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loki-mode

Multi-agent autonomous startup system for Claude Code. Takes PRD to fully deployed, revenue-generating product with zero human intervention.

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Quellinformationen

Repository
tomevault-io/claude-code-plugins
Letzte Quellaktivität
6. April 2026 um 08:37
Erkannte Sprache von SKILL.md
Englisch
Sterne
3
Forks
2

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Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
loki-mode
description
Multi-agent autonomous startup system for Claude Code. Takes PRD to fully deployed, revenue-generating product with zero human intervention.
metadata
{"author":"davila7"}
# Loki Mode - Claude Code Skill Multi-agent autonomous startup system for Claude Code. Takes PRD to fully deployed, revenue-generating product with zero human intervention. ## Quick Start ```bash # Launch Claude Code with autonomous permissions claude --dangerously-skip-permissions # Then invoke: # "Loki Mode" or "Loki Mode with PRD at path/to/prd" ``` ## Project Structure ``` SKILL.md # Main skill definition (read this first) references/ # Detailed documentation (loaded progressively) openai-patterns.md # OpenAI Agents SDK: guardrails, tripwires, handoffs lab-research-patterns.md # DeepMind + Anthropic: Constitutional AI, debate production-patterns.md # HN 2025: What actually works in production advanced-patterns.md # 2025 research patterns (MAR, Iter-VF, GoalAct) tool-orchestration.md # ToolOrchestra-inspired efficiency & rewards memory-system.md # Episodic/semantic memory architecture quality-control.md # Code review, anti-sycophancy, guardrails agent-types.md # 37 specialized agent definitions sdlc-phases.md # Full SDLC workflow task-queue.md # Queue system, circuit breakers spec-driven-dev.md # OpenAPI-first development architecture.md # Directory structure, state schemas core-workflow.md # RARV cycle, autonomy rules claude-best-practices.md # Boris Cherny patterns deployment.md # Cloud deployment instructions business-ops.md # Business operation workflows mcp-integration.md # MCP server capabilities autonomy/ # Runtime state and constitution benchmarks/ # SWE-bench and HumanEval benchmarks ``` ## Key Concepts ### RARV Cycle Every iteration follows: **R**eason -> **A**ct -> **R**eflect -> **V**erify ### Model Selection - **Opus**: Planning and architecture ONLY (system design, high-level decisions) - **Sonnet**: Development and functional testing (implementation, integration tests) - **Haiku**: Unit tests, monitoring, and simple tasks - use extensively for parallelization ### Quality Gates 1. Static analysis (CodeQL, ESLint) 2. 3-reviewer parallel system (blind review) 3. Anti-sycophancy checks (devil's advocate on unanimous approval) 4. Severity-based blocking (Critical/High/Medium = BLOCK) 5. Test coverage gates (>80% unit, 100% pass) ### Memory System - **Episodic**: Specific interaction traces (`.loki/memory/episodic/`) - **Semantic**: Generalized patterns (`.loki/memory/semantic/`) - **Procedural**: Learned skills (`.loki/memory/skills/`) ### Metrics System (ToolOrchestra-inspired) - **Efficiency**: Task cost tracking (`.loki/metrics/efficiency/`) - **Rewards**: Outcome/efficiency/preference signals (`.loki/metrics/rewards/`) ## Development Guidelines ### When Modifying SKILL.md - Keep under 500 lines (currently ~370) - Reference detailed docs in `references/` instead of inlining - Update version in header AND footer - Update CHANGELOG.md with new version entry ### Version Numbering Follows semantic versioning: MAJOR.MINOR.PATCH - Current: v2.35.0 - MINOR bump for new features - PATCH bump for fixes ### Code Style - No emojis in code or documentation - Clear, concise comments only when necessary - Follow existing patterns in codebase ## Testing ```bash # Run benchmarks ./benchmarks/run-benchmarks.sh humaneval --execute --loki ./benchmarks/run-benchmarks.sh swebench --execute --loki ``` ## Research Foundation Built on 2025 research from three major AI labs: **OpenAI:** - Agents SDK (guardrails, tripwires, handoffs, tracing) - AGENTS.md / Agentic AI Foundation (AAIF) standards **Google DeepMind:** - SIMA 2 (self-improvement, hierarchical reasoning) - Gemini Robotics (VLA models, planning) - Dreamer 4 (world model training) - Scalable Oversight via Debate **Anthropic:** - Constitutional AI (principles-based self-critique) - Alignment Faking Detection (sleeper agent probes) - Claude Code Best Practices (Explore-Plan-Code) **Academic:** - CONSENSAGENT (anti-sycophancy) - GoalAct (hierarchical planning) - A-Mem/MIRIX (memory systems) - Multi-Agent Reflexion (MAR) - NVIDIA ToolOrchestra (efficiency metrics) See `references/openai-patterns.md`, `references/lab-research-patterns.md`, and `references/advanced-patterns.md`. --- > Converted and distributed by [TomeVault](https://tomevault.io) | [Claim this content](https://tomevault.io/claim/davila7/claude-code-templates) <!-- tomevault:2.0:skill_md:2026-04-05 -->
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