Lola
Lola is an open-source AI context package manager that installs your agent skills across six coding assistants from one declaration
If you maintain any nontrivial skill set across two or more coding assistants, Lola removes real copy-paste drudgery and turns skill distribution into a versioned, reviewable process. The marketplace-as-YAML design is the part worth noticing: anyone can host a catalog, and Red Hat Product Security already does. The catch is scope — this is a Python 3.13+ CLI installed through uv, the assistant list is fixed at six, and content is yours to author or fetch.
Verified 4d ago · liveness 62/100 · cite: rightaichoice.com/tools/lola
- Developers running two or more AI coding assistants who keep the same skills in several places
- Platform and DevOps engineers scripting agent setup onto fresh machines via .lola-req and lola sync
- Teams that want skills, slash commands and MCP config reviewed in pull requests, not dotfiles
- Security-minded groups pulling from maintained catalogs like Red Hat Product Security's OpenSSF and OWASP modules
- Developers who work inside a single assistant ecosystem, where per-agent skill folders are enough
- Anyone who needs a graphical installer — Lola is terminal-only
- Buyers who want a curated, vetted skills catalogue with a trust guarantee
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Skip Lola if you only ever use one AI assistant, since there is no fragmentation to package away and a folder with a SKILL.md in it does the job.
You supply the content: without a curated marketplace, the hours you spend writing and maintaining SKILL.md modules are the real cost of adoption.
Lola is an open-source project, so the cost question is engineering time rather than licence spend: compare it against the hours your team spends duplicating skills across assistants, not against a per-seat subscription. If you only need skills in one assistant, a single-folder setup is cheaper. We could not verify any paid tier or listing this run.
In short
Lola — Lola is an open-source AI context package manager that installs your agent skills across six coding assistants from one declaration. Best for Developers running two or more AI coding assistants who keep the same skills in several places, Platform and DevOps engineers scripting agent setup onto fresh machines via .lola-req and lola sync, Teams that want skills, slash commands and MCP config reviewed in pull requests, not dotfiles. Free to use.
What people actually say about Lola — is it worth it?
We scanned public community sources for Lola on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Lola? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Universal AI context package manager: write skills once, install everywhere
- Single-command install across assistants (lola install <module>)
- Six install targets: Claude Code, Copilot CLI, Copilot (VS Code), Cursor, Gemini CLI, OpenCode
- Converts skills, commands and agents into each assistant's native layout
- AgentSkills.io-standard SKILL.md packages with scripts, references and assets
- AI context modules bundling many skills, AGENTS.md spec, commands and mcps.json MCP settings
- Marketplaces as a single YAML file at any URL — register, search, enable/disable per machine
- Cross-marketplace search with disambiguation when a module appears in multiple catalogs
- Declare dependencies as code in .lola-req with Git refs and version ranges
- Monorepo installs via #subdirectory= and per-module assistant filters
- Install hooks for post-install automation
- lola sync to install everything declared in a project
- OCI format support for containerized module distribution
- Apache-2.0 open source, Python 3.13+, installed via uv tool install lola-ai
- MCP server configuration alongside skills in a distributable module
About Lola
Lola is a command-line package manager for AI agent context. You write a skill or a full context module once, declare it in a `.lola-req` file or pull it from a Git repo, and `lola install` drops it into each assistant's native layout — Claude Code, Copilot CLI, Copilot in VS Code, Cursor, Gemini CLI and OpenCode all get converted from the same source. The project's own framing is an infrastructure analogy: if an agent's skills were an RPM, Lola is the DNF for it. Two package shapes matter here. An agent skill is a single SKILL.md following the AgentSkills.io standard plus whatever scripts, references and assets it needs. An AI context module is the superset — many skills bundled with a main AGENTS.md spec, slash commands and an `mcps.json` MCP settings file, shipped as one distributable unit. Lola converts skills, commands and agents into each target's own format on install, and it's honest about the matrix: Gemini CLI takes no agents, Copilot in VS Code takes agents at project scope only. Marketplaces work like package repos for agents. A marketplace is one YAML file at a URL, so you can register as many catalogs as you like, search across all of them at once, and enable or disable each per machine. Red Hat Product Security already publishes a curated catalog with a module builder, an OpenSSF security skill and an OWASP-aware review assistant. If a module shows up in two catalogs, Lola asks which you meant instead of guessing. The rest is declared as code: install hooks for post-install automation, Git refs and version ranges in `.lola-req`, monorepo subdirectory paths, and assistant filters so a module only lands in the tools you name. It installs via uv (`uv tool install lola-ai`), needs Python 3.13+, and ships Apache-2.0 under the Lobster Trap project with governance and a Matrix room. Compared with per-assistant skill folders or a bespoke dotfiles script, Lola's advantage is that the same versioned module reaches every assistant you use — with no curated,
Behind the Verdict
The honest test for Lola is simple: count your assistants. Use one and you don't have the problem it solves. Use two or more and keep more than a couple of skills in play, and the arithmetic flips fast — one `lola install sec-skills` beats hand-copying SKILL.md into `.claude/skills/`, `.cursor/skills/` and a GEMINI.md that quietly drifted three edits ago. We'd reach for it hardest on machine setup. Platform and DevOps folks who rebuild environments or onboard engineers can put skills, slash commands, agents and MCP config into a `.lola-req` file, commit it, and run `lola sync`. That turns agent configuration into something you review in a pull request rather than something living in one laptop's dotfiles. The marketplace model deserves a second look because it inverts the usual direction. Most skill tools ship a catalogue they curate and you accept. Lola ships the registry and lets you register any YAML file at a URL, search across all of them, and disable ones you don't want on a given box. Red Hat Product Security's catalog is the early proof that a third party will actually maintain one. Where it bites is the friction before the payoff. Installation goes through uv, Python 3.13 or newer is required, and everything happens at a terminal — no GUI, no click-to-install storefront. There is no curated, vetted catalogue you can trust out of the box, so a module from a random Git URL is exactly as trustworthy as its author. That's the tradeoff of a registry: power in exchange for you doing the vetting. Support coverage is uneven by design and worth reading before you commit. Skills land everywhere. Commands land everywhere. Agents don't — Gemini CLI has no agents target, and Copilot in VS Code takes them at project scope only. Lola documents that matrix rather than
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Real-world workflow fit
Concrete scenarios for the personas Lola actually fits — and what changes day-one when you adopt it.
They keep their compliance-skills repo in Git, run `lola mod add https://github.com/myorg/compliance-skills.git` once, then `lola install compliance-skills`. Lola writes the skill to .claude/skills/ and .cursor/skills/ so both assistants pick it up.
Outcome: One command keeps both assistants on the same skill version instead of two manual copies drifting apart.
They define the house skillset as a config-file module and add an install hook that runs post-install steps, so a fresh machine needs `uv tool install git+https://github.com/LobsterTrap/lola` and one install command.
Outcome: New developers start with the team's agent context already in place, and updates ship by changing the module rather than messaging everyone.
They push shared commands and agent definitions into a Git module so Claude Code, Cursor, Gemini CLI and OpenCode users all receive the same definitions where each assistant supports them.
Outcome: Reviewers see prompt changes in pull requests, and rollback is a Git revert rather than a scramble across five directories.
Use Cases
- Install a compliance skills repo across Claude Code, Cursor, Gemini CLI, OpenCode and OpenClaw with one command
- Distribute team-standard agent definitions through a shared Git module
- Stand up a new developer machine with your preferred assistant configs via install hooks
- Publish a shared skill pack so other developers can install it as a module
- Keep skills, commands and agents in sync when you switch between assistants during the day
- Version control your context modules by hosting them in Git
Limitations
- Lola is an open-source (Apache-2.0) Python 3.13+ CLI package manager, installed via `uv tool install lola-ai`, and is operated entirely from the command line — there is no graphical interface.
- The evidence specifies six assistants with differing native-layout support: Claude Code, Copilot CLI, Copilot (VS Code), Cursor, Gemini CLI and OpenCode, with capability varying per assistant (e.g.
- Gemini CLI is listed as N/A for agents, and Copilot for VS Code is project only).
- Lola converts skills, commands and agents into each tool's native layout on install, but content is user-supplied — you write or fetch your own modules from Git sources or registered marketplaces rather than drawing on a curated library.
- This refresh reached only the homepage, so API surface, release cadence and the full marketplace listing could not be verified.
as of 2026-10-05
Verification history
We have re-verified Lola 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Lola's pricing actually pencils out — and where peers do it cheaper.
Lola is an open-source project, so the cost question is engineering time rather than licence spend: compare it against the hours your team spends duplicating skills across assistants, not against a per-seat subscription. If you only need skills in one assistant, a single-folder setup is cheaper. We could not verify any paid tier or listing this run.
Setup time & first value
How long it actually takes to get something useful out of Lola — broken out by persona, not the marketing-page minute.
Individual developer: install with `uv tool install git+https://github.com/LobsterTrap/lola`, add a module and install it — minutes if your skills are already in Git. Team: allow an afternoon to package shared skills as a module and add install hooks. Per-assistant directory quirks are the slow part, not the CLI.
Switching to or from Lola
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manually copying SKILL.md files: move the files into a Git repo, add it with `lola mod add`, then `lola install <module>` to fan it out.
- →From per-assistant prompt folders: consolidate the shared content into one module per concern, then install each module to every assistant you use.
- →From a personal dotfiles script: replace the copy steps with a Lola module plus an install hook so the same config applies across assistants.
- ↗To a single-assistant setup: copy the skill out of .claude/skills/ or .cursor/skills/ and drop Lola from the workflow.
- ↗To hand-maintained folders: keep the Git repo that hosted your modules and copy its SKILL.md files into each assistant directory yourself.
- ↗To a per-assistant tool of your own: export the module contents from Git; nothing in Lola's config is proprietary.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Lola”, and we withheld 6: 6 could not be judged, because “Lola” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Lola.
Official links
Tools that pair well with Lola
Common stack mates teams adopt alongside Lola, with the specific reason each pairing earns its keep.
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Featured Head-to-Head Comparisons
Lola vs Spider Cloud
Lola and Spider Cloud solve entirely different problems: Lola is a skill package manager for agents (free, CLI-only), while Spider Cloud is a web data API for agents (freemium, usage-based). If your pain point is managing skills across assistants, choose Lola. If you need live web scraping for RAG, choose Spider Cloud. No direct overlap.
Lola vs Temporal Ai
Lola and Temporal AI solve fundamentally different problems: Lola unifies skill management across AI assistants for devs who switch tools, while Temporal ensures reliable execution for complex AI agents that need crash recovery. Choose Lola if your pain point is fragmented skills across Claude Code, Cursor, etc.; choose Temporal if you need bulletproof orchestration with retries, rollbacks, and human-in-the-loop.
Lola vs Presto Voice
Lola and Presto Voice serve entirely different markets — Lola is a free CLI tool for developers juggling multiple AI coding assistants, while Presto Voice is a contact-priced enterprise platform for QSR drive-thru automation. Your choice depends on whether your problem is AI tool fragmentation or restaurant operations: choose Lola if you're a developer syncing prompts across agents, choose Presto Voice if you're an operator wanting voice order-taking with proven upselling ROI.
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Frequently Asked Questions
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