Proposal: Post-Task Reflection, Persistent "Gotchas", and a Community Recipe Hub for OpenCode/Omarchy

[Evolver70]

1. The Core Problem

Right now, agentic CLI tools (like OpenCode in Omarchy) operate in a stateless bubble. When setting up complex workflows—such as configuring local services, audio pipelines, or running multi-container Docker setups like ComfyUI and OpenWebUI—the agent often stumbles through trial-and-error across several turns.

It might fail on network endpoints, misread local paths, or struggle with service handshakes before finally resolving the issue on the third or fourth try. Once the session ends, that hard-won knowledge vanishes. If the user asks for a similar task next week, the agent repeats the exact same missteps from scratch.

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2. The Local Fix: A 3-File Post-Task Reflection

Instead of relying on heavy RAG architectures or complex vector databases, Omarchy can leverage plain-text Markdown conventions. At the end of a successful troubleshooting or setup task, the agent runs a short automated post-mortem step:

1. Session Audit (~/OpenCode-notes/sessions.md):
   Logs the initial prompt, the final working response, and a clean file/command diff. Intermediate chain-of-thought tokens are stripped out to keep it concise.
2. Persistent Machine Memory (~/.config/opencode/gotchas.md):
   A lightweight, persistent markdown file injected into the agent’s context at the start of every session to prevent recurring mistakes on the same machine.

• Human-in-the-Loop Safeguard: To prevent context poisoning or recording hallucinations as fact, writing to this file must never happen silently. At the end of a session involving trial-and-error, the agent proposes a clear, concise lesson and asks for user confirmation (e.g., "Learned rule: Service X requires internal Docker bridge hostname. Add to gotchas.md? [y/n]").
• Deterministic Learning: Only verified fixes get written down, making the system progressively smarter and faster without accumulating invisible noise or model-generated cruft.
  In short: gotchas.md becomes a persistent way for OpenCode to learn from its mistakes, making it noticeably better, faster, and more reliable with each new request.

3. Clean Recipe Generation (~/OpenCode-notes/playbooks.md):
   The agent synthesizes the entire interaction into a single, clean "golden prompt" and associated config files, stripped of all intermediate errors and dead ends.

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3. The Community Layer: An OpenCode Recipe Hub

Taking this a step further, the wider open-source community could pool these distilled recipes into a shared public repository or wiki.

• Deterministic Execution over Reasoning: Solving an unknown edge case requires heavy reasoning models. Once the edge cases are documented, running the clean recipe requires only structured execution.
• Empowering Smaller, Local Models: Compact, open-weight models running on local consumer hardware often fail when debugging complex Linux configuration errors. However, given an explicit playbook that already accounts for specific gotchas, lightweight models can execute the task flawlessly.
• Cost, Privacy, and Energy Efficiency: Eliminating repeated trial-and-error drastically reduces token consumption. Users save on API fees, keep configurations private and offline, and avoid burning cloud compute to solve problems the community has already solved.

However, I'm not so sure about this one, because:

• The Security Imperative: Unvetted community prompts pose an obvious attack vector (malicious commands or prompt injection executed with system privileges). Any shared hub would require strict peer review, automated static analysis (sandboxed dry-runs, linter checks, or secondary AI safety screening), and signed commits before inclusion.

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4. Export to Standalone Python / Shell Automation:

Beyond generating a clean prompt, the agent could synthesize its final changes into a standalone, reproducible Python script (e.g., setup_.py).

• Zero-AI Execution: The next time this environment needs to be deployed (on a new machine or after a fresh install), it requires no LLM tokens or API calls at all—just running the script natively.

• Deterministic & Auditable: Plain code turns unpredictable agent interactions into inspectable, version-controlled Infrastructure-as-Code. Users can read exactly which files were modified, which directories were created, and which system/docker commands are run.

• Scope & Boundaries: Naturally, this approach suits static, deterministic tasks (container orchestration, package provisioning, config generation). Tasks dependent on variable hardware drivers, dynamic network environments, or interactive credentials would fall back to the distilled playbook (prompt) rather than a rigid script.

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Why bake this into Omarchy?

Experienced tinkerers could theoretically script parts of this behavior themselves via custom agent hooks or aliases. But that defeats the purpose: having every individual user reinvent the wheel in isolation.

The beauty and promise of Omarchy’s Omakase philosophy is delivering thoughtful, curated defaults out of the box. Having the system naturally learn from its own setup friction, preserve local gotchas, and export clean scripts feels like a natural evolution of that philosophy—making the entire environment smarter, faster, and far more resource-efficient for everyone from day one.