Results for “long context”

Fetch a URL and save the body to a temp file so you can pipe it through rg / jq / awk instead of dumping the whole page into the conversation. Use this whenever a user gives you a URL to read - articles, docs, PRs, gists, README files, blog posts, anything HTTP. Returns a path on disk; the agent then greps the path for the relevant section and quotes only what matters. Strongly preferred over the built-in webfetch tool when (a) the page is more than a few KB, (b) the user wants to find a specific phrase / table / code block on the page, (c) you need to keep the long page out of context, or (d) you want to inspect raw HTML alongside rendered markdown. Triggers: 'fetch', 'fetch this URL', 'fetch and save', 'fetch the page', 'pull this', 'grab this article', 'read this URL', 'read the docs at', 'mirror this page', 'page is too long', 'save the page so I can grep it', 'put it on disk so I can rg', 'cache this URL'.

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Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.

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Build agent memory with Pinecone by storing searchable representations of conversations and documents. Retrieve relevant past context for answers, organize memory by session or user, and prototype semantic search that finds information by meaning.

• Building agent memory that persists across sessions
• Adding retrieved context to agent answers
• Organizing memory separately for each user

Delegate code lookup, small edits, and diff reviews to three specialized subagents. Their compact, structured results help conserve the main agent's context, with guidance on when to delegate and when to keep work in the main conversation.

• Locating definitions and callers
• Making a focused edit across one or two files
• Reviewing a diff for bugs

Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.

• Add conversation history to a chat system
• Remember user preferences across sessions
• Reuse patterns from successful interactions

Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.

• Add conversation history to a chat system
• Remember user preferences across sessions
• Reuse patterns from successful interactions

Design how an AI agent trims long conversations and large tool outputs while retaining decisions, constraints, failed approaches, and sources. Compare summarization strategies, choose compression triggers, and consider separate subagents to keep intermediate information out of the main conversation.

• Managing large search results and tool outputs
• Preserving decisions and constraints during summarization
• Choosing compression triggers and batch processing

Save facts, decisions, preferences, and task outcomes to Hindsight long-term memory. The skill turns what was learned into short notes with context and reasons, choosing either a Bot’s own memory store or a shared store.

• Saving a completed task’s outcome and reasoning
• Remembering a user’s stated preferences
• Recording decisions for future work

Add interactive panels to an MCP server so users can select records, view charts, or follow progress inside a conversation. The skill guides tool connections, interface implementation, sandbox restrictions, and testing in hosts that support these panels.

• Build a searchable contact or file picker
• Display charts and previews inside a conversation
• Show live progress for a long-running job

Make focused interface changes from screenshots, annotations, or visually selected elements. Helps locate the responsible component and apply small style or markup edits while preserving existing behavior, accessibility features, and surrounding design.

• Adjust a button identified in a screenshot
• Change only one selected card
• Apply interface fixes from visual annotations

Creates a compact handoff from verified work status, including commits, branches, automated checks, blockers, decisions, and the next action. Saves a durable record so another session or the user can resume without reconstructing the conversation.

• Handing off work before ending a session
• Moving ongoing work into a fresh conversation
• Saving a checkpoint during a long operation

Design memory systems that help AI agents retrieve relevant past information. Choose short-term and long-term memory structures, compare vector storage options, and review document splitting and retrieval strategies for different kinds of knowledge and experience.

• Design memory for information used across conversations
• Choose storage for experiences, facts, and procedures
• Review retrieval when an agent misses saved information

Run a saved weekly or monthly report and combine its results with a brief business snapshot. Deliver one briefing with an XLSX workbook, using a connected data source or uploaded files, and reuse the definition for scheduled runs.

• Receive the same weekly or monthly report on schedule
• Review report figures alongside cash and sales context
• Rerun a saved report from CSV or XLSX data

Compare assigned work from connected trackers with TASKS.md, review stale tasks, and fill gaps in stored context about people and projects. An optional comprehensive scan finds possible missed commitments in recent activity, with changes confirmed by the user.

• Review assigned tracker tasks against TASKS.md
• Triage overdue tasks and long-standing active items
• Fill missing context about people, projects, and acronyms

List Git commits linked to agent work sessions, with branch or repository filters. See each commit’s message, date, linked sessions, and observation counts to review recorded changes alongside their session context.

• Review recent commits linked to agent sessions
• Filter linked commits by branch
• Review linked commits for a specific repository

Keep plans, research findings, and progress in Markdown files for complex agent work. Separate task plans and automatic planning reminders help the agent recover saved context, track decisions, and identify the next action.

• Resuming long research tasks
• Tracking phases and the next action
• Separating plans for concurrent tasks

Document a technical decision in an architecture decision record, or ADR. Capture its context, alternatives, and consequences, assign a record number, and distinguish a proposed decision from an accepted one so dependent work reflects its actual status.

• Recording the reasons behind a technical choice
• Documenting alternatives and consequences of a design decision
• Creating numbered records alongside existing architecture decisions

Explore how coordinating agents can pass relevant working state to workers through KV cache, a model's internal attention memory. Covers task-specific selection, runtime requirements, and comparisons of accuracy, token use, and processing overhead.

• Reducing repeated history sent to worker agents
• Comparing internal memory sharing with text summaries
• Assessing runtime support for KV cache compaction

Use Memori through MCP, a protocol connecting agents to tools, to retrieve past decisions and preferences, summarize ongoing work, and resume after conversation compression. It also guides selective long-term memory storage and usage limit checks.

• Retrieve decisions and preferences from earlier sessions
• Review current work when starting a session
• Resume work after conversation context is compressed

Implement Mamba, a model architecture that processes sequences through an internal state with computation scaling linearly with input length. Covers pretrained text generation, Mamba-1 and Mamba-2 configuration, speed comparisons with Transformers, and GPU memory troubleshooting.

• Explore models for long sequences or streaming applications
• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations

Use MemOS Local to find past conversations, preferences, and decisions, then retrieve original messages or task context. The skill also guides sharing memories and reusable procedures with local agents or a configured team.

• Find preferences and decisions from past conversations
• Read original messages and surrounding dialogue
• Share memories with local agents or a team

Compare assigned work from connected trackers with TASKS.md, review stale tasks, and fill gaps in stored context about people and projects. An optional comprehensive scan finds possible missed commitments in recent activity, with changes confirmed by the user.

• Review assigned tracker tasks against TASKS.md
• Triage overdue tasks and long-standing active items
• Fill missing context about people, projects, and acronyms

Organize knowledge and specifications in OpenRig so agents can find the right context and preserve what they learn. The skill explains where files belong and how to read documents from a local role up to broader shared guidance.

• Choosing where to record a lesson
• Preparing role knowledge for a handover
• Tracing work back to its purpose and shared practices

Create and edit FigJam boards through Figma's use_figma tool. Organize meeting or brainstorming content with sections, sticky notes, shapes, connectors, and tables, and inspect existing board elements before updating their text, styling, or positions.

• Prepare a brainstorming board with sticky notes
• Build a retrospective or meeting board
• Draw diagrams with shapes and connectors