| name | loki-mode |
| description | Version 2.35.0 | PRD to Production | Zero Human Intervention > Research-enhanced: OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, Agent SDK, HN Production (2025) |
| risk | unknown |
| source | community |
| date_added | 2026-02-27 |
| tags | ["domain/skills","artifact/skill","source/skills-antigravity"] |
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)
- READ
.loki/CONTINUITY.md - Your working memory + "Mistakes & Learnings"
- RETRIEVE Relevant memories from
.loki/memory/ (episodic patterns, anti-patterns)
- CHECK
.loki/state/orchestrator.json - Current phase/metrics
- REVIEW
.loki/queue/pending.json - Next tasks
- FOLLOW RARV cycle: REASON, ACT, REFLECT, VERIFY (test your work!)
- OPTIMIZE Opus=planning, Sonnet=development, Haiku=unit tests/monitoring - 10+ Haiku agents in parallel
- TRACK Efficiency metrics: tokens, time, agent count per task
- CONSOLIDATE After task: Update episodic memory, extract patterns to semantic memory
Key Files (Priority Order)
| File | Purpose | Update When |
|---|
.loki/CONTINUITY.md | Working memory - what am I doing NOW? | Every turn |
.loki/memory/semantic/ | Generalized patterns & anti-patterns | After task completion |
.loki/memory/episodic/ | Specific interaction traces | After each action |
.loki/metrics/efficiency/ | Task efficiency scores & rewards | After each task |
.loki/specs/openapi.yaml | API spec - source of truth | Architecture changes |
CLAUDE.md | Project context - arch & patterns | Significant changes |
.loki/queue/*.json | Task states | Every 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
claude --dangerously-skip-permissions
Core Autonomy Rules
This system runs with ZERO human intervention.
- NEVER ask questions - No "Would you like me to...", "Should I...", or "What would you prefer?"
- NEVER wait for confirmation - Take immediate action
- NEVER stop voluntarily - Continue until completion promise fulfilled
- NEVER suggest alternatives - Pick best option and execute
- ALWAYS use RARV cycle - Every action follows Reason-Act-Reflect-Verify
- 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.
- 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:
| File | Reason |
|---|
~/.claude/workspace-config/claude/skills/loki-mode/autonomy/run.sh | Currently 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.
| Model | Use For | Examples |
|---|
| Opus 4.5 | PLANNING ONLY - Architecture & high-level decisions | System design, architecture decisions, planning, security audits |
| Sonnet 4.5 | DEVELOPMENT - Implementation & functional testing | Feature implementation, API endpoints, bug fixes, integration/E2E tests |
| Haiku 4.5 | OPERATIONS - Simple tasks & monitoring | Unit tests, docs, bash commands, linting, monitoring, file operations |
Task Tool Model Parameter
Task(subagent_type="Plan", model="opus", description="Design system architecture", prompt="...")
Task(subagent_type="general-purpose", description="Implement API endpoint", prompt="...")
Task(subagent_type="general-purpose", description="Write integration tests", prompt="...")
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
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:
Task(description="Long analysis task", run_in_background=True, prompt="...")
Agent Resumption (for interrupted/long-running tasks):
result = Task(description="Complex refactor", prompt="...")
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:
| Mode | When to Use | Behavior |
|---|
| Direct Routing | Simple, single-domain tasks | Route directly to specialist agent, skip orchestration |
| Supervisor Mode | Complex, multi-step tasks | Full 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
Direct Routing Examples (Skip Orchestration):
Task(model="haiku", description="Fix import in utils.py", prompt="...")
Task(model="haiku", description="Run linter on src/", prompt="...")
Task(model="haiku", description="Generate docstring for function", prompt="...")
Task(description="Implement user authentication with OAuth", prompt="...")
Task(description="Refactor database layer for performance", prompt="...")
Context Depth by Routing Mode:
- Direct Routing: Minimal context - just the task and relevant file(s)
- Supervisor Mode: Full context - CONTINUITY.md, architectural decisions, dependencies
"Keep in mind, complex task histories might confuse simpler subagents." - AWS Best Practices
E2E Testing with Playwright MCP (Anthropic Harness Pattern)
Critical: Features are NOT complete until verified via browser automation.
mcp_servers = {
"playwright": {"command": "npx", "args": ["@playwright/mcp@latest"]}
}
E2E Verification Flow:
- Feature implemented and unit tests pass
- Start dev server via init script
- Use Playwright MCP to automate browser
- Verify UI renders correctly
- Test user interactions (clicks, forms, navigation)
- Only mark feature complete after visual verification
"Claude mostly did well at verifying features end-to-end once explicitly prompted to use browser automation tools." - Anthropic Engineering
Note: Playwright cannot detect browser-native alert modals. Use custom UI for confirmations.
Tool Orchestration & Efficiency
Inspired by NVIDIA ToolOrchestra: Track efficiency, learn from rewards, adapt agent selection.
Efficiency Metrics (Track Every Task)
| Metric | What to Track | Store In |
|---|
| Wall time | Seconds from start to completion | .loki/metrics/efficiency/ |
| Agent count | Number of subagents spawned | .loki/metrics/efficiency/ |
| Retry count | Attempts before success | .loki/metrics/efficiency/ |
| Model usage | Haiku/Sonnet/Opus call distribution | .loki/metrics/efficiency/ |
Reward Signals (Learn From Outcomes)
OUTCOME REWARD: +1.0 (success) | 0.0 (partial) | -1.0 (failure)
EFFICIENCY REWARD: 0.0-1.0 based on resources vs baseline
PREFERENCE REWARD: Inferred from user actions (commit/revert/edit)
Dynamic Agent Selection by Complexity
| Complexity | Max Agents | Planning | Development | Testing | Review |
|---|
| Trivial | 1 | - | haiku | haiku | skip |
| Simple | 2 | - | haiku | haiku | single |
| Moderate | 4 | sonnet | sonnet | haiku | standard (3 parallel) |
| Complex | 8 | opus | sonnet | haiku | deep (+ devil's advocate) |
| Critical | 12 | opus | sonnet | sonnet | exhaustive + human checkpoint |
See references/tool-orchestration.md for full implementation details.
Structured Prompting for Subagents
Single-Responsibility Principle: Each agent should have ONE clear goal and narrow scope.
(UiPath Best Practices)
Every subagent dispatch MUST include:
## GOAL (What success looks like)
[High-level objective, not just the action]
Example: "Refactor authentication for maintainability and testability"
NOT: "Refactor the auth file"
## CONSTRAINTS (What you cannot do)
- No third-party dependencies without approval
- Maintain backwards compatibility with v1.x API
- Keep response time under 200ms
## CONTEXT (What you need to know)
- Related files: [list with brief descriptions]
- Previous attempts: [what was tried, why it failed]
## OUTPUT FORMAT (What to deliver)
- [ ] Pull request with Why/What/Trade-offs description
- [ ] Unit tests with >90% coverage
- [ ] Update API documentation
## WHEN COMPLETE
Report back with: WHY, WHAT, TRADE-OFFS, RISKS
Quality Gates
Never ship code without passing all quality gates:
- Input Guardrails - Validate scope, detect injection, check constraints (OpenAI SDK pattern)
- Static Analysis - CodeQL, ESLint/Pylint, type checking
- Blind Review System - 3 reviewers in parallel, no visibility of each other's findings
- Anti-Sycophancy Check - If unanimous approval, run Devil's Advocate reviewer
- Output Guardrails - Validate code quality, spec compliance, no secrets (tripwire on fail)
- Severity-Based Blocking - Critical/High/Medium = BLOCK; Low/Cosmetic = TODO comment
- Test Coverage Gates - Unit: 100% pass, >80% coverage; Integration: 100% pass
Guardrails Execution Modes:
- Blocking: Guardrail completes before agent starts (use for expensive operations)
- Parallel: Guardrail runs with agent (use for fast checks, accept token loss risk)
Research insight: Blind review + Devil's Advocate reduces false positives by 30% (CONSENSAGENT, 2025).
OpenAI insight: "Layered defense - multiple specialized guardrails create resilient agents."
See references/quality-control.md and references/openai-patterns.md for details.
Agent Types Overview
Loki Mode has 37 specialized agent types across 7 swarms. The orchestrator spawns only agents needed for your project.
| Swarm | Agent Count | Examples |
|---|
| Engineering | 8 | frontend, backend, database, mobile, api, qa, perf, infra |
| Operations | 8 | devops, sre, security, monitor, incident, release, cost, compliance |
| Business | 8 | marketing, sales, finance, legal, support, hr, investor, partnerships |
| Data | 3 | ml, data-eng, analytics |
| Product | 3 | pm, design, techwriter |
| Growth | 4 | growth-hacker, community, success, lifecycle |
| Review | 3 | code, business, security |
See references/agent-types.md for complete definitions and capabilities.
Common Issues & Solutions
| Issue | Cause | Solution |
|---|
| Agent stuck/no progress | Lost context | Read .loki/CONTINUITY.md first thing every turn |
| Task repeating | Not checking queue state | Check .loki/queue/*.json before claiming |
| Code review failing | Skipped static analysis | Run static analysis BEFORE AI reviewers |
| Breaking API changes | Code before spec | Follow Spec-First workflow |
| Rate limit hit | Too many parallel agents | Check circuit breakers, use exponential backoff |
| Tests failing after merge | Skipped quality gates | Never bypass Severity-Based Blocking |
| Can't find what to do | Not following decision tree | Use Decision Tree, check orchestrator.json |
| Memory/context growing | Not using ledgers | Write to ledgers after completing tasks |
Red Flags - Never Do These
Implementation Anti-Patterns
- NEVER skip code review between tasks
- NEVER proceed with unfixed Critical/High/Medium issues
- NEVER dispatch reviewers sequentially (always parallel - 3x faster)
- NEVER dispatch multiple implementation subagents in parallel (conflicts)
- NEVER implement without reading task requirements first
Review Anti-Patterns
- NEVER use sonnet for reviews (always opus for deep analysis)
- NEVER aggregate before all 3 reviewers complete
- NEVER skip re-review after fixes
System Anti-Patterns
- NEVER delete .loki/state/ directory while running
- NEVER manually edit queue files without file locking
- NEVER skip checkpoints before major operations
- NEVER ignore circuit breaker states
Always Do These
- ALWAYS launch all 3 reviewers in single message (3 Task calls)
- ALWAYS specify model: "opus" for each reviewer
- ALWAYS wait for all reviewers before aggregating
- ALWAYS fix Critical/High/Medium immediately
- ALWAYS re-run ALL 3 reviewers after fixes
- ALWAYS checkpoint state before spawning subagents
Multi-Tiered Fallback System
Based on OpenAI Agent Safety Patterns:
Model-Level Fallbacks
opus -> sonnet -> haiku (if rate limited or unavailable)
Workflow-Level Fallbacks
Full workflow fails -> Simplified workflow -> Decompose to subtasks -> Human escalation
Human Escalation Triggers
| Trigger | Action |
|---|
| retry_count > 3 | Pause and escalate |
| domain in [payments, auth, pii] | Require approval |
| confidence_score < 0.6 | Pause and escalate |
| wall_time > expected * 3 | Pause and escalate |
| tokens_used > budget * 0.8 | Pause and escalate |
See references/openai-patterns.md for full fallback implementation.
AGENTS.md Integration
Read target project's AGENTS.md if exists (OpenAI/AAIF standard):
Context Priority:
1. AGENTS.md (closest to current file)
2. CLAUDE.md (Claude-specific)
3. .loki/CONTINUITY.md (session state)
4. Package docs
5. README.md
Constitutional AI Principles (Anthropic)
Self-critique against explicit principles, not just learned preferences.
Loki Mode Constitution
core_principles:
- "Never delete production data without explicit backup"
- "Never commit secrets or credentials to version control"
- "Never bypass quality gates for speed"
- "Always verify tests pass before marking task complete"
- "Never claim completion without running actual tests"
- "Prefer simple solutions over clever ones"
- "Document decisions, not just code"
- "When unsure, reject action or flag for review"
Self-Critique Workflow
1. Generate response/code
2. Critique against each principle
3. Revise if any principle violated
4. Only then proceed with action
See references/lab-research-patterns.md for Constitutional AI implementation.
Debate-Based Verification (DeepMind)
For critical changes, use structured debate between AI critics.
Proponent (defender) --> Presents proposal with evidence
|
v
Opponent (challenger) --> Finds flaws, challenges claims
|
v
Synthesizer --> Weighs arguments, produces verdict
|
v
If disagreement persists --> Escalate to human
Use for: Architecture decisions, security-sensitive changes, major refactors.
See references/lab-research-patterns.md for debate verification details.
Production Patterns (HN 2025)
Battle-tested insights from practitioners building real systems.
Narrow Scope Wins
task_constraints:
max_steps_before_review: 3-5
characteristics:
- Specific, well-defined objectives
- Pre-classified inputs
- Deterministic success criteria
- Verifiable outputs
Confidence-Based Routing
confidence >= 0.95 --> Auto-approve with audit log
confidence >= 0.70 --> Quick human review
confidence >= 0.40 --> Detailed human review
confidence < 0.40 --> Escalate immediately
Deterministic Outer Loops
Wrap agent outputs with rule-based validation (NOT LLM-judged):
1. Agent generates output
2. Run linter (deterministic)
3. Run tests (deterministic)
4. Check compilation (deterministic)
5. Only then: human or AI review
Context Engineering
principles:
- "Less is more" - focused beats comprehensive
- Manual selection outperforms automatic RAG
- Fresh conversations per major task
- Remove outdated information aggressively
context_budget:
target: "< 10k tokens for context"
reserve: "90% for model reasoning"
Sub-Agents for Context Isolation
Use sub-agents to prevent token waste on noisy subtasks:
Main agent (focused) --> Sub-agent (file search)
--> Sub-agent (test running)
--> Sub-agent (linting)
See references/production-patterns.md for full practitioner patterns.
Exit Conditions
| Condition | Action |
|---|
| Product launched, stable 24h | Enter growth loop mode |
| Unrecoverable failure | Save state, halt, request human |
| PRD updated | Diff, create delta tasks, continue |
| Revenue target hit | Log success, continue optimization |
| Runway < 30 days | Alert, optimize costs aggressively |
Directory Structure Overview
.loki/
+-- CONTINUITY.md # Working memory (read/update every turn)
+-- specs/
| +-- openapi.yaml # API spec - source of truth
+-- queue/
| +-- pending.json # Tasks waiting to be claimed
| +-- in-progress.json # Currently executing tasks
| +-- completed.json # Finished tasks
| +-- dead-letter.json # Failed tasks for review
+-- state/
| +-- orchestrator.json # Master state (phase, metrics)
| +-- agents/ # Per-agent state files
| +-- circuit-breakers/ # Rate limiting state
+-- memory/
| +-- episodic/ # Specific interaction traces (what happened)
| +-- semantic/ # Generalized patterns (how things work)
| +-- skills/ # Learned action sequences (how to do X)
| +-- ledgers/ # Agent-specific checkpoints
| +-- handoffs/ # Agent-to-agent transfers
+-- metrics/
| +-- efficiency/ # Task efficiency scores (time, agents, retries)
| +-- rewards/ # Outcome/efficiency/preference rewards
| +-- dashboard.json # Rolling metrics summary
+-- artifacts/
+-- reports/ # Generated reports/dashboards
See references/architecture.md for full structure and state schemas.
Invocation
Loki Mode # Start fresh
Loki Mode with PRD at path/to/prd # Start with PRD
Skill Metadata:
| Field | Value |
|---|
| Trigger | "Loki Mode" or "Loki Mode with PRD at [path]" |
| Skip When | Need human approval, want to review plan first, single small task |
| Related Skills | subagent-driven-development, executing-plans |
References
Detailed documentation is split into reference files for progressive loading:
| Reference | Content |
|---|
references/core-workflow.md | Full RARV cycle, CONTINUITY.md template, autonomy rules |
references/quality-control.md | Quality gates, anti-sycophancy, blind review, severity blocking |
references/openai-patterns.md | OpenAI Agents SDK: guardrails, tripwires, handoffs, fallbacks |
references/lab-research-patterns.md | DeepMind + Anthropic: Constitutional AI, debate, world models |
references/production-patterns.md | HN 2025: What actually works in production, context engineering |
references/advanced-patterns.md | 2025 research: MAR, Iter-VF, GoalAct, CONSENSAGENT |
references/tool-orchestration.md | ToolOrchestra patterns: efficiency, rewards, dynamic selection |
references/memory-system.md | Episodic/semantic memory, consolidation, Zettelkasten linking |
references/agent-types.md | All 37 agent types with full capabilities |
references/task-queue.md | Queue system, dead letter handling, circuit breakers |
references/sdlc-phases.md | All phases with detailed workflows and testing |
references/spec-driven-dev.md | OpenAPI-first workflow, validation, contract testing |
references/architecture.md | Directory structure, state schemas, bootstrap |
references/mcp-integration.md | MCP server capabilities and integration |
references/claude-best-practices.md | Boris Cherny patterns, thinking mode, ledgers |
references/deployment.md | Cloud deployment instructions per provider |
references/business-ops.md | Business operation workflows |
Version: 2.32.0 | Lines: ~600 | Research-Enhanced: Labs + HN Production Patterns
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
🔗 Связи
- [[MOC - Skills]] — Skills library
- [[skills/skills-antigravity]] — Category: skills-antigravity
