hindsight-local

Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user)

By vectorize-io · 387 installs

npx skills add vectorize-io/hindsight --skill hindsight-local

Source repository · Upstream listing

Hindsight Memory Skill (Local) You have persistent memory via the hindsight embed CLI. Proactively store learnings and recall context to provide better assistance. Setup Check (First Time Only) Before using memory commands, verify Hindsight is configured: If this fails or shows "not configured" , run the interactive setup: This will prompt for an LLM provider and API key. After setup, the commands below will work. How Hindsight Works When you call retain , Hindsight does not store the string as is. The server runs an internal pipeline that: 1. Extracts structured facts from the content using an LLM 2. Identifies entities (people, tools, concepts) and links related facts 3. Builds temporal and causal relationships between facts 4. Generates embeddings for semantic search This means you should pass rich, full context content — the server is better at extracting what matters than a pre summarized string. Your job is to decide when to store, not what to extract. Commands Store a memory Use memory retain to store what you learn. Pass the full context — raw observations, session notes, conversation excerpts, or detailed descriptions: You can also pass a raw conversation transcript with timestamps: Recall memories Use memory recall BEFORE starting tasks to get relevant context: Reflect on memories Use memory reflect to synthesize context: IMPORTANT: When to Store Memories Always store after you learn something valuable: User Preferences Coding style (indentation, naming conventions, language preferences) Tool preferences (editors, linters, formatters) Communication preferences Project conventions Procedure Outcomes Steps that successfully completed a task Commands that worked (or failed) and why Workarounds discovered Configuration that resolved issues Learnings from Tasks Bugs encountered and their solutions Performance optimizations that worked Architecture decisions and rationale Dependencies or version requirements IMPORTANT: When to Recall Memories Always recall before: Starting any non trivial task Making decisions about implementation Suggesting tools, libraries, or approaches Writing code in a new area of the project Best Practices 1. Store immediately : When you discover something, store it right away 2. Pass rich context : Include full observations, not pre summarized strings — the server extracts facts automatically 3. Include outcomes : Store what happened AND why, including failures and workarounds 4. Recall first : Always check for relevant context before starting work 5. Use context for metadata : The context flag labels the type of memory (e.g., procedures , learnings , preferences ), not a replacement for full content