Results for “context-engine”

Search the FPF knowledge base and display hypothesis details with assurance information

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Reconcile the project's FPF state with recent repository changes

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Execute one complex task as ordered, dependent steps run sequentially, passing context from each step to the next, with per-step LLM-as-a-judge verification. Use when later steps depend on the results of earlier ones.

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Manage evidence freshness by identifying stale decisions and providing governance actions

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Use when working on multiple branches simultaneously, context switching without stashing, reviewing PRs while developing, testing in isolation, or comparing implementations across branches - provides git worktree commands and workflow patterns for parallel development with multiple working directories.

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Reset the FPF reasoning cycle to start fresh

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Use when adding metadata to commits without changing history, tracking review status, test results, code quality annotations, or supplementing commit messages post-hoc - provides git notes commands and patterns for attaching non-invasive metadata to Git objects.

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Implement a task step by step with automated LLM-as-Judge verification at the end of each phase

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Systematically fix all failing tests after business logic changes or refactoring

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Use to load open/unresolved PR review comments then aggregate them as tasks in .specs/comments/*.md for parallel agents to fix.

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Add line-specific review comments to pull requests using GitHub CLI API

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Iterative PDCA cycle for systematic experimentation and continuous improvement

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Use when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide tehniquest to avoid over-engineering and apply iterative improvements.

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Comprehensive guide for creating Claude Code agents with proper structure, triggering conditions, system prompts, and validation - combines official Anthropic best practices with proven patterns

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creates draft task file in .specs/tasks/draft/ with original user intent

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Refine a draft task specification into a fully planned, implementation-ready task with acceptance criteria, architecture, per-step sub-task files and verifiable phases

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Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.

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Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification

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Interactive assistant for creating new Claude commands with proper structure, patterns, and MCP tool integration

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Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification

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Use when creating or developing, before writing code or implementation plans - refines rough ideas into fully-formed designs through collaborative questioning, alternative exploration, and incremental validation. Don't use during clear 'mechanical' processes

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Evaluate solutions through multi-round debate between independent judges until consensus

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Launch a meta-judge then a judge sub-agent to evaluate results produced in the current conversation

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Auto-selects best Kaizen method (Gemba Walk, Value Stream, or Muda) for target

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