調査、論文、教育、資格学習（4,981 件）
Manage project learnings. Review, search, prune, and export what gstack has learned across sessions. Use when asked to "what have we learned", "show learnings", "prune stale learnings", or "export learnings". Proactively suggest when the user asks about past patterns or wonders "didn't we fix this before?"
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。
Creates professional PDF whitepapers from Markdown files. Use when the user wants to create a whitepaper, technical document, or PDF with professional formatting and typography.
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。
Orchestrate continual learning by delegating transcript mining and AGENTS.md updates to `agents-memory-updater`.
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。
Build a personalized learning roadmap with milestones and practice checkpoints
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Evaluate learning progress, identify blockers, and adjust the learning plan
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-agent exploration and task delegation for complex areas of interest. Use when you need to deeply explore a codebase area, gather information from multiple perspectives, or delegate parallel research tasks for comprehensive analysis.
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。
Capture durable project memory from work, debates, debugging discoveries, learned conventions, deferred options, and session outcomes. Load when the user says remember this, save this learning, record what happened, update project memory, or preserve context for future agents.
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。
Research a skill domain before building or improving a skill. Searches academic papers, practitioner blogs, and GitHub skill repos in parallel to find current best practices, domain gotchas, and existing skill patterns. Called by universal-skill-creator and improve-skills before writing any skill. Also load directly when the user asks to research a domain for a skill, find existing skills on a topic, discover best practices for a skill, check what research exists before building an agent skill, or says "what does current research say about", "find best practices for".
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。
Extract actionable patterns, architecture decisions, code conventions, and skill-relevant insights from GitHub/GitLab repositories. Assess repo credibility, run the full security scan pipeline, and apply findings to existing skills, new skills, or the current project. Load when the user asks to learn from a repo, extract patterns from a codebase, study a repository, or analyze a repo for reusable techniques. Also triggers on "learn from this repo", "learn from this repository", "what can we learn from this codebase", "extract patterns from this repo", "study this 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。
Promote project-specific memories into strict, small global memory only when they are cross-project, stable, useful, safe, and worth the global context cost. Load when the user says make this global, remember across projects, save this globally, or promote this learning.
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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).
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。
Orchestrator for the learn-from suite - auto-detects source type (academic paper, GitHub repo, blog/web article, or in-conversation learnings) and routes to the correct sub-skill for credibility check, security scan, insight extraction, and application. Load when the user says "learn from", "learn from this", "extract insights from", "apply learnings from", "what can we learn from", or provides a URL, file path, or pasted content that should be ingested as knowledge. Single entry point for all learning workflows.
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。
Extract actionable skill knowledge from academic papers and research articles, assess credibility, run security checks, and either improve existing skills or create new ones. Load when the user asks to learn from a paper, extract insights from a research paper, turn a paper into a skill, apply paper findings to skills, read this paper and improve my skills, or process a research article. Also triggers on "skill from paper", "learn from this paper", "paper to skill", "extract from this research", "apply this paper", or when the user uploads or links to an academic PDF or paper URL.
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。
Capture actionable learnings that emerge during conversation — when the agent or user discovers that a skill, a set of skills, or a process needs to be updated based on what's happening in the current chat. Sub-skill of the learn-from orchestrator. Load when the user says "we should update the skill for this", "this should be a skill rule", "add this as a gotcha", "the skill should know about this", "update the process for this", "remember this for next time", "this is important for the skill". Also triggers when the agent notices a skill's guidance was wrong or incomplete, a process step failed or was unnecessary, a new pattern emerged, a guardrail was missing, a workaround became a pattern, or a debugging session reveals a gap.
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。
Extract actionable insights from blog posts, web articles, and practitioner content - assess credibility, run security checks, and either improve existing skills or apply to the current project. Load when the user asks to learn from an article, extract insights from a blog post, apply a practitioner's findings, or process engineering blog content. Also triggers on "learn from this article", "learn from this blog post", "extract insights from this post", "what can we learn from this article", "apply this article", or when the user links to a blog, Medium, Substack, dev.to, or engineering blog post.
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。
Apply validated research paper insights to the current project codebase — improving architecture, code patterns, testing strategies, documentation, or workflows based on empirical findings. Load when learn-from-paper routes insights to the current project, or when the user asks to apply paper findings to this project, improve this codebase with research, use this paper to improve my project, or apply research to my code. Also triggers on "apply this to my project", "how can this paper help my codebase", "use these findings here", "implement paper recommendations", "use research to improve my code". Always called AFTER learn-from-paper has completed credibility and security checks — never ingests papers directly.
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。
Gracefully retire a skill that is redundant, superseded, or no longer earning its place in the context window. Load when improve-skills finds a skill scoring 0-5/14 AND research confirms the domain is now handled natively by current models, when two skills have overlapping triggers and one subsumes the other, when the user asks to remove a skill, retire a skill, delete a skill, or clean up redundant skills, or when validate-skills flags a skill as a duplicate trigger risk. Handles removal cleanly: updates all callers, removes from AGENTS.md, updates README, and archives rather than deletes so the skill can be recovered if needed.
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。
Machine Learning integration for Flutter using TensorFlow Lite and Firebase ML
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 a researcher asks where to start, which project skill fits the current stage of a study, or in what order literature scoping, source access, vault upkeep, drafting, citation verification, disclosure, and review response should run.
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Use when choosing or configuring MCP servers for research work, when a literature, reference manager, or scholarly search server needs a trust check before installation, or when an existing research tool stack needs a data flow and permission audit.
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Use when a research proposal needs work packages, milestones, deliverables, a timeline, a risk register, and budget-justification scaffolding, while keeping scientific claims and feasibility honest and human-owned.
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Use when a reference list needs APA 7 cleanup, when DOIs must be confirmed real against Crossref or PubMed, when the user asks whether AI-suggested citations actually exist, or when a manuscript needs a citation hygiene pass before submission or defense.
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Use before a tag, GitHub release, or Zenodo archive, when README, CATALOG, CHANGELOG, CITATION.cff, the disclosure aggregate, and skill discovery must agree, or when a go or no-go release verdict needs recorded evidence 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。
Use when a high-stakes analysis, citation set, or decision should be checked by a second independent AI agent that verifies rather than decides, or when disagreement between agents must be surfaced for the human to adjudicate.
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。