'improvement' 검색 결과

Analyze sprint history to forecast delivery ranges, score team health, and track retrospective actions. Python tools turn structured sprint data into reports that support planning, highlight recurring problems, and show whether improvement efforts are progressing.

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• Forecasting sprint workload from past delivery data
• Assessing team health and prioritizing improvements
• Tracking retrospective actions and recurring themes

Review a sprint or milestone by comparing planned work with actual results. Summarize completion, estimation gaps, and blockers, then produce a retrospective with specific improvement actions, owners, and deadlines for the next iteration.

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• Reviewing completed work at the end of a sprint
• Investigating estimation gaps and carried-over tasks
• Checking progress on previous improvement actions

Generate improvement ideas grounded in the project or topic, critique every candidate, and rank the survivors with explanations. Produce a document that helps users choose which ideas deserve further exploration before defining requirements.

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• Finding improvement ideas based on the current project
• Exploring unexpected directions for a topic
• Comparing candidates and understanding rejection reasons

Review past dsh agent sessions to identify recurring tasks and test proposed skill or memory improvements. This skill guides preview runs, validation on reserved tasks, staged proposals, adoption with backups, and scheduled nightly cycles.

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• Extract recurring tasks from past agent sessions
• Test proposed skill and memory improvements
• Review staged changes before adopting them

Improve a compiled paper through two rounds of external model review, source edits, and PDF rebuilding. Check claims against evidence, resolve inconsistencies, and refine figures and layout, with optional checkpoints and restrictions on which edits are allowed.

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• Revising a generated paper before submission
• Correcting mismatches between claims and evidence
• Improving notation, figures, tables, and layout

Runs repeated reviews and fixes on a Claude Code skill until no critical or major findings remain. Tracks findings across rounds, checks change boundaries, and stops for a design decision when fixes stop making progress.

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• Refining a skill until critical and major findings are resolved
• Improving skill metadata, trigger descriptions, and references
• Tracking recurring findings across review rounds

Run the Part 12 continuous improvement loop into a quarterly brief for leadership. "produce the quarterly improvement brief"

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Conduct a project or sprint retrospective by gathering data from status reports and velocity metrics, structuring what went well and what needs improvement, and generating actionable improvement items with owners and due dates. Use at the end of a sprint, after a project phase or milestone, following a significant incident or success, at a quarterly review of ongoing processes, or before starting a similar project to capture lessons learned.

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Scaffold and run Karpathy-style autoresearch loops in any git repo. This skill should be used when setting up autonomous code improvement, generating adversarial eval harnesses, running hypothesis-implement-eval-keep/discard loops, or checking autoresearch progress. Triggers on "autoresearch", "autonomous improvement", "eval loop", "hypothesis loop", "self-improvement loop".

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Define a Lean Six Sigma improvement project using DMAIC, problem framing, baseline measures, root-cause hypotheses, CTQs, and control planning. Use when an existing process is underperforming and the team needs a charter, SIPOC, baseline, and improvement path. Do not use for brand-new process design or vague complaints with no process boundary.

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Synthesizes actionable feedback from validation results, confidence scores, and iteration triggers. Creates structured improvement guidance for re-execution. Activate on 'synthesize feedback', 'improvement suggestions', 'actionable feedback', 'iteration guidance', 'feedback generation'. NOT for iteration detection (use dag-iteration-detector) or convergence tracking (use dag-convergence-monitor).

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Guides continuous improvement and change management for SMEs adopting AI. Applies Marginal Gains philosophy, Kaizen principles, and structured sprint methodologies to organizational transformation. Use when user asks about change management, continuous improvement, AI adoption strategy, team resistance, transformation roadmaps, or improvement sprints.

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Capture and develop a reusable skill, tool repair, or behavior improvement noticed while completing a task. Use when instructions are stale, a recurring workflow deserves a skill, or a tool contract fails in practice.

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River Review のメインエントリポイント。 レビュー依頼の intent classification → 専門 skill 選択 → 実行 → finding verification → feedback classification → fixture / reference / suppression への還元までを束ねる improvement-loop orchestrator。

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Apply continuous improvement principles to iteratively enhance processes, code quality, team practices, and system performance through small, incremental changes. Use when improving development processes, optimizing workflows, reducing waste, enhancing code quality over time, implementing retrospective learnings, or fostering a culture of continuous improvement.

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Design a self-improvement loop for a shipped product's AI agents — production traces feed evals, evals feed improvement proposals (prompts, playbooks, retrieval configs), and a human approval gate promotes changes with rollback. Technique-agnostic: chooses per project between ACE-style evolving playbooks, GEPA/MIPROv2 offline optimization, or simple eval-driven iteration via references/techniques.md. Load when the user asks to make my product's agents self-improving, learn from production traces, add a learning loop, evolve prompts or playbooks safely, promote agent improvements, or GEPA-style optimization. NOT harness-evolution (that improves the coding agent), NOT experimentation (product A/B tests), NOT agent-run-retro (dev-phase manual retros — this skill is the production-scale continuation).

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OpenClaw를 실행하는 기기나 서버의 보안 상태를 점검합니다. SSH, 방화벽, 업데이트, 백업, 암호화 상태를 확인하고 우선순위에 따른 단계별 개선 계획을 제안합니다.

• OpenClaw 실행 환경 보안 점검
• 서버의 외부 노출 상태 확인
• 백업 및 디스크 암호화 상태 확인

Collect comments marking deferred improvements into a ledger grouped by file. Record what was simplified, its stated limit, and when to revisit it, while flagging entries that lack a trigger for future action.

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• Collect deferred improvements across a repository
• Find shortcuts with no stated review trigger
• Gather TODO comments using a custom marker

Create engineering retrospectives from Git history, covering delivered changes, work patterns, test code ratios, and frequently changed files. Review individual contributions and improvement opportunities, save metrics, and compare results with earlier periods.

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• Summarizing a week of engineering work
• Reviewing individual contributions and improvement opportunities
• Checking test ratios and frequently changed files

Turn interviews, support tickets, surveys, and product context into a single HTML workspace for decisions. Link findings to evidence, compare improvement options, show uncertainty, and outline experiments that help teams choose what to try next.

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• Combining interviews and support tickets into findings
• Comparing onboarding improvement options
• Sharing evidence and uncertainty with stakeholders

Develop campaign concepts and guide design work from the brief to presentation. Combine structured idea generation, repeated self-assessment, and available design resources to define a direction, critique work, and plan improvements.

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• Developing a campaign concept
• Defining a page's visual direction
• Reviewing a design and planning improvements

Turn software development goals into manageable milestones with clear deliverables, dependencies, effort estimates, and success criteria. Use it to plan feature implementation, bug fixes, refactoring, performance improvements, and testing work.

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• Planning OAuth2 authentication implementation
• Setting measurable database performance targets
• Breaking refactoring into incremental steps

Build agents that record task outcomes and use past experience to choose future approaches. Implement pattern matching, strategy comparisons, persistent learning records, and metrics for checking how recommendations and results change over time.

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• Build an agent that learns from task outcomes
• Compare approaches to code review or bug fixing
• Find learned patterns relevant to the current situation

Measure page loading speed and identify what prevents a page from loading in under three seconds. Use performance measurements to guide improvements such as lazy loading, caching, CDN delivery, and reducing resource costs.

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• Investigating slow page loads
• Finding heavy resources and costly rendering steps
• Checking performance on a throttled 3G connection