SkillsLib.ai

Loki Mode

Deploy products from specs autonomously with multi-agent coordination

4.5(49 reviews)
1,000+ downloads
Updated Oct 2026
Verified SafeSecurity VerifiedThis skill was analyzed by our AI security scanner for harmful content including data exfiltration, system manipulation, credential theft, and prompt injection. No threats were detected.

What You Can Do

Activate Loki Mode to orchestrate a multi-agent team that autonomously takes any product specification—whether a Markdown PRD, GitHub issue, OpenAPI doc, or Jira ticket—and drives it through to deployment. The system makes decisions independently, coordinates across multiple AI providers (Claude, Codex, Gemini, Cline, Aider), and tracks progress through phases while maintaining state and handling failures gracefully.

Features

Spec-agnostic input

accepts PRDs, GitHub issues, OpenAPI docs, Jira tickets, or any structured specification format

Multi-provider orchestration

routes tasks across Claude, Codex, Gemini, Cline, and Aider with abstract model tiers and degraded mode support

Autonomous decision-making

operates without stopping to ask questions; makes calls independently within defined phases

State persistence

maintains session context, task queues, orchestrator state, and memory indexes across execution

Phase-based workflow

structures work into RARV-C phases (Requirement, Architecture, Review, Validation, Closure) with task generation and phase progression

Runtime flexibility

supports both Bash and Bun runtimes with seamless fallback; migration in progress for performance

Status tracking

provides JSON-formatted status reports, stats, and doctor diagnostics for monitoring execution

Example Output

Example 1: GitHub Issue → Deployed Feature

code
Input: GitHub issue #342 requesting user authentication with OAuth2
Loki processes: Extracts requirements → Generates architecture → Creates implementation tasks → Writes code → Runs tests → Deploys to staging → Validates integration
Output: OAuth2 authentication feature live on staging, session log with phase completion timestamps

Example 2: OpenAPI Spec → Backend API

code
Input: OpenAPI 3.0 spec for payment processing REST API
Loki processes: Parses endpoints → Generates data models → Creates route handlers → Implements validation → Adds error handling → Deploys to production
Output: Fully functional payment API with Swagger docs and automated test coverage

Example 3: PRD → Full Product

code
Input: 5-page product requirements document for a real-time collaboration tool
Loki processes: Extracts user stories → Plans architecture → Coordinates frontend/backend teams → Manages dependencies → Validates requirements → Ships MVP
Output: Deployed product with documented decisions and phase completion report

What's Included

  • `SKILL.md`: Complete Loki Mode instruction file with phase logic, decision trees, and agent behaviors
  • `skills/00-index.md`: Module registry and phase-specific skill mappings
  • `.loki/state/` templates: Orchestrator state schema, session tracking, and task queue formats
  • `skills/providers.md`: Multi-provider routing configuration and degraded mode fallbacks
  • `docs/architecture/ADR-001-runtime-migration.md`: Runtime migration guide (Bash → Bun)

Who It's For

  • Engineering managers — delegate product spec-to-launch execution to autonomous agents
  • Startup founders — rapidly prototype and deploy MVPs from PRDs without manual coordination
  • DevOps engineers — automate infrastructure-as-spec workflows triggered by Jira/GitHub
  • Platform teams — orchestrate cross-team delivery pipelines from requirements to production
  • Solo developers — scale output by running parallel autonomous sprints from issue backlogs

Best For

  • Rapid MVP deployment from product requirements or issue descriptions
  • Multi-phase product development coordinated across architecture, implementation, testing, and deployment
  • API-first development triggered by OpenAPI specs or async specifications
  • Autonomous sprint execution driven by GitHub issues or Jira tickets
  • Cross-provider orchestration when you need fallback AI models or heterogeneous agent teams

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