skills/optimization-suggest .claude/skills/optimization-suggest

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Optimization Suggest in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Optimization Suggest on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Optimization Suggest is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Optimization Suggest

Generate data-driven optimization recommendations for Claude Code usage.

Input

The user provides: $ARGUMENTS

This may be:

"all" or empty (default: comprehensive optimization scan)
"cost" for cost reduction focus
"speed" for performance/speed focus
"quality" for error reduction focus
"efficiency" for workflow efficiency focus

Procedure

Gather optimization data from http://localhost:4820:
- GET /api/sessions?limit=200 — session history
- GET /api/analytics — tool and token analytics
- GET /api/pricing/cost — cost data
- GET /api/pricing — pricing rules for model comparison
- Sample event streams for behavioral analysis
Analyze optimization opportunities:

💰 Cost Optimization
- Model downgrade opportunities: Tasks completed with expensive models that could use cheaper ones
  - Compare success rates per model per task type
  - Calculate savings from model substitution
- Cache optimization: Sessions with low cache hit rates
  - Identify sessions that could benefit from better prompt caching
- Early termination: Sessions that ran longer than needed
  - Detect sessions where useful work completed well before session end
- Compaction reduction: Sessions hitting context limits
  - Suggest breaking large tasks into smaller sessions
⚡ Speed Optimization
- Tool selection: Faster alternatives for commonly-used tool patterns
- Subagent parallelization: Tasks that could run in parallel
- Session planning: Better upfront context to reduce back-and-forth
- Preemptive context loading: Frequently needed files/context
🛡 Quality Optimization
- Error prevention: Common error patterns with preventive measures
- Tool reliability: Tools with high failure rates and alternatives
- Validation gaps: Sessions lacking verification steps
- Recovery strategies: Better error handling patterns
🔄 Workflow Optimization
- Session sizing: Optimal session scope based on historical success
- Task decomposition: Complex sessions that should be split
- Automation candidates: Repetitive workflows to automate
- Knowledge reuse: Patterns where previous session context could help
Quantify each recommendation:
- Estimated impact (cost savings $, time savings %, error reduction %)
- Implementation effort (low/medium/high)
- Confidence level based on data available
- Priority score = Impact × Confidence / Effort

Output Format

Present as a prioritized optimization plan:

#	Recommendation	Category	Impact	Effort	Priority
1	Specific action	💰/⚡/🛡/🔄	High	Low	★★★★★
2	Specific action	...	...	...	★★★★☆

For the top 5 recommendations, include:

Detailed explanation with supporting data
Step-by-step implementation guide
Expected before/after metrics
How to measure success

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

agents/openai.yaml1 KB

Frequently asked questions

What does the Optimization Suggest AI skill do?

Why use Optimization Suggest on TypingMind?

Because you install it once and use it with any model. Optimization Suggest is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Optimization Suggest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-insights/skills/optimization-suggest. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Optimization Suggest?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Optimization Suggest?

As many as you like. As long as a model supports skills, you can use Optimization Suggest with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Optimization Suggest AI skill free?

Yes. It is published on GitHub by hoangsonww under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Optimization Suggest

Input

Procedure

💰 Cost Optimization

⚡ Speed Optimization

🛡 Quality Optimization

🔄 Workflow Optimization

Output Format

Bundled files

Frequently asked questions

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