common-skills/skills/meta-creator/SKILL.md

name, description, metadata

name	description	metadata
meta-creator	Creates and iteratively improves agent skills and custom agents. Use when a user wants to create a new skill, update an existing skill, create a new agent, or run eval-driven iteration. Triggers on phrases like "create a skill", "make a skill", "new skill", "update skill", "improve skill", "create an agent", "new agent", "update agent", "创建skill", "创建技能", "新建skill", "更新skill", "优化skill", "创建agent", "新建agent", "更新agent".	author version common-skills 1.0

Meta Creator

Create or update agent skills and custom agents. Skills conform to the Agent Skills specification. Agents conform to the Kiro custom agent configuration. For eval-driven iteration, follow the eval methodology. For Kiro CLI configuration scopes, file paths, and conflict resolution rules, refer to the Kiro CLI Chat configuration.

References

• skills-Specification.md — SKILL.md format, frontmatter rules, directory structure
• skills-eval.md — eval design, grading, iteration methodology
• custom-agents-configuration-reference.md — Kiro agent JSON config fields
• kiro-cli-chat-configuration.md — Kiro CLI configuration scopes (global/project/agent), file paths, and conflict resolution priority

Inputs

The user will provide one of:

• A description of what the new skill should do
• An existing skill directory to update or improve
• Eval results / feedback to incorporate into an existing skill

Workflow

1. Gather Requirements

Ask the user (or infer from context):

• What does the skill do? When should it activate?
• What are 2–3 concrete example tasks it should handle?
• Any environment requirements (tools, packages, network)?

2. Create or Update SKILL.md

Frontmatter rules:

• name: lowercase, hyphens only, matches directory name, max 64 chars
• description: describes what it does AND when to use it; include trigger phrases; max 1024 chars
• Add compatibility only if the skill has real environment requirements
• Add metadata (author, version) for team skills

Body content:

• Write clear step-by-step instructions the agent will follow
• Include concrete examples of inputs and expected outputs
• Cover the 2–3 most important edge cases
• Keep under 500 lines; move detailed reference material to references/

3. Create evals/evals.json

Write at least 3 eval cases covering:

• A typical happy-path use case
• A variation with different phrasing or context
• An edge case (unusual input, boundary condition, or ambiguous request)

Each eval case must have:

• id: integer
• prompt: realistic user message (not "process this data" — use specific context)
• expected_output: human-readable description of what success looks like

Add assertions after the first eval run reveals what "good" looks like.

Format:

{
  "skill_name": "<name>",
  "evals": [
    {
      "id": 1,
      "prompt": "...",
      "expected_output": "..."
    }
  ]
}

4. Create or Update README.md and Diagrams

After creating or updating a skill, create (or update) skills/<name>/README.md and generate two PlantUML diagrams:

Architecture diagram (assets/architecture.puml) — static component view:

• Show the skill's files and their roles (SKILL.md, references/, assets/, evals/)
• Show external dependencies (tools, APIs, databases, other files the skill reads/writes)
• Use package blocks to group related components; use component, database, actor

Workflow diagram (assets/workflow.puml) — dynamic sequence view:

• Show the interaction between the user, the skill, and any external systems step by step
• Use participant / actor and sequence arrows (->, -->)
• Include branching (alt/opt) for key decision points

Convert to SVG:

bash scripts/puml2svg.sh <name>

This requires Java and Graphviz. The PlantUML jar is resolved automatically from the VS Code extension; override with PLANTUML_JAR=/path/to/plantuml.jar.

README structure:

# <skill-name>

One-line description.

## Architecture
![Architecture](assets/<name>-architecture.svg)

## Workflow
![Workflow](assets/<name>-workflow.svg)

## When to Use
...

## How It Works
...

## File Structure
...

## Evals
\`\`\`bash
python scripts/run_evals.py <name>
\`\`\`

5. Iterative Improvement (if eval results are provided)

When the user provides eval results, grading output, or human feedback:

1. Identify which assertions failed and why (read execution transcripts if available)
2. Distinguish between:
   • Instruction gaps: the skill didn't tell the agent to do something it should
   • Ambiguous instructions: the agent interpreted instructions inconsistently
   • Wrong assertions: the assertion was too strict, too vague, or checking the wrong thing
3. Propose targeted changes to SKILL.md:
   • Generalize fixes — don't patch for a single test case
   • Remove instructions that caused wasted work
   • Add reasoning ("Do X because Y") rather than rigid directives
4. Update evals/evals.json to fix broken assertions and add new cases for uncovered scenarios

6. Create or Update a Custom Agent (if requested)

When the user wants a new or updated Kiro agent (.kiro/agents/<name>.json):

Required fields:

• name: descriptive, matches the filename (without .json)
• description: what the agent does and when to use it
• prompt: concise system prompt; delegate detail to skill resources where possible
• tools: only include tools the agent actually needs
• allowedTools: read-only tools are safe to auto-allow; tools that write files or run commands should require confirmation (omit from allowedTools)

Help/greeting response: The agent's prompt file MUST include instructions to respond to greetings and help requests (e.g., "hi", "hello", "help", "你好", "帮助", "?") with a structured introduction covering:

• What the agent does (one-line summary)
• Key capabilities (bullet list)
• How the agent works step-by-step (execution flow)
• 2–3 concrete example prompts

Example prompt section to include:

When the user sends a greeting or help request (e.g., "hi", "hello", "help", "你好", "帮助", "?"), respond with:

---
👋 **<Agent Name>** — <one-line description>

**功能：**
- <capability 1>
- <capability 2>

**执行步骤：**
1. <step 1>
2. <step 2>
3. <step 3>

**使用示例：**
- `<example prompt 1>`
- `<example prompt 2>`
---

Resources:

• Use skill:// for skills (lazy-loads, saves context)
• Use file:// only for small reference docs needed at startup

Output location: .kiro/agents/<name>.json

Prompt file: Extract the prompt to file://prompts/<name>.md (relative to .kiro/agents/) and reference it as "prompt": "file://prompts/<name>.md" to keep the JSON clean.

Skill install path: Skills are installed under .kiro/skills/<name>/. Reference them as skill://.kiro/skills/**/SKILL.md (or a specific path). The skill:// protocol loads only name/description metadata at startup and fetches full content on demand.

7. Post-Creation: Agent Setup (after creating a new skill)

After successfully creating a new skill, ask the user:

"Do you want a dedicated agent to invoke this skill? If not, it will be available to the g-assistent agent by default."

• If yes: proceed with Step 5 to create a .kiro/agents/<name>.json for the skill.
• If no: inform the user that g-assistent will route to this skill automatically based on its description trigger phrases.

8. Post-Agent Checkpoint: Update install-agents.sh

After creating or updating any agent, check whether scripts/install-agents.sh needs updating:

1. Read scripts/install-agents.sh (if it exists in the repo root).
2. Check if the script handles:
   • Any file://prompts/<name>.md references — the script must copy prompt files to the target prompts/ directory
   • Any new skill references that require special handling
3. If a gap is found, update scripts/install-agents.sh and tell the user what changed.
4. If no changes are needed, briefly confirm: "install-agents.sh is up to date."

Output

• skills/<name>/SKILL.md — the skill file
• skills/<name>/evals/evals.json — eval cases
• skills/<name>/README.md — documentation with architecture and workflow diagrams
• skills/<name>/assets/architecture.puml + architecture.svg — static component diagram
• skills/<name>/assets/workflow.puml + workflow.svg — dynamic sequence diagram
• .kiro/agents/<name>.json — the agent config (only if user requests a dedicated agent)
• .kiro/agents/prompts/<name>.md — the agent prompt file (extracted from JSON)

If creating a new skill, also suggest the directory structure needed (scripts/, references/, assets/) based on the skill's requirements.

Quality Checklist

Before finishing, verify:

• name matches the directory name exactly
• description includes both what it does and when to activate (trigger phrases)
• Body instructions are actionable, not vague
• At least 3 eval cases with varied prompts
• No eval prompt is too generic (e.g., "test this skill")
• SKILL.md is under 500 lines
• README.md exists with Architecture and Workflow sections
• assets/architecture.puml and assets/workflow.puml exist and SVGs are generated
• Agent prompt includes a greeting/help response with capabilities and example prompts (for new agents)