Memorizz
MemoRizz: A Python library serving as a memory layer for AI applications. Leverages popular databases and storage solutions to optimize memory usage. Provides utility classes and methods for efficient data management.
Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add RichmondAlake/memorizz
Maintenance
fresh
3d since push
Risk
Safe to try
License is unclear
GitHub quality
745
81/100 quality · 84/100 trust
Coverage tags
Review notes
License is unclear · Quality score needs review
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Strong shortlistGood trust signals with a few areas worth checking before rollout.
Audit
Safe to tryInstall readiness, security metadata, maintenance, and adoption risk.
Trust Score v3
Human review before install
Test in a sandbox workflow and compare its install path with close alternatives.
Stars
745 GitHub stars
Repo activity
745 stars, 77 forks
Maintenance
3d since push
License
Unknown
Install
npx skills add RichmondAlake/memorizz
Install safety
standard package or runtime install path
Permission surface
filesystem or document access, database access
Docs
Strong README/SKILL.md context
Risk summary
Review before production
- License is unclear
- Quality score needs review
- License clarity: Unknown
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is unclear
- 3d since push
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- RAG and knowledge workflows
- Claude Code teams
- teams that value GitHub adoption signals
- Chunk documents
- Create embeddings
Suited agents
Trust and risk
- Trust score
- 84/100
- Risk level
- Safe to try
- Auto install
- review
Do not use when
- teams that need a vendor-supported SLA
- high-compliance environments without internal security review
- No OpenAgentSkill engagement data yet
- License is unclear
- Quality score needs review
Agent safety v2
66/100 · Review before install
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Filesystem access
Skill may read or write project files, documents, generated artifacts, or local workspace state.
medium
Database access
Skill may inspect schemas, query databases, or work with persistent stores.
- License is unclear
Install targets
Install this skill in your agent workflow
Copy the registry command or an agent-specific install prompt for Codex, Claude Code, and Cursor.
OpenAgentSkill CLI
Use the registry command when your workflow supports the OpenAgentSkill installer.
$ npx skills add RichmondAlake/memorizzAgent resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Resolve JSON
/api/agent/resolve?task=Use%20Memorizz%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20Memorizz%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/richmondalake-memorizz/install
Agent should check
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Copy prompt
Task: Use Memorizz in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20Memorizz%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/richmondalake-memorizz/install
Install command: npx skills add RichmondAlake/memorizz
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/richmondalake-memorizz/install
LLM text format
/api/skills/richmondalake-memorizz/install?format=text
Find alternatives
/api/skills/search?q=Memorizz&limit=3
Agent prompt
Use Memorizz for this task. Review https://www.openagentskill.com/api/skills/richmondalake-memorizz/install, then install with: npx skills add RichmondAlake/memorizzRegistry metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/richmondalake-memorizz
LLM text
/api/registry/manifest/richmondalake-memorizz?format=text
Install alias
/api/registry/install/richmondalake-memorizz
Recommend
/api/registry/recommend?task=Use%20Memorizz%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Python, Semantic Search, Claude Code
Audit report
Safe to try · 86/100
Review install readiness, maintenance, trust, quality, and metadata warnings before adding this skill to an agent workflow.
Agent decision cockpit
Primary pick for RAG and knowledge
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
RAG and knowledge
Trust label
Production-ready
Install path
Command ready
Use when
- RAG and knowledge workflows
- Claude Code teams
- teams that value GitHub adoption signals
Evidence
- 745 GitHub stars
- recent repository activity
- install command or GitHub repo available
- 81/100 quality profile
Review first
- No OpenAgentSkill engagement data yet
Implementation path
- 1Install it in a sandbox agent and run one RAG and knowledge task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Trust profile
Strong shortlist
Good trust signals with a few areas worth checking before rollout.
GitHub adoption
INFO745 GitHub stars
Stars/forks activity
INFO745 stars, 77 forks; issue activity unavailable in current metadata
Recent maintenance
PASS3d since push
License clarity
CHECKUnknown
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Meaningful GitHub adoption signal
- Install command has no obvious high-risk pattern
Review before install
- License is unclear
- Quality score needs review
- License clarity: Unknown
Recommended action
Test in a sandbox workflow and compare its install path with close alternatives.
Quality profile
Strong candidate for agent workflows
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Use this skill in these scenarios
Search private knowledge
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Stack fit
Add it to a complete workflow
Ingest, retrieve, and cite
RAG knowledge base
A stack for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Turn skills into distribution
Content growth agent
A stack for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
Coding review agent
A stack for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Compare before you install
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Overview
MemoRizz: A Python library serving as a memory layer for AI applications. Leverages popular databases and storage solutions to optimize memory usage. Provides utility classes and methods for efficient data management.
Imported by the skill-only GitHub discovery pipeline because it matches agent skill, automation, domain workflow, RAG, document-processing, data, finance, security, or developer-tool signals. Protocol-server projects are excluded from automated imports.
Platform Compatibility
Technical Details
- Version
- 1.0.0
- License
- Unknown
- Last Updated
- 6/15/2026
- Published
- 6/5/2026
Frameworks & Tools
Decision snapshot
Primary pick
745 GitHub stars
Audit snapshot
Install review
Install and adoption review
- Security
- 81/100
- Maintenance
- 100/100
- Install
- 92/100
Install
Add to agent workflow
Free and open source. Review the audit before production use.
Growth loop
Share kit
Scenario-led draft for Memorizz, ready for a manual X post.
OpenAgentSkill Update Today: Memorizz Use it when your agent needs to turn docs, data, or knowledge bases into answers and actions. 745 stars - rag-knowledge Link: https://www.openagentskill.com/skills/richmondalake-memorizz?ref=x #AIAgents #OpenAgentSkill
Optional reply with install command
Link for Memorizz: https://www.openagentskill.com/skills/richmondalake-memorizz?ref=x Install: npx skills add RichmondAlake/memorizz
Listing source
Community indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- RichmondAlake
- Source
- RichmondAlake/memorizz
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This community indexed listing is attributed to RichmondAlake but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
README badge
Add this badge to your GitHub README to show the listing, trust score, and install handoff.
[](https://www.openagentskill.com/skills/richmondalake-memorizz)Author
RichmondAlake
@richmondalake
Tags
Platform Fit
Health Signals
- GitHub stars
- 745
- Quality score
- 54/100
- Last GitHub push
- Jun 13, 2026
- Framework hints
- 2
- OpenAgentSkill views
- 0
- Install copies
- 0
- Outbound clicks
- 0
Community Signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & Safety
Strong shortlist
- GitHub adoption745 GitHub starsINFO
- Stars/forks activity745 stars, 77 forks; issue activity unavailable in current metadataINFO
- Recent maintenance3d since pushPASS
- License clarityUnknownCHECK
- README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
- Dependency/runtime riskdatabase surfacePASS
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