opencontext

supercent-io/skills-template · updated Apr 8, 2026

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$npx skills add https://github.com/supercent-io/skills-template --skill opencontext
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summary

Persistent memory and context management for AI agents across sessions, repositories, and dates.

  • Load project background before work, search prior conclusions during tasks, and store decisions afterward using slash commands ( /opencontext-context , /opencontext-search , /opencontext-iterate )
  • Supports keyword, vector, and hybrid search modes with optional semantic indexing via embeddings API
  • Integrates with multi-agent workflows (Claude, Gemini, Codex) through MCP tools for knowledge
skill.md

OpenContext Context Management (Persistent Memory)

Give your AI assistant persistent memory. Stop repeating explanations, and build smarter.

When to use this skill

  • When you need to keep context across sessions
  • When you need to record project background/decisions
  • When you need to search prior conclusions/lessons
  • When you need knowledge sharing in a Multi-Agent workflow
  • When you want to reduce repetitive background explanations

1. Core concepts

Problem

When working with an AI assistant, context gets lost (across sessions, repos, and dates). You end up repeating background, re-explaining decisions, and sometimes the assistant continues with incorrect assumptions.

Solution

OpenContext is a lightweight personal context/knowledge store for AI assistants.

[Load context] → [Do work] → [Store conclusions]

Default paths

Item Path
Contexts ~/.opencontext/contexts
Database ~/.opencontext/opencontext.db

2. Install and initialize

Install CLI

npm install -g @aicontextlab/cli
# Or use npx
npx @aicontextlab/cli <command>

Initialize (run inside the repo)

cd your-project
oc init

What oc init does:

  • Prepare the global context store (on first run)
  • Generate user-level commands/skills + mcp.json for the selected tool
  • Update the repo's AGENTS.md

3. Slash Commands

Beginner-friendly commands

Command Purpose
/opencontext-help When you don't know where to start
/opencontext-context (Recommended default) Load background before work
/opencontext-search Search existing documents
/opencontext-create Create a new document/idea
/opencontext-iterate Store conclusions and citations

Install locations

# Slash Commands
Cursor:      ~/.cursor/commands
Claude Code: ~/.claude/commands

# Skills
Cursor:      ~/.cursor/skills/opencontext-*/SKILL.md
Claude Code: ~/.claude/skills/opencontext-*/SKILL.md
Codex:       ~/.codex/skills/opencontext-*/SKILL.md

# MCP Config
Cursor:      ~/.cursor/mcp.json
Claude Code: ~/.claude/mcp.json

4. Core CLI commands

Folder/document management

# List folders
oc folder ls --all

# Create folder
oc folder create project-a -d "My project"

# Create document
oc doc create project-a design.md -d "Design doc"

# List documents
oc doc ls project-a

Search & manifest

# Search (keyword/hybrid/vector)
oc search "your query" --mode keyword --format json

# Generate a manifest (list of files the AI should read)
oc context manifest project-a --limit 10

Search modes

Mode Description Requirements
--mode keyword Keyword-based search No embeddings required
--mode vector Vector search Embeddings + index required
--mode hybrid Hybrid (default) Embeddings + index required

Embedding configuration (for semantic search)

# Set API key
oc config set EMBEDDING_API_KEY "<<your_key>>"

# (Optional) Set base URL
oc config set EMBEDDING_API_BASE "https://api.openai.com/v1"

# (Optional) Set model
oc config set EMBEDDING_MODEL "text-embedding-3-small"

# Build index
oc index build

5. MCP Tools

OpenContext MCP Tools

oc_list_folders    # List folders
oc_list_docs       # List documents
oc_manifest        # Generate manifest
oc_search          # Search documents
oc_create_doc      # Create document
oc_get_link        # Generate stable link

Multi-Agent integration

# Gemini: large-scale analysis
ask-gemini "Analyze the structure of the entire codebase"

# Codex: run commands
shell "docker-compose up -d"

# OpenContext: store results
oc doc create project-a conclusions.md -d "Analysis conclusions"

6. Multi-Agent workflow integration

Orchestration Pattern

[Claude] Plan
[Gemini] Analysis/research + OpenContext search
[Claude] Write code
[Codex] Run/test
[Claude] Synthesize results + store in OpenContext

Practical example: API design + implementation + testing

# 1. [Claude] Design API spec using the skill
/opencontext-context   # Load project background

# 2. [Gemini] Analyze a large codebase
ask-gemini "@src/ Analyze existing API patterns"

# 3. [Claude] Implement code based on the analysis
# (Use context loaded from OpenContext)

# 4. [Codex] Test and build
shell "npm test && npm run build"

# 5. [Claude] Create final report + store conclusions
/opencontext-iterate   # Store decisions and lessons learned

7. Recommended daily workflow

Before work (1 min)

/opencontext-context
  • Load project background + known pitfalls

During work

/opencontext-search
  • Search existing conclusions when unsure

After work (2 min)

/opencontext-iterate
  • Record decisions, pitfalls, and next steps

High-ROI document types

  • Acceptance Criteria - acceptance criteria
  • Common Pitfalls - common pitfalls
  • API Contracts - API contracts
  • Dependency Versions - dependency versions

8. Stable links (Stable Links)

Keep links stable across renames/moves by referencing document IDs:

[label](oc://doc/<stable_id>)

Generate a link via CLI

oc doc link <doc_path>

Generate a link via MCP

oc_get_link doc_path="Product/api-spec"

9. Desktop App & Web UI

Desktop App (recommended)

  • Manage/search/edit context with a native UI
  • Use without the CLI
  • Automatic index builds (in the background)

Citation features:

Action How Result
Cite text snippet Select text → right-click → "Copy Citation" Agent reads the snippet + source
Cite document Click the citation icon next to the document title Agent reads the full document + obtains stable_id
Cite folder Right-click folder → "Copy Folder Citation" Agent bulk-reads all docs in the folder

Web UI

oc ui
# Default URL: http://127.0.0.1:4321

Quick Reference

Essential workflow

Before: /opencontext-context (load background)
During: /opencontext-search (search)
After: /opencontext-iterate (store)

Core CLI commands

oc init              # Initialize project
oc folder ls --all   # List folders
oc doc ls <folder>   # List documents
oc search "query"    # Search
oc doc create ...    # Create document

MCP Tools

oc_list_folders  list folders
oc_list_docs     list documents
oc_search        search
oc_manifest      manifest
oc_create_doc    create document
oc_get_link      generate link

Paths

~/.opencontext/contexts      context store
~/.opencontext/opencontext.db  database

References

how to use opencontext

How to use opencontext on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add opencontext
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/supercent-io/skills-template --skill opencontext

The skills CLI fetches opencontext from GitHub repository supercent-io/skills-template and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/opencontext

Reload or restart Cursor to activate opencontext. Access the skill through slash commands (e.g., /opencontext) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.756 reviews
  • Ganesh Mohane· Dec 28, 2024

    Keeps context tight: opencontext is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Xiao Perez· Dec 28, 2024

    opencontext has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Carlos Tandon· Dec 20, 2024

    opencontext fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Nia Huang· Dec 16, 2024

    We added opencontext from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Sakshi Patil· Nov 19, 2024

    opencontext has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Xiao Ramirez· Nov 19, 2024

    Keeps context tight: opencontext is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Xiao Jackson· Nov 11, 2024

    Registry listing for opencontext matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Camila Patel· Nov 7, 2024

    opencontext reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Xiao Gonzalez· Oct 26, 2024

    Registry listing for opencontext matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Chaitanya Patil· Oct 10, 2024

    Solid pick for teams standardizing on skills: opencontext is focused, and the summary matches what you get after install.

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