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Agent Skill

Agent Orchestration Improve Agent skill

agent-orchestration-improve-agentsickn33/agentic-awesome-skillsplugins/agentic-awesome-skills-claude/skills/agent-orchestration-improve-agent/SKILL.md

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

48kMIT

always on

25 tokname + description

once used

2,283 tokSKILL.md body

bundled files

0

repo stars

48kGitHub

Install the Agent Orchestration Improve Agent skill

Skills CLI (Claude Code, Codex, Cursor and others)

npx skills add https://github.com/sickn33/agentic-awesome-skills --skill agent-orchestration-improve-agent

Claude Code, by hand (all projects)

git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git /tmp/agentic-awesome-skills
mkdir -p ~/.claude/skills
cp -R /tmp/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/agent-orchestration-improve-agent ~/.claude/skills/agent-orchestration-improve-agent

For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.

What it costs your context

Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.

WhenWhat loadsCost
Every sessionName and description25 tok
When the agent uses itThe SKILL.md instructions2,283 tok
Only if openedNo bundled files—

Until it is used, agent-orchestration-improve-agent costs about 54× less than the median MCP server we measured (1,362 tokens on every turn). Installing all 150 skills from sickn33/agentic-awesome-skills would load 5,255 tokens of descriptions. Skills vs MCP study

Checks

Our checks found nothing to flag: it follows the Agent Skills specification's naming and size guidelines and doesn't pipe downloads into a shell.

Checked 2026-10-22 · method

What's inside

The headings of its SKILL.md. Read the full file on GitHub.

  • Agent Performance Optimization Workflow
  • Use this skill when
  • Do not use this skill when
  • Instructions
  • Execution boundaries
  • Safety
  • Phase 1: Performance Analysis and Baseline Metrics
  • 1.1 Gather Performance Data
  • 1.2 User Feedback Pattern Analysis
  • 1.3 Failure Mode Classification
  • 1.4 Baseline Performance Report
  • Phase 2: Prompt Engineering Improvements
  • 2.1 Chain-of-Thought Enhancement
  • 2.2 Few-Shot Example Optimization
  • 2.3 Role Definition Refinement
  • 2.4 Constitutional AI Integration
  • 2.5 Output Format Tuning
  • Phase 3: Testing and Validation
  • 3.1 Test Suite Development
  • 3.2 A/B Testing Framework
  • 3.3 Evaluation Metrics
  • 3.4 Human Evaluation Protocol
  • Phase 4: Version Control and Deployment
  • 4.1 Version Management

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