「context-engine」の検索結果

Systematic Fishbone analysis exploring problem causes across six categories

日本語の概要は準備中です。原文の説明を表示しています。

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

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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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Reconcile the project's FPF state with recent repository changes

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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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Use before writing any type of tests. Distills 14 industry sources into deterministic decision gates, schemas, and worked test examples.

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Manage evidence freshness by identifying stale decisions and providing governance actions

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Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts

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Execute tasks through competitive multi-agent generation, meta-judge evaluation specification, multi-judge evaluation, and evidence-based synthesis

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Use when found gap or repetative issue, that produced by you or implemenataion agent. Esentially use it each time when you say "You absolutly right, I should have done it differently." -> need create rule for this issue so it not appears again.

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Create and configure git hooks with intelligent project analysis, suggestions, and automated testing

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Review what an LLM feature or agent actually puts in its context window — and find what's bloating, missing, or fighting itself. Use when asked to review a system prompt and context assembly, cut token usage without losing quality, debug an agent that ignores instructions, or audit how retrieval results, history, and tool definitions are packed into the window. Produces a context inventory with a keep/cut/restructure verdict per component, ordering and caching fixes, and a token budget. For wording-level prompt tuning use prompt-optimizer.

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项目级上下文工程框架：通过 PROJECT / REQUIREMENTS / ROADMAP / STATE 四层文档 为每个任务建立结构化上下文，控制 token 预算，防止上下文腐烂， 确保跨会话连续性。与 context-lifecycle 的 L0-L3 层互补。

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Audit a repo's agent context — CLAUDE.md files, codebase docs, skills, and tool/MCP designs — against Anthropic's Claude 5 context-engineering guidance ("unhobbling": Anthropic cut ~80% of Claude Code's system prompt with no eval loss). Finds overconstraint, conflicting instructions, redundancy, stale facts, and missing "unknown knowns"; produces a scored findings report with concrete rewrites, then applies approved fixes. Use when someone says "audit my CLAUDE.md", "context audit", "unhobble this repo", "review our agent docs/skills/tools", or after upgrading to Claude 5-generation models.

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Context management engine for AI coding agents. Use when building agent memory systems, optimizing context windows, allocating token budgets, designing RAG pipelines for code, or managing persistent multi-session agent state.

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Load shared marketing context: brand profile, benchmarks, compliance rules, specs. "load my brand context"

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Manages attention and evidence in long agent sessions. Use for lost instructions, dropped evidence, or large multi-agent contexts.

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Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.

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Make technology decisions for every product capability by discovering existing assets, evaluating vendor-aligned options, and categorizing as Reuse/Extend/Build/Buy/Integrate/Research during PRD v0.5 Red Team Review. Handles both greenfield and brownfield contexts. Triggers on "tech stack", "build or buy?", "what technologies?", "technical decisions", "what do we reuse?", "existing stack", "vendor constraint", "IBM-first", "what tools do we need?", "evaluate solutions", "select tech stack". Consumes FEA- (features), SCR- (screens), RISK- (constraints). Outputs TECH- entries with decisions, rationale, and cross-references. Feeds v0.6 Architecture Design.

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[1-2 sentence description of what this skill does]. Triggers on [specific phrases/contexts that should activate this skill]. Outputs [what the skill produces].

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Install the PRD-Driven Context Engineering methodology into a fresh OR existing repository — the subscription-native alternative to forking the whole repo. Runs an interactive wizard that seeds the framework (.claude/ hooks, skills, agents, rules, scripts) without clobbering product content. Triggers on "install the methodology", "adopt this into my repo", "self-install", "set up PRD lifecycle here", "onboard existing project". Outputs an installed framework + a verification report.

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Validates and registers new SoT IDs with cross-reference integrity. Triggers when creating BR-XXX, UJ-XXX, API-XXX, or CFD-XXX entries. Outputs formatted SoT entry with validated cross-references.

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Synchronizes README.md Command Center with current project state. Triggers on gate changes, EPIC status changes, or explicit `/ghm-status-sync` invocation. Outputs updated README.md dashboard with current lifecycle stage, blockers, and metrics.

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Transform vague product ideas into evidence-anchored problem statements for PRD v0.1 Spark. Triggers on starting new products/features, validating market opportunities, drafting PRD Why sections, or requests like "frame the problem", "define pain points", "write problem statement", "start v0.1", "what problem are we solving". Outputs structured problem tables with CFD evidence IDs.

日本語の概要は準備中です。原文の説明を表示しています。