「improvement」の検索結果

Run the improvement loop, led by the solve-wall share and the follow-up of each earned fail, with signal a rare confirming event: find the one link holding the climb, choose one simple domain-agnostic change against it, measure it as a matched arm, freeze a prediction, launch, read recorded bytes, decide the next move. Owns experiment selection and evidence reading; the product controller owns build, measure, climb, rebuild, claim and promotion. Also the Super Loop's Meta Agent: a second session that audits and steers the session driving the loop so it keeps moving (references/meta-agent.md).

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Self-improvement loop for multi-agent workflows. Diagnose failures, improve tool descriptions, and learn from success/failure patterns.

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。

Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.

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Assesses operational improvement opportunities with cost rationalization, revenue stabilization, and management changes for distressed businesses. Use when evaluating turnaround plans, identifying operational improvements, or assessing management capability.

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Structures 100-day and long-term value creation plans with revenue growth, margin improvement, and capital efficiency initiatives. Use when building value creation plans, tracking improvement initiatives, or preparing operating partner reviews.

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Map existing business processes using BPMN 2.0 notation or simplified swimlane diagrams. Identify waste, handoffs, decision points, and improvement opportunities using Lean principles (8 Wastes). Use when documenting workflows for SOP creation, automation assessment, or process improvement. Do not use for software architecture diagrams — use architecture tools instead.

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Fierce autonomous internal co-founder. Bounded web research for evidence-backed improvements. Structured idea formulation with self-critique filter that rejects ideas not serving the goal. Auto-implements high-confidence aligned improvements, proposes medium-confidence ideas for approval. Evaluates viral coefficient, AI search visibility (GEO), and solo SaaS economics. Considers higher pursuits: employing disabled people, spiritual tech investigation, 99% wealth donation ethos.

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Iterative manuscript improvement workflow where a reviewer agent critiques, an author agent revises, a verifier checks the revision, and the loop repeats until acceptance or a human-pause condition is reached. Use for virtual peer review, reviewer-author revision loops, rebuttal preparation, resubmission polishing, journal paper improvement, response-to-reviewers drafting, and deciding when additional data, experiments, theory, modeling, or author judgment is required before further revision.

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Turns a single feature idea, improvement, or bug into ONE well-structured GitHub issue. TRIGGER when: the user wants to file/open/create an issue, turn a feature idea or improvement into a ticket, or capture something missing or broken as a ticket. DO NOT TRIGGER when: breaking work into multiple issues or planning a body of work → a planning skill; writing a full Product Requirements Document → creating-prd; the idea is still fuzzy and unhardened → grilling-ideas first.

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。

A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution. Communicates with EvoMap Hub via local Proxy mailbox.

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Full design improvement pipeline — syncs your app to Google Stitch, generates alternatives, presents side-by-side comparison, and adopts cherry-picked improvements. Triggers: 'stitch pipeline', 'improve my design with stitch', 'make my app look better', 'stitch redesign everything'.

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Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

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Autonomous iterative refactoring workflow. Scans for tactical code quality improvements (DRY, dead code, naming, complexity), implements through SMEs, verifies with QA, commits atomically, and loops until no improvements remain.

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。

Deliberate system-improvement loop — audit your own Claude Code setup (rules / memory / skills / MCP / agents / registry), evaluate incoming tools via /vet, adopt the worthwhile ones with discipline (backup → verify), and record every decision. Use when bringing in a tool/idea/collection to improve the setup, or when doing a tooling-improvement pass. Triggers — "upgrade the workflow", "audit our setup", "let's improve the tooling", "evaluate this for us", when handed a post/repo/reel with a tool.

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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 speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

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。

Application of Six Sigma quality methodology to AI agent process improvement and reliability

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Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

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。

Turns a single feature idea, improvement, or bug into ONE well-structured GitHub issue. TRIGGER when: the user wants to file/open/create an issue, turn a feature idea or improvement into a ticket, or capture something missing or broken as a ticket. DO NOT TRIGGER when: breaking work into multiple issues or planning a body of work → a planning skill; writing a full Product Requirements Document → creating-prd; the idea is still fuzzy and unhardened → grilling-ideas first.

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。

Run the skill-improvement pipeline after a learning was detected — capture, classify, create, validate, apply; explicit request only.

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。

Refine a persona from recent corrections — tightens its Unique Questions, governance-gated; explicit request only. Skill analog → skill-improvement-pipeline.

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。

AID Memory System for session feedback, learning cycles, and skill improvement. Manages state, feedback collection, and improvement analysis. Load for /aid-init, /aid-start, /aid-status, /aid-end, /aid-improve, /aid-memory, /aid-reset.

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。

Builds SOC performance metrics and KPI tracking dashboards measuring Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), alert quality ratios, analyst productivity, and detection coverage using SIEM data. Use when SOC leadership needs operational visibility, continuous improvement tracking, or executive-level reporting on security operations effectiveness.

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。

インシデント発生後の振り返りと根本原因分析を体系的に実施するスキル。 ログファイル解析や技術デバッグが必要な場合は log-debugger を使用すること。 本スキルはインシデント管理プロセス（タイムライン構築、影響評価、 是正措置計画、再発防止策）に特化する。ログデータの分析ではなく、 組織的な振り返りプロセスと是正措置の策定が主目的である。 Use when conducting post-incident reviews without log analysis, creating corrective action plans, or performing organizational root cause analysis focused on process improvement. NOT for log file debugging. Triggers: "post-incident review", "corrective action plan", "incident report作成", "再発防止策", "RCAレポート", "是正措置", "インシデント振り返り"

エラーや例外シナリオを顧客体験(CX)の観点で体系的に分析し、改善優先度を付ける スキル。6軸評価（影響度・頻度・復旧容易性・メッセージ品質・感情的影響・ ビジネスコスト）でスコアリングし、Impact vs Effort マトリクスで改善施策を 優先順位付けする。Use when analyzing error scenarios from customer experience perspective, evaluating error message quality, prioritizing error UX improvements, or creating CX-focused error analysis reports. Triggers: "CXエラー分析", "error experience", "エラーUX改善", "customer experience error", "エラーメッセージ品質", "error scenario analysis", "顧客体験エラー評価"