--- name: create-hat-collection-90ef9fedd005 description: Generates new Ralph hat collection presets through guided conversation. Asks clarifying questions, validates against schema constraints, and outputs production-ready YAML files. --- # Create Hat Collection ## Overview This skill generates Ralph hat collection presets through a guided, conversational workflow. It asks clarifying questions about your workflow, validates the configuration against schema constraints, and produces a production-ready YAML preset file. **Output:** A complete `.yml` preset file in the `presets/` directory. ## When to Use - Creating a new multi-agent workflow from scratch - Transforming a workflow idea into a structured preset - Need guidance on hat design patterns and event routing **Not for:** Modifying existing presets (use `/creating-hat-collections` reference instead) ## Workflow ### Phase 1: Understand the Workflow Ask clarifying questions to understand: 1. **Purpose:** What problem does this workflow solve? 2. **Pattern:** Which architecture pattern fits best? - **Pipeline:** A→B→C linear flow (analyze→summarize) - **Critic-Actor:** One proposes, another critiques (code review) - **Supervisor-Worker:** Coordinator delegates to specialists - **Scientific:** Observe→Hypothesize→Test→Fix (debugging) 3. **Roles:** What distinct agent personas are needed? 4. **Handoffs:** When should each role hand off to the next? 5. **Completion:** What signals the workflow is done? ### Phase 2: Design Event Flow Map the workflow as an event chain: ``` task.start → [Role A] → event.a → [Role B] → event.b → [Role C] → LOOP_COMPLETE ↓ event.rejected → [Role A] ``` **Constraints to validate:** - Each trigger maps to exactly ONE hat (no ambiguous routing) - `task.start` and `task.resume` are RESERVED (never use as triggers) - Every hat must publish at least one event - Chain must eventually reach LOOP_COMPLETE ### Phase 3: Generate Preset Create the YAML file with these sections: ```yaml # # Pattern: # # # Usage: # ralph run --config presets/.yml --prompt "" event_loop: starting_event: "" # Ralph publishes this hats: hat_key: name: " Display Name" description: "" triggers: ["event.triggers.this"] publishes: ["event.this.publishes", "alternate.event"] default_publishes: "event.this.publishes" instructions: | ## MODE ### Process 1. 2. 3. Publish appropriate event ### Event Format ``` key: value ``` ### DON'T - - ``` ## Schema Reference ### Required Top-Level Fields | Field | Description | |-------|-------------| | `event_loop.starting_event` | First event Ralph publishes | ### Hat Definition Fields | Field | Required | Description | |-------|----------|-------------| | `name` | Yes | Display name with optional emoji (e.g., "🔍 Analyzer") | | `description` | Yes | Short description of the hat's purpose (one sentence) | | `triggers` | Yes | Events this hat responds to (list) | | `publishes` | Yes | Events this hat can emit (list) | | `default_publishes` | Recommended | Fallback event if hat forgets to publish | | `instructions` | Yes | Role-specific prompt (use `\|` for multiline) | ### Fields That DON'T Exist Never use these—they're not in the schema: - `emoji` (put emoji in `name` instead) - `system_prompt` (use `instructions`) - `subscriptions` / `publications` (use `triggers` / `publishes`) ## Event Naming Conventions ``` .ready / .done # Phase transitions .approved / .rejected # Review gates .found / .missing # Discovery events .request / .complete # Request-response ``` **Examples:** `analysis.complete`, `review.approved`, `build.blocked`, `spec.rejected` ## Validation Checklist Before finalizing the preset, verify: - [ ] Each trigger maps to exactly ONE hat - [ ] No hat uses `task.start` or `task.resume` as triggers - [ ] Every hat has `name`, `description`, `triggers`, `publishes`, and `instructions` - [ ] `default_publishes` is set for hats with multiple publish options - [ ] Event chain can reach LOOP_COMPLETE - [ ] No orphan events that never get handled (warning only) - [ ] Instructions include role definition, process steps, event format, and DON'Ts ## Output Location Save generated presets to: `presets/.yml` For minimal/embedded presets: `presets/minimal/.yml` ## Testing the Preset After generation, test with: ```bash # Dry run to validate config parsing cargo run --bin ralph -- run -c presets/.yml -p "test prompt" --dry-run # Run smoke tests cargo test -p ralph-core smoke_runner ``` Use `/evaluate-presets` for comprehensive validation. ## Example Conversation **User:** I want a preset for doing code reviews with a security focus. **Assistant:** I'll help create a security-focused code review preset. Let me ask a few questions: 1. **Review depth:** Should this be a quick scan or thorough analysis? 2. **Roles needed:** I'm thinking: - Security Analyst (finds vulnerabilities) - Code Reviewer (checks logic/style) - Summarizer (creates final report) Does this cover your needs, or should we add/remove roles? 3. **Iteration:** If issues are found, should the code be fixed, or just reported? **User:** Thorough analysis, those roles work, just report issues don't fix. **Assistant:** Here's the event flow I'm designing: ``` task.start → [Security Analyst] → security.complete ↓ → [Code Reviewer] → review.complete ↓ → [Summarizer] → LOOP_COMPLETE ``` Let me generate the preset... [Generates YAML with three hats following the schema] ## Common Patterns Reference ### Pipeline (Sequential) ``` A → B → C → done ``` Use for: analysis workflows, document processing ### Critic-Actor (Review Loop) ``` Actor → Critic → approved/rejected ↓ rejected → Actor (retry) ``` Use for: code review, quality gates ### Supervisor-Worker ``` Supervisor → worker.task → Worker → work.done → Supervisor ``` Use for: complex task decomposition ### Scientific Method ``` Observe → Hypothesize → Test → confirmed/rejected ↓ rejected → Observe ``` Use for: debugging, investigation --- Source attribution: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/paulpete/create-hat-collection/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 clawdbot Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/paulpete/create-hat-collection/SKILL.md Attribution basis: Source repository record Fields of work: Design, Product Management Roles: Growth Product Manager, Product Manager, UX Designer Specialties: API Tools: Ralph Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: c317974617a09c2c837c29d54706db54ddd765b27b7f76269698aa7d76f69f33