chore: restructure skills repo with new agents and skill bundles
- Add new skills: deep-dive, docs-rag, meta-creator, ppt-maker, sdlc - Add agent configs: g-assistent, meta-creator, sdlc with prompt files - Add reference docs for custom agents and skills specification - Add utility scripts: install-agents.sh, orchestrate.py, puml2svg.sh - Update README and commit-message skill config - Remove deprecated skills: codereview, python, testing, typescript - Add .gitignore
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Team
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---
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name: meta-creator
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description: 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".
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metadata:
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author: common-skills
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version: "1.0"
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---
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# Meta Creator
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Create or update agent skills and custom agents. Skills conform to the [Agent Skills specification](references/skills-Specification.md). Agents conform to the [Kiro custom agent configuration](references/custom-agents-configuration-reference.md). For eval-driven iteration, follow the [eval methodology](references/skills-eval.md). For Kiro CLI configuration scopes, file paths, and conflict resolution rules, refer to the [Kiro CLI Chat configuration](references/kiro-cli-chat-configuration.md).
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## References
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- [skills-Specification.md](references/skills-Specification.md) — SKILL.md format, frontmatter rules, directory structure
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- [skills-eval.md](references/skills-eval.md) — eval design, grading, iteration methodology
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- [custom-agents-configuration-reference.md](references/custom-agents-configuration-reference.md) — Kiro agent JSON config fields
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- [kiro-cli-chat-configuration.md](references/kiro-cli-chat-configuration.md) — Kiro CLI configuration scopes (global/project/agent), file paths, and conflict resolution priority
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## Inputs
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The user will provide one of:
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- A description of what the new skill should do
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- An existing skill directory to update or improve
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- Eval results / feedback to incorporate into an existing skill
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## Workflow
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### 1. Gather Requirements
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Ask the user (or infer from context):
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- What does the skill do? When should it activate?
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- What are 2–3 concrete example tasks it should handle?
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- Any environment requirements (tools, packages, network)?
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### 2. Create or Update `SKILL.md`
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**Frontmatter rules:**
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- `name`: lowercase, hyphens only, matches directory name, max 64 chars
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- `description`: describes what it does AND when to use it; include trigger phrases; max 1024 chars
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- Add `compatibility` only if the skill has real environment requirements
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- Add `metadata` (author, version) for team skills
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**Body content:**
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- Write clear step-by-step instructions the agent will follow
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- Include concrete examples of inputs and expected outputs
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- Cover the 2–3 most important edge cases
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- Keep under 500 lines; move detailed reference material to `references/`
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### 3. Create `evals/evals.json`
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Write at least 3 eval cases covering:
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- A typical happy-path use case
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- A variation with different phrasing or context
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- An edge case (unusual input, boundary condition, or ambiguous request)
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Each eval case must have:
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- `id`: integer
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- `prompt`: realistic user message (not "process this data" — use specific context)
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- `expected_output`: human-readable description of what success looks like
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Add `assertions` after the first eval run reveals what "good" looks like.
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Format:
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```json
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{
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"skill_name": "<name>",
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"evals": [
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{
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"id": 1,
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"prompt": "...",
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"expected_output": "..."
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}
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]
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}
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```
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### 4. Create or Update `README.md` and Diagrams
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After creating or updating a skill, create (or update) `skills/<name>/README.md` and generate two PlantUML diagrams:
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**Architecture diagram** (`assets/architecture.puml`) — static component view:
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- Show the skill's files and their roles (SKILL.md, references/, assets/, evals/)
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- Show external dependencies (tools, APIs, databases, other files the skill reads/writes)
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- Use `package` blocks to group related components; use `component`, `database`, `actor`
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**Workflow diagram** (`assets/workflow.puml`) — dynamic sequence view:
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- Show the interaction between the user, the skill, and any external systems step by step
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- Use `participant` / `actor` and sequence arrows (`->`, `-->`)
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- Include branching (`alt`/`opt`) for key decision points
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**Convert to SVG:**
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```bash
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bash scripts/puml2svg.sh <name>
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```
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This requires Java and Graphviz. The PlantUML jar is resolved automatically from the VS Code extension; override with `PLANTUML_JAR=/path/to/plantuml.jar`.
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**README structure:**
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```markdown
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# <skill-name>
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One-line description.
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## Architecture
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## Workflow
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## When to Use
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...
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## How It Works
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...
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## File Structure
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...
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## Evals
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\`\`\`bash
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python scripts/run_evals.py <name>
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\`\`\`
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```
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### 5. Iterative Improvement (if eval results are provided)
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When the user provides eval results, grading output, or human feedback:
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1. Identify which assertions failed and why (read execution transcripts if available)
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2. Distinguish between:
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- **Instruction gaps**: the skill didn't tell the agent to do something it should
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- **Ambiguous instructions**: the agent interpreted instructions inconsistently
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- **Wrong assertions**: the assertion was too strict, too vague, or checking the wrong thing
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3. Propose targeted changes to `SKILL.md`:
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- Generalize fixes — don't patch for a single test case
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- Remove instructions that caused wasted work
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- Add reasoning ("Do X because Y") rather than rigid directives
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4. Update `evals/evals.json` to fix broken assertions and add new cases for uncovered scenarios
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### 6. Create or Update a Custom Agent (if requested)
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When the user wants a new or updated Kiro agent (`.kiro/agents/<name>.json`):
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**Required fields:**
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- `name`: descriptive, matches the filename (without `.json`)
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- `description`: what the agent does and when to use it
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- `prompt`: concise system prompt; delegate detail to skill resources where possible
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- `tools`: only include tools the agent actually needs
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- `allowedTools`: read-only tools are safe to auto-allow; tools that write files or run commands should require confirmation (omit from `allowedTools`)
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**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:
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- What the agent does (one-line summary)
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- Key capabilities (bullet list)
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- How the agent works step-by-step (execution flow)
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- 2–3 concrete example prompts
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Example prompt section to include:
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```
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When the user sends a greeting or help request (e.g., "hi", "hello", "help", "你好", "帮助", "?"), respond with:
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---
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👋 **<Agent Name>** — <one-line description>
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**功能：**
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- <capability 1>
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- <capability 2>
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**执行步骤：**
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1. <step 1>
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2. <step 2>
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3. <step 3>
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**使用示例：**
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- `<example prompt 1>`
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- `<example prompt 2>`
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---
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```
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**Resources:**
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- Use `skill://` for skills (lazy-loads, saves context)
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- Use `file://` only for small reference docs needed at startup
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**Output location:** `.kiro/agents/<name>.json`
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**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.
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**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.
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### 7. Post-Creation: Agent Setup (after creating a new skill)
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After successfully creating a new skill, ask the user:
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> "Do you want a dedicated agent to invoke this skill? If not, it will be available to the `g-assistent` agent by default."
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- If **yes**: proceed with Step 5 to create a `.kiro/agents/<name>.json` for the skill.
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- If **no**: inform the user that `g-assistent` will route to this skill automatically based on its `description` trigger phrases.
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### 8. Post-Agent Checkpoint: Update install-agents.sh
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After creating or updating any agent, check whether `scripts/install-agents.sh` needs updating:
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1. Read `scripts/install-agents.sh` (if it exists in the repo root).
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2. Check if the script handles:
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- Any `file://prompts/<name>.md` references — the script must copy prompt files to the target `prompts/` directory
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- Any new skill references that require special handling
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3. If a gap is found, update `scripts/install-agents.sh` and tell the user what changed.
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4. If no changes are needed, briefly confirm: "install-agents.sh is up to date."
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## Output
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- `skills/<name>/SKILL.md` — the skill file
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- `skills/<name>/evals/evals.json` — eval cases
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- `skills/<name>/README.md` — documentation with architecture and workflow diagrams
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- `skills/<name>/assets/architecture.puml` + `architecture.svg` — static component diagram
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- `skills/<name>/assets/workflow.puml` + `workflow.svg` — dynamic sequence diagram
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- `.kiro/agents/<name>.json` — the agent config (only if user requests a dedicated agent)
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- `.kiro/agents/prompts/<name>.md` — the agent prompt file (extracted from JSON)
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If creating a new skill, also suggest the directory structure needed (scripts/, references/, assets/) based on the skill's requirements.
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## Quality Checklist
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Before finishing, verify:
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- [ ] `name` matches the directory name exactly
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- [ ] `description` includes both what it does and when to activate (trigger phrases)
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- [ ] Body instructions are actionable, not vague
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- [ ] At least 3 eval cases with varied prompts
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- [ ] No eval prompt is too generic (e.g., "test this skill")
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- [ ] SKILL.md is under 500 lines
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- [ ] `README.md` exists with Architecture and Workflow sections
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- [ ] `assets/architecture.puml` and `assets/workflow.puml` exist and SVGs are generated
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- [ ] Agent prompt includes a greeting/help response with capabilities and example prompts (for new agents)
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