giulio-leone

Rules and strategies for managing agent context window size, avoiding bloat, and preserving signal-to-noise ratio.

Standard patterns for error handling, retry logic, circuit breakers, and graceful degradation.

Detect and remove contradictions across agent policies before execution.

Multi-step tool workflows via code orchestration to reduce latency, context pollution, and token overhead.

Bind project-specific prompts to local schema and workflow artifacts while keeping the harness core generic and globally reusable.

Operational session protocol for task-scoped leases, reconciliation, checkpoints, inspection, queue promotion, and handoff across long-running work.

Operational session protocol for task-scoped leases, reconciliation, checkpoints, inspection, queue promotion, and handoff across long-running work.

Two-phase agent harness lifecycle — initializer session for scaffolding and incremental sessions for feature-by-feature execution with cross-session memory.

Structured decision process between Non-Breaking and Breaking paths without relaxing quality gates.

Mandatory quality gate for every Issue, Milestone, and PR with zero-error execution and no technical debt carry-over.

Deterministic, CI-ready end-to-end validation for critical user flows.

Keep local plan and GitHub project artifacts perfectly aligned with milestones, issues, labels, and statuses.

Iterative decision loop with consistent user checkpoints and explicit stop criteria.

Execution plan with milestone/issue hierarchy, explicit dependencies, and safe parallelism.

Detect and remove contradictions across agent policies before execution.

Multi-step tool workflows via code orchestration to reduce latency, context pollution, and token overhead.

OnePlan is a GitHub-native, context-driven engineering workflow using Milestones (tracks), Issues (spec/plan), and Sub-issues (phases/tasks). Use for complex features/bugfixes that need structured planning, resumable execution, parallel tasks, and team coordination.

Systematic technical debt reduction and code optimization agent. Use when refactoring code, reducing complexity, eliminating code smells, improving performance, cleaning up unused code, or modernizing legacy patterns. Handles dependency updates, architecture improvements, and codebase health metrics.

State-of-the-art debugging agent with hypothesis-driven analysis, automatic code instrumentation, git worktree isolation, and browser automation. Use when debugging errors, stack traces, unexpected behavior, performance issues, failed tests, race conditions, or hard-to-reproduce bugs.

Meta-skill for creating, refining, and managing Agent Skills. Use when needing to create new skills, improve existing skills, analyze skill performance, or teach the agent new capabilities. Enables self-improvement and knowledge capture.