'context-engineering' 검색 결과

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

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Comprehensive A3 one-page problem analysis with root cause and action plan

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Reconcile the project's FPF state with recent repository changes

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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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Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Use when tasks do not depend on each other and can run side by side.

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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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Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.

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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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Create well-formatted commits with conventional commit messages and emoji

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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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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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Creates new Source of Truth (SoT) files when existing templates don't fit your needs. Triggers on requests to create a new SoT file, add a new artifact type, or when user says "I need to track [X] but there's no SoT for it", "create SoT", "new source of truth". Outputs a properly structured SoT.*.md file with ID prefix, frontmatter, and update protocol.

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Build Tier 1/2/3 cold outreach sequences differentiated by personalization depth and research signal strength during PRD v0.9 Go-to-Market. Triggers on requests to plan cold outreach, founder-led sales, or when user asks "cold email sequence", "outbound campaign", "founder outreach", "LinkedIn outreach", "Tier 1 personalization", "predictable revenue", "sales cadence", "BDR sequence". Outputs GTM-OUT-* sequence entries and lead-list references.

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Orchestrate go-to-market strategy by running positioning, offer construction, and channel allocation as a sequenced workflow during PRD v0.9 Go-to-Market. Triggers on requests to plan launch, define GTM strategy, or when user asks "how do we launch?", "go-to-market", "launch plan", "marketing strategy", "GTM", "launch this product". Chains three framework-grounded sub-skills (Dunford → Hormozi → ORB) and reconciles their outputs.

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Construct a high-conversion offer using Alex Hormozi's value equation and Grand Slam Offer mechanics during PRD v0.9 Go-to-Market. Triggers on requests to design the offer, set up the pitch, build bonuses or guarantees, or when user asks "how do we package this?", "what's the offer?", "build a grand slam offer", "Hormozi", "$100M offers", "guarantee", "bonus stack". Outputs GTM-* entries with Type=Offer / Type=Guarantee and BR-PRICING-* updates.

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Design the changelog as a distribution surface bridging engineering releases (DEP-/FEA-) to marketing channels (GTM-) during PRD v0.8 Deployment & Ops. Triggers on requests to set up a changelog system, publish releases, or when user asks "how should we publish releases?", "changelog as marketing", "release notes", "Stripe-style changelog", "what to do with our release notes", "ship and tell". Outputs MON-CHG-* changelog entries and per-channel formats.

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Establish channels and processes for capturing and processing post-launch feedback during PRD v0.9 Go-to-Market. Triggers on requests to set up feedback systems, capture user input, or when user asks "how do we collect feedback?", "feedback loop", "user research", "post-launch feedback", "customer feedback", "NPS", "voice of customer". Outputs CFD- entries specialized for post-launch feedback capture.

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Map the competitive landscape before positioning your product for PRD v0.2 Market Definition. Triggers on completing v0.1 Spark, analyzing competitors, researching market, or requests like "competitive analysis", "who else solves this", "market landscape", "what alternatives exist", "competitor research", "feature comparison". Outputs CFD- entries for competitive intelligence and BR- entries for positioning rules.

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Audit how AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude) describe and recommend your product, then propose fixes. Triggers on requests to audit AI search visibility, improve AEO/GEO, check ChatGPT/Perplexity coverage, or when user asks "do we show up in AI search?", "AEO audit", "generative engine optimization", "AI discoverability", "how does ChatGPT describe us?", "Perplexity ranking". Outputs GTM-AEO-* entries and a Coverage Matrix.

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Allocate launch channels using the Owned / Rented / Borrowed (ORB) framework during PRD v0.9 Go-to-Market. Triggers on requests to pick launch channels, distribute the offer, build a channel portfolio, or when user asks "where do we launch?", "what channels?", "ORB", "owned vs rented vs borrowed", "channel allocation", "channel mix", "Corey Haines launch". Outputs GTM-* entries with Type=Channel, a channel-mix matrix, and a launch sequence.

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