「bloat」の検索結果

Step-by-step workflow for converting bloated command files to lean declarative definitions

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。

Audit token consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations. Use when the context window is filling up too fast or before adding new components.

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。

Explores codebase structure, affected files, blast radius, related tests, and local conventions for a focused topic. Use when review or planning needs structural lookup without bloating main context.

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。

Inspect LiveView socket assigns for memory bloat — missing; Use when LiveView memory grows or you need to add…

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。

Inspect LiveView socket assigns for memory bloat — missing temporary_assigns, unused assigns, unbounded lists needing streams, memory estimates. Use when LiveView memory grows or you need to add temporary_assigns.

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。

Inspect LiveView socket assigns for memory bloat — missing temporary_assigns, unused assigns, unbounded lists needing streams, memory estimates. Use when LiveView memory grows or you need to add temporary_assigns.

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。

Post-implementation cleanup that removes AI-generated bloat while preserving functionality. Runs pass-by-pass with test verification after each pass. Activate after kraken/spark complete a feature, or when a codebase needs hygiene work.

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。

Lazy senior dev mode: the smallest change that fully solves the task, and a reply a busy human understands in one read. Use on any coding task (writing, fixing, refactoring, reviewing, choosing dependencies) and when the user says "ponytail", "be lazy", "simplest solution", "yagni", or complains about over-engineering or bloat. Levels: lite, full (default), ultra.

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。

Remove AI-generated code slop from branches. Use after AI-assisted coding sessions to clean up defensive bloat, unnecessary comments, type casts, and style inconsistencies. Focuses on identifying and removing AI artifacts that degrade code quality.

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。

Prune bloated session with a prescription. Removes progress ticks, stale reads, duplicate content, and more.

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。

Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

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。

Audit the instructions an agent already carries — CLAUDE.md, AGENTS.md, skills, tool descriptions — for contradictions, over-constraint, and duplication, then propose a cut list. Use when an agent ignores its own instructions, when a CLAUDE.md has grown bloated, or when the user asks to audit or rightsize their agent context.

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。

Updates documentation after code changes with quality gates, slop detection, and accuracy checks. Use when code changes require corresponding doc updates.

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。

Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.

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。

Orchestrates the QUALITY pipeline stage for egregore work items, running code review, unbloat, and test updates. Use when running quality checks before a PR.

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。

Compresses verbose responses by removing filler and framing to save 200-400 tokens. Use when responses feel bloated or context is filling fast.

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。

Delegate search- and review-heavy work to Honey's read-only subagents so large reads return compressed instead of bloating context.

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。

Review a diff for what Honey would cut — over-engineering (speculative generality, hand-rolled stdlib, single-caller abstractions) and over-verbosity (dead code, narration, redundant comments). Outputs a terse delete-list, one line per finding, with the line count each cut saves. Use when asked to review, trim, or de-bloat a change, or before opening a PR. Quality lever only — it finds waste, not correctness bugs.

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。

Safely diagnose and improve local network speed, latency, jitter, DNS, Wi-Fi, Ethernet, macOS network services, and bufferbloat. Use this skill whenever the user asks to optimize internet/network speed, make the network faster, diagnose slow Wi-Fi, latency, packet loss, DNS delay, unstable Codex/AI tool connectivity, or asks about the viral Codex network optimization workflow. Always protect VPN/proxy tools such as Clash Verge, Mihomo, Shadowrocket, Tailscale, V2Ray, Surge, and corporate VPNs; do not modify or disable them unless the user explicitly asks for that specific change.

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。

Use when Codex feels slow, bloated, or heavy after heavy use. Provides safe session hygiene, archive strategy, and handoff discipline to keep daily Codex usage responsive and low-friction. Always pairs with comprehensive handoffs before archiving active work.

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。

Manage MCP servers - discover, analyze, execute tools/prompts/resources. Use for MCP integrations, capability discovery, tool filtering, programmatic execution, or encountering context bloat, server configuration, tool execution errors.

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。

Dynamic MCP server discovery and code-mode execution via central registry. Use for multiple MCP integrations, tool discovery, progressive disclosure, or encountering MCP context bloat, changing server sets, large tool sets.

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Structured logging with proper levels, context, PII handling, centralized aggregation. Use for application logging, log management integration, distributed tracing, or encountering log bloat, PII exposure, missing context errors.

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。

Report which kasetto-installed skills and MCP servers are actually being used across the AI agents on this machine, and render a branded HTML dashboard of the result. Use whenever the user asks what agent assets they actually use, which skills or MCPs are dead weight, what to prune or clean up from kasetto.yaml, why their context is bloated with unused MCP servers, whether a skill has ever been invoked, or wants a usage report, audit, or dashboard of their agent setup. Also trigger on "kst ai assets usage", "ai asset usage", "what skills do I actually use", "which MCPs are worth keeping", "audit my agent assets", "am I using all these skills", or any request to review, trim, or justify an installed agent toolkit.

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。