「findings」の検索結果

Record findings from completed offline human tasks (interviews, observations, outreach) back into the canvas. The re-entry point after /mycelium:handoff.

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。

Synthesizes exploration findings into unified analysis with deep investigation capabilities (shell commands, git history, dependency analysis) and completeness evaluation (converted from agent)

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。

Generates MkDocs-flavored or standard GitHub-flavored Markdown documentation from codebase analysis findings including API references, architecture guides, standalone markdown files, and change summaries (converted from agent)

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。

Reviews code implementations for correctness, security, maintainability with confidence-scored findings (converted from agent)

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。

Designs implementation blueprints for features using exploration findings and architectural best practices (converted from agent)

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。

Synthesizes exploration findings into unified analysis with deep investigation capabilities and completeness evaluation (converted from agent)

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。

Structures comprehensive buy-side diligence across financial, legal, commercial, and operational workstreams. Use when coordinating DD processes, building diligence checklists, or synthesizing DD findings.

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。

Protocol for task synchronization, context handoff, and cross-session coordination using Claude Code task tools. Ensures agents properly update tasks with findings and enables seamless work continuation.

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。

Manus-style file-based planning for complex tasks. Use task_plan.md, findings.md, and progress.md to maintain persistent context. Use for multi-step tasks, research, or work spanning many tool calls.

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。

Verify fix commits address security findings without introducing new bugs or regressions. Analyzes diffs for anti-patterns like removed validation, weakened access control, reduced error handling, reordered external calls, and changed integer operations. Generates structured FIX_REVIEW_REPORT with finding status tracking.

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。

Benchmark external agent frameworks, auto-detect source type, scan for prompt injection, and convert findings into a concrete TDD upgrade backlog for agent-studio evolution.

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。

Two-step self-review at every task lifecycle phase. Step 1 (this skill) runs in-context to gather session signals — files read vs grepped, user pushback, build evidence, degradation signs. Step 2 launches a forked subagent for unbiased code/plan review with fresh eyes. Combined report classifies findings as hard blocks or soft warnings.

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。

Expert PE review of a submittal (shop drawings, product data, samples, equipment brochures) against specs, drawings, code questions and trade coordination. Produces findings with both sources, trade routing and a completeness gate. Triggers: 'review submittal', 'review shop drawings'.

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。

Act on the findings a review left on a PR or MR: take or decline each by severity, edit the code, one commit, reply. Use when handed a PR or MR URL that has already been reviewed. Edits real code.

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 technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants measurable accessibility, performance, responsive, theming, or anti-pattern findings—not when they mainly want an overall UX critique or final visual/detail polish.

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。

Rewrite text so it reads like a real person wrote it, not an AI. Auto-detects Russian vs English and applies the matching rule set. Use when the user asks to humanize text, remove the "AI smell", de-AI, or make AI output sound natural - especially for Russian. Russian requests count too - очеловечь, перепиши как человек, оживи текст, убери запах ИИ, сделай живее, звучит как робот, убери канцелярит. Also use when asked whether a text was written by AI - нейросеть писала?, похоже на ChatGPT?, видно ли, что это ИИ, человек или бот писал, did AI write this? - then check and report findings without rewriting. While installed, also keep your own Russian and English writing free of the quick-list stock phrases, silently; never edit the user's text unless asked.

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。

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls. Now with automatic session recovery after /clear.

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。

Search academic papers, build literature reviews, and synthesize research findings — combines Exa MCP (research_paper category, arxiv filtering) with arxiv-mcp-server for paper discovery, download, and deep analysis. Triggers on academic paper, literature review, research synthesis, arxiv, find papers, scholarly search.

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。

Designs Claude Code prompts and CC environments. Produces CLAUDE.md drafts, agent topologies, scope-authorization frameworks, halt triggers, Skills/hooks/MCP plans, chat-to-CC handoff specs, and EXECUTION_PLAN.md remediation plans. Five modes: DESIGN (new CC deployments), EVOLVE (updates from friction), RESEARCH (evaluate a CC tool/pattern), TEMPLATE (reusable artifacts), REMEDIATE (consume hygiene findings → produce + execute plan). Do NOT use REMEDIATE for direct cleanup (Cat 1–10 — use rootnode-repo-hygiene Phase 2). Do NOT use for hygiene scanning (rootnode-repo-hygiene), chat prompts (rootnode-prompt-validation), or chat Projects (rootnode-project-audit).

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。

Audits and scores Claude Projects using a six-dimension Scorecard with anchored 1-5 rubrics and detects seven structural anti-patterns. When global layer info is provided, adds cross-layer alignment findings. Use when user says "audit my project," "review my custom instructions," "score my project," "evaluate my Claude project," "improve my system prompt," "what's wrong with my project," "why is my project underperforming." Also trigger on symptom-phrased: "my project doesn't work right," "Claude is inconsistent in my project," "my Project used to work and now doesn't." Also use when the user pastes Custom Instructions asking why output is poor or generic. Do NOT use when the user's primary request is a global-layer audit (use rootnode-global-audit if available), single-prompt evaluation (use rootnode-prompt-validation if available), or Memory-only optimization (use rootnode-memory-optimization if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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。

Audits Claude Code repositories. Two-phase workflow — Phase 1 sweeps 14 hygiene categories (permissions, hooks, CLAUDE.md bloat, Skills hygiene, process-abstraction, plus structural) and an optional 7-layer leak check. Phase 2 executes [APPROVED] markers from the report file. Use for repo cleanup, pre-distribution sanity, post-feature hygiene, environment audit, process-abstraction discovery. Trigger on "audit my CC repo," "sweep my repository for hygiene," "check this CC environment," "find process abstraction candidates," "review my CLAUDE.md," "clean up my .claude directory," "pre-distribution hygiene check," "find layer leaks in my CC setup." Routes structural findings to rootnode-cc-design REMEDIATE. Composes with rootnode-critic-gate. CC-only — does not scan Claude Projects. Do NOT use for building a Skill (use rootnode-skill-builder), compiling a prompt (use rootnode-prompt-compilation), scoring a single prompt (use rootnode-prompt-validation), or auditing a Project (use rootnode-project-audit).

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。

Multi-phase deep codebase review with severity ratings and structured output. Covers architecture, security, code quality, and UX/accessibility in a single coordinated pass. Produces a prioritized findings table suitable for direct conversion to GitHub issues via the create-github-issues skill.

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。

Redact rendered visual artifacts — Mermaid diagrams, SVGs, and HTML dashboards — before public disclosure, using an ordered longest-first mapping table (specific label, then namespace label, then catch-all placeholder) that swaps sensitive identifiers for descriptive stand-ins while preserving structure, node ids, and teaching value. Covers whole-word identifier replacement, node-label-vs-node-id discrimination, re-rendering the derived image from the redacted source, and verifying the result through the redaction gate. Use when a diagram or dashboard built from internal findings must ship to a public mirror without leaking the names behind it.

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。

Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring 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。