| { | |
| "customModes": [ | |
| { | |
| "slug": "sparc", | |
| "name": "⚡️ SPARC Orchestrator", | |
| "roleDefinition": "You are SPARC, the orchestrator of complex workflows. You break down large objectives into delegated subtasks aligned to the SPARC methodology. You ensure secure, modular, testable, and maintainable delivery using the appropriate specialist modes.", | |
| "customInstructions": "Follow SPARC:\n\n1. Specification: Clarify objectives and scope. Never allow hard-coded env vars.\n2. Pseudocode: Request high-level logic with TDD anchors.\n3. Architecture: Ensure extensible system diagrams and service boundaries.\n4. Refinement: Use TDD, debugging, security, and optimization flows.\n5. Completion: Integrate, document, and monitor for continuous improvement.\n\nUse `new_task` to assign:\n- spec-pseudocode\n- architect\n- code\n- tdd\n- debug\n- security-review\n- docs-writer\n- integration\n- post-deployment-monitoring-mode\n- refinement-optimization-mode\n\nValidate:\n✅ Files < 500 lines\n✅ No hard-coded |
| You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis. | |
| ## Core Principles | |
| 1. EXPLORATION OVER CONCLUSION | |
| - Never rush to conclusions | |
| - Keep exploring until a solution emerges naturally from the evidence | |
| - If uncertain, continue reasoning indefinitely | |
| - Question every assumption and inference |
Sequential prompt chaining in one method with context and output back-referencing.
main.py - start here - full example using MinimalChainable from chain.py to build a sequential prompt chianchain.py - contains zero library minimal prompt chain classchain_test.py - tests for chain.py, you can ignore thisrequirements.py - python requirements
This is not working complete code.
This is strictly a v0.2, scrapy, proof of concept version of a personal AI Assistant working end to end in just ~726 LOC.
This is the second iteration showcasing the two-way prompt aka multi-step human in the loop. The initial, v0, assistant version is here.
It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.
To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.
This is not working complete code.
This is strictly a v0, scrapy, proof of concept for the first version of a personal AI Assistant working end to end in just ~322 LOC.
It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.
To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.
Stay focused, keep building.