'improvement' 검색 결과

Harness-first workflow for evaluating and improving terminal UIs through real interaction. Use this whenever the user wants to review a TUI experience, compare before/after behavior, identify UX friction, validate navigation or filtering flows, inspect visual stability, or turn observed interaction problems into concrete follow-up tests and improvement recommendations. Prefer this skill any time a TUI should be exercised through a reproducible PTY/session harness instead of only reading code.

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Use when reviewing, fixing, or verifying a pull request. Focus on correctness regressions, missing tests, permission mistakes, unsafe behavior, and minimal fixes.

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GStack retrospective method for completed workstreams, identifying what worked, what failed, process changes, and role-skill improvements.

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Apply the Workflow Orchestration methodology for complex, multi-step tasks — plan-first approach, subagent strategy, self-improvement loop, verification before done, elegant solutions, and autonomous bug fixing. Use for any non-trivial task with 3+ steps or architectural decisions.

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Use this skill when an incident or a serious near miss is closed and the team must learn from it, or an auditor asks for lessons learned, and someone says "run the post-mortem", "post-incident review", "lessons learned", "retro on the outage", "blameless review", "root cause of the incident" or "what do we change after this breach". Builds the timeline from records before the meeting, runs a blameless discussion, finds the contributing factors, and turns them into a few owned, dated actions linked to updates of the risk register, controls, playbooks and supplier records, then writes the review and an action tracker.

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Operations leadership for scaling companies. Process design, OKR execution, operational cadence, and scaling playbooks. Use when designing operations, setting up OKRs, building processes, scaling teams, analyzing bottlenecks, planning operational cadence, or when user mentions COO, operations, process improvement, OKRs, scaling, operational efficiency, or execution.

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Ranks any list of impediments and their countermeasures using a value-stream scoring model (ROI, Cost to Implement, Ease of Deployment, Risk Factor) and a fixed prioritization formula. Use when someone asks to prioritize, rank, sequence, or triage impediments, countermeasures, remediation items, risks, findings, gaps, action items, or backlog entries; or mentions value-stream prioritization, A3 / lean countermeasure ranking, ROI vs. effort scoring, or building a remediation / improvement backlog. Works with GHQR findings, audit results, retrospective action items, risk registers, architecture review gaps, or any free-form `{impediment, countermeasure}` list.

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Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

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Improve AI application with evaluation-driven development. Define eval criteria, instrument the application, build golden datasets, observe and evaluate application runs, analyze results, and produce a concrete action plan for improvements. ALWAYS USE THIS SKILL when the user asks to set up QA, add tests, add evals, evaluate, benchmark, fix wrong behaviors, improve quality, or do quality assurance for any Python project that calls an LLM model.

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Durable categorized record-keeping for any predicate-enabled walk. Trigger when: - Classifying a step's output for recording (decision, finding, debt, feedback). - Promoting a scratch artifact to the ledger, to repo/docs, or to a category home (.ledger/tech-debt/, .ledger/process-feedback/). - Auditing whether a draft is structurally ready for its destination. - Running a zero-context sensibility read before committing to promotion. - Recording a known limitation as a tech-debt entry (not prose in the log). - Recording a friction, miss, improvement, or process amendment. - Prompt contains: /record, record-keeping, promote, ledger note, zettelkasten, scratch-to-ledger, zero-context read, over-strip, classification trigger, tech-debt, process-feedback, amendment.

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Bounded improvement loop: one artifact, fixed eval score, keep an edit only if strictly better. Use for AR_DESIGN/AR_FUNCTION/AR_ACCEPTANCE stages or standalone bounded iteration.

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Comprehensive Lean Six Sigma consulting skill supporting all belt levels (White Belt to Master Black Belt). Use this skill for DMAIC/DMADV project execution, Lean waste elimination (VSM, 8 Wastes/DOWNTIME, 5S), statistical analysis (process capability Cp/Cpk, control charts, hypothesis testing), and Six Sigma training/education. Triggers include "improve process", "reduce defects", "sigma level", "DMAIC project", "value stream mapping", "Kaizen", "process capability", "control chart", "root cause analysis", "5 Whys", "Fishbone diagram", "FMEA", "DOE", or requests involving process improvement methodologies.

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This skill should be used when analyzing bug ticket data to identify quality trends, assess software quality status, and provide improvement recommendations. Use when a user provides bug lists (CSV, Excel, JSON, or Markdown format) and needs comprehensive quality analysis from a QA manager perspective. Generates statistical analysis, identifies quality hotspots, provides actionable recommendations, and produces professional Markdown reports suitable for project managers and executive leadership. Ideal for IT system development, migration, and implementation projects.

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Generate audit-ready internal control design documents from As-Is business process inventories. Produces control IDs, assertion mappings, procedures, SoD analysis, KPIs, materiality thresholds, and implementation roadmaps. Use when building internal controls for new audit engagements, SOX/J-SOX compliance, or process improvement initiatives.

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Review skills in any project using a dual-axis method: (1) deterministic code-based checks (structure, scripts, tests, execution safety) and (2) LLM deep review findings. Use when you need reproducible quality scoring for `skills/*/SKILL.md`, want to gate merges with a score threshold (for example 90+), or need concrete improvement items for low-scoring skills. Works across projects via --project-root.

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Analyze and optimize CLAUDE.md files for quality improvements and token reduction. Supports analysis-only, simplification-only, or full optimization modes. Use when asked to "review", "optimize", "simplify", "improve", "check", or "reduce tokens" in CLAUDE.md.

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Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code spe...

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Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.

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TLS 1.3 (RFC 8446) is the latest version of the Transport Layer Security protocol, providing significant improvements over TLS 1.2 in both security and performance. It reduces handshake latency to 1-R

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Review code for potential issues and improvements. Use when asked to review specific files, functions, or code sections.

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