davila7/claude-code-templates/loki-mode

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Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.

Install

npx vskill@latest install davila7/claude-code-templates/loki-mode

SKILL.md

Loki Mode - Multi-Agent Autonomous Startup System

Version 2.35.0 | PRD to Production | Zero Human Intervention
Research-enhanced: OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, Agent SDK, HN Production (2025)

Quick Reference

Critical First Steps (Every Turn)
  1. READ .loki/CONTINUITY.md - Your working memory + "Mistakes & Learnings"
  2. RETRIEVE Relevant memories from .loki/memory/ (episodic patterns, anti-patterns)
  3. CHECK .loki/state/orchestrator.json - Current phase/metrics
  4. REVIEW .loki/queue/pending.json - Next tasks
  5. FOLLOW RARV cycle: REASON, ACT, REFLECT, VERIFY (test your work!)
  6. OPTIMIZE Opus=planning, Sonnet=development, Haiku=unit tests/monitoring - 10+ Haiku agents in parallel
  7. TRACK Efficiency metrics: tokens, time, agent count per task
  8. CONSOLIDATE After task: Update episodic memory, extract patterns to semantic memory
Key Files (Priority Order)
FilePurposeUpdate When
.loki/CONTINUITY.mdWorking memory - what am I doing NOW?Every turn
.loki/memory/semantic/Generalized patterns & anti-patternsAfter task completion
.loki/memory/episodic/Specific interaction tracesAfter each action
.loki/metrics/efficiency/Task efficiency scores & rewardsAfter each task
.loki/specs/openapi.yamlAPI spec - source of truthArchitecture changes
CLAUDE.mdProject context - arch & patternsSignificant changes
.loki/queue/*.jsonTask statesEvery task change
Decision Tree: What To Do Next?
START
|
+-- Read CONTINUITY.md ----------+
| |
+-- Task in-progress? |
| +-- YES: Resume |
| +-- NO: Check pending queue |
| |
+-- Pending tasks? |
| +-- YES: Claim highest priority
| +-- NO: Check phase completion
| |
+-- Phase done? |
| +-- YES: Advance to next phase
| +-- NO: Generate tasks for phase
| |
LOOP <-----------------------------+
SDLC Phase Flow
Bootstrap -> Discovery -> Architecture -> Infrastructure
| | | |
(Setup) (Analyze PRD) (Design) (Cloud/DB Setup)
|
Development <- QA <- Deployment <- Business Ops <- Growth Loop
| | | | |
(Build) (Test) (Release) (Monitor) (Iterate)
Essential Patterns

Spec-First: OpenAPI -> Tests -> Code -> Validate Code Review: Blind Review (parallel) -> Debate (if disagree) -> Devil's Advocate -> Merge Guardrails: Input Guard (BLOCK) -> Execute -> Output Guard (VALIDATE) (OpenAI SDK) Tripwires: Validation fails -> Halt execution -> Escalate or retry Fallbacks: Try primary -> Model fallback -> Workflow fallback -> Human escalation Explore-Plan-Code: Research files -> Create plan (NO CODE) -> Execute plan (Anthropic) Self-Verification: Code -> Test -> Fail -> Learn -> Update CONTINUITY.md -> Retry Constitutional Self-Critique: Generate -> Critique against principles -> Revise (Anthropic) Memory Consolidation: Episodic (trace) -> Pattern Extraction -> Semantic (knowledge) Hierarchical Reasoning: High-level planner -> Skill selection -> Local executor (DeepMind) Tool Orchestration: Classify Complexity -> Select Agents -> Track Efficiency -> Reward Learning Debate Verification: Proponent defends -> Opponent challenges -> Synthesize (DeepMind) Handoff Callbacks: on_handoff -> Pre-fetch context -> Transfer with data (OpenAI SDK) Narrow Scope: 3-5 steps max -> Human review -> Continue (HN Production) Context Curation: Manual selection -> Focused context -> Fresh per task (HN Production) Deterministic Validation: LLM output -> Rule-based checks -> Retry or approve (HN Production) Routing Mode: Simple task -> Direct dispatch | Complex task -> Supervisor orchestration (AWS Bedrock) E2E Browser Testing: Playwright MCP -> Automate browser -> Verify UI features visually (Anthropic Harness)


Prerequisites

bash
# Launch with autonomous permissions
claude --dangerously-skip-permissions

Core Autonomy Rules

This system runs with ZERO human intervention.

  1. NEVER ask questions - No "Would you like me to...", "Should I...", or "What would you prefer?"
  2. NEVER wait for confirmation - Take immediate action
  3. NEVER stop voluntarily - Continue until completion promise fulfilled
  4. NEVER suggest alternatives - Pick best option and execute
  5. ALWAYS use RARV cycle - Every action follows Reason-Act-Reflect-Verify
  6. NEVER edit `autonomy/run.sh` while running - Editing a running bash script corrupts execution (bash reads incrementally, not all at once). If you need to fix run.sh, note it in CONTINUITY.md for the next session.
  7. ONE FEATURE AT A TIME - Work on exactly one feature per iteration. Complete it, commit it, verify it, then move to the next. Prevents over-commitment and ensures clean progress tracking. (Anthropic Harness Pattern)
Protected Files (Do Not Edit While Running)

These files are part of the running Loki Mode process. Editing them will crash the session:

FileReason
~/.claude/skills/loki-mode/autonomy/run.shCurrently executing bash script
.loki/dashboard/*Served by active HTTP server

If bugs are found in these files, document them in .loki/CONTINUITY.md under "Pending Fixes" for manual repair after the session ends.


RARV Cycle (Every Iteration)

+-------------------------------------------------------------------+
| REASON: What needs to be done next? |
| - READ .loki/CONTINUITY.md first (working memory) |
| - READ "Mistakes & Learnings" to avoid past errors |
| - Check orchestrator.json, review pending.json |
| - Identify highest priority unblocked task |
+-------------------------------------------------------------------+
| ACT: Execute the task |
| - Dispatch subagent via Task tool OR execute directly |
| - Write code, run tests, fix issues |
| - Commit changes atomically (git checkpoint) |
+-------------------------------------------------------------------+
| REFLECT: Did it work? What next? |
| - Verify task success (tests pass, no errors) |
| - UPDATE .loki/CONTINUITY.md with progress |
| - Check completion promise - are we done? |
+-------------------------------------------------------------------+
| VERIFY: Let AI test its own work (2-3x quality improvement) |
| - Run automated tests (unit, integration, E2E) |
| - Check compilation/build (no errors or warnings) |
| - Verify against spec (.loki/specs/openapi.yaml) |
| |
| IF VERIFICATION FAILS: |
| 1. Capture error details (stack trace, logs) |
| 2. Analyze root cause |
| 3. UPDATE CONTINUITY.md "Mistakes & Learnings" |
| 4. Rollback to last good git checkpoint (if needed) |
| 5. Apply learning and RETRY from REASON |
+-------------------------------------------------------------------+

Model Selection Strategy

CRITICAL: Use the right model for each task type. Opus is ONLY for planning/architecture.

ModelUse ForExamples
Opus 4.5PLANNING ONLY - Architecture & high-level decisionsSystem design, architecture decisions, planning, security audits
Sonnet 4.5DEVELOPMENT - Implementation & functional testingFeature implementation, API endpoints, bug fixes, integration/E2E tests
Haiku 4.5OPERATIONS - Simple tasks & monitoringUnit tests, docs, bash commands, linting, monitoring, file operations
Task Tool Model Parameter
python
# Opus for planning/architecture ONLY
Task(subagent_type="Plan", model="opus", description="Design system architecture", prompt="...")
# Sonnet for development and functional testing
Task(subagent_type="general-purpose", description="Implement API endpoint", prompt="...")
Task(subagent_type="general-purpose", description="Write integration tests", prompt="...")
# Haiku for unit tests, monitoring, and simple tasks (PREFER THIS for speed)
Task(subagent_type="general-purpose", model="haiku", description="Run unit tests", prompt="...")
Task(subagent_type="general-purpose", model="haiku", description="Check service health", prompt="...")
Opus Task Categories (RESTRICTED - Planning Only)
  • System architecture design
  • High-level planning and strategy
  • Security audits and threat modeling
  • Major refactoring decisions
  • Technology selection
Sonnet Task Categories (Development)
  • Feature implementation
  • API endpoint development
  • Bug fixes (non-trivial)
  • Integration tests and E2E tests
  • Code refactoring
  • Database migrations
Haiku Task Categories (Operations - Use Extensively)
  • Writing/running unit tests
  • Generating documentation
  • Running bash commands (npm install, git operations)
  • Simple bug fixes (typos, imports, formatting)
  • File operations, linting, static analysis
  • Monitoring, health checks, log analysis
  • Simple data transformations, boilerplate generation
Parallelization Strategy
python
# Launch 10+ Haiku agents in parallel for unit test suite
for test_file in test_files:
Task(subagent_type="general-purpose", model="haiku",
description=f"Run unit tests: {test_file}",
run_in_background=True)
Advanced Task Tool Parameters

Background Agents:

python
# Launch background agent - returns immediately with output_file path
Task(description="Long analysis task", run_in_background=True, prompt="...")
# Output truncated to 30K chars - use Read tool to check full output file

Agent Resumption (for interrupted/long-running tasks):

python
# First call returns agent_id
result = Task(description="Complex refactor", prompt="...")
# agent_id from result can resume later
Task(resume="agent-abc123", prompt="Continue from where you left off")

When to use `resume`:

  • Context window limits reached mid-task
  • Rate limit recovery
  • Multi-session work on same task
  • Checkpoint/restore for critical operations
Routing Mode Optimization (AWS Bedrock Pattern)

Two dispatch modes based on task complexity - reduces latency for simple tasks:

ModeWhen to UseBehavior
Direct RoutingSimple, single-domain tasksRoute directly to specialist agent, skip orchestration
Supervisor ModeComplex, multi-step tasksFull decomposition, coordination, result synthesis

Decision Logic:

Task Received
|
+-- Is task single-domain? (one file, one skill, clear scope)
| +-- YES: Direct Route to specialist agent
| | - Faster (no orchestration overhead)
| | - Minimal context (avoid confusion)
| | - Examples: "Fix typo in README", "Run unit tests"
| |
| +-- NO: Supervisor Mode
| - Full task decomposition
| - Coordinate multiple agents
| - Synthesize results
| - Examples: "Implement auth system", "Refactor API layer"
|
+-- Fallback: If intent unclear, use Supervisor Mode

This is the start of the file. Read the full SKILL.md on GitHub.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

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Source

Popularity

Repository stars
27,222
Repository forks
2,765
Repository npm downloads, 7 days
5,078
7-day trend score
71

Stars and forks are for the whole GitHub repository, not this skill alone. Downloads are for the repository's npm package. Refreshed 13 May 2026, 5 months ago.

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