Openagentskill
Give your AI agent a job in plain language and OpenAgentSkill hands back one ranked, install-ready skill with trust signals attached.
If your agent installs third-party code without a human checking first, this is the stop-before-you-run layer most stacks lack. The trust profile with risk notes and permission hints is the actual product — the 35,752-skill count is the byproduct. Expect to bring your own runtime, because OpenAgentSkill tells the agent what to install and nothing about containing it afterward.
Verified 15h ago · liveness 77/100 · cite: rightaichoice.com/tools/openagentskill
- Agent developers wiring API-driven skill discovery with trust signals before install
- Teams running Codex, Claude Code, Cursor, MCP-compatible agents or custom runners
- Engineers building scraping, coding-agent or research pipelines that reuse published skills
- Platform teams that want ranked alternatives and risk notes instead of a flat skill list
- Anyone wanting a managed execution environment — the registry ships install paths but no sandbox
- Beginner or non-technical users who don't work through CLI commands or API calls
- Teams that need every listed skill to carry outcome evidence, since coverage is partial
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
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Skip OpenAgentSkill if you need the registry to also contain or sandbox what it recommends — it returns an install command and a risk summary, and running, isolating and rolling back the code is still on you.
Openagentskill's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
In short
Openagentskill — Give your AI agent a job in plain language and OpenAgentSkill hands back one ranked, install-ready skill with trust signals attached. Best for Agent developers wiring API-driven skill discovery with trust signals before install, Teams running Codex, Claude Code, Cursor, MCP-compatible agents or custom runners, Engineers building scraping, coding-agent or research pipelines that reuse published skills. Free to use.
What's new in Openagentskill
Checked 7 days agoAcross the latest 1 update: 1 changelog entry.
What people actually say about Openagentskill — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
17 mentions across 2 sources (YouTube, GitHub) · researched Aug 1, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Agent-first design with natural language task-to-skill resolution.
- +Trust profiles with risk, permissions, and audit notes for safety.
- +Install plans generate ready-to-run commands for multiple surfaces.
- +Web, API, and CLI access make it flexible for any workflow.
- +Covers 20K+ skills across categories like RAG and browser automation.
- −Approved submissions not persisted when database save fails.
- −Security risk: agent might install every skill without oversight.
- −Privacy: anonymous telemetry may concern sensitive users.
- −Limited real-world feedback on reliability in production.
- −Comparisons with skills.sh and agentskills.io may create confusion.
- • None reported, but enterprise features or support may be paid later
Viability Score
How well maintained and how widely used is Openagentskill? 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
- Natural-language task-to-skill resolution via POST /api/agent/resolve
- Returns one recommended skill plus ranked alternatives with fit scores
- Trust profiles with risk summaries, permission hints and readiness notes
- Install plan generation with ready-to-run npx commands
- GET /api/agent/skills/{name} for repository health and risk review
- GET /api/skills/{name}/install?format=text tuned for low-token LLM calls
- format=text responses across skill list and search endpoints
- Works with Codex, Claude Code, Cursor, MCP-compatible agents and custom runtimes
- npx CLI with a --dry-run resolve step before anything is installed
- Copy for AI flow that pastes a prompt into Codex, Claude Code or Cursor without installing
- GitHub popularity leaderboard filtering weak matches before ranking
- Ranking across workflow fit, maintenance activity, GitHub stars and audit signals
- Human browsing by category and by output format alongside the agent API
- Gallery of skills with published author example outputs
- Skill submission via GitHub with a SKILL.md name and description manifest
About Openagentskill
OpenAgentSkill is a skill registry built for agents to call, not a directory for people to scroll. You describe a task in natural language and the registry returns a single recommended skill, a ranked list of alternatives with fit scores, an install command, and the review evidence behind the pick — risk summary, permission hints, and readiness notes. It currently shows 35,752 indexed skills, 589 verified installs, 677 agent outcomes, and 229 skills with evidence on file, drawn from tens of thousands of GitHub projects and refreshed continuously. The machine-facing path is an API loop across four layers: intent capture, a recommendation ranker that weighs workflow fit, maintenance activity, GitHub stars and audit signals, a trust profile, and an executable install path. Endpoints include POST /api/agent/resolve, GET /api/agent/skills/{name} for repository health, and GET /api/skills/{name}/install?format=text, with text-format responses tuned for low-token LLM calls. Humans aren't locked out. The site offers category browsing across web/UI, coding, video, research, scraping, documents, presentations and data, plus a GitHub popularity leaderboard that screens weak matches before ranking — its October update listed weather at 391.3K stars, grill-me at 294.9K and using-superpowers at 280.4K. An npx CLI provides a --dry-run resolve step before anything installs, and Copy for AI pastes a prompt into Codex, Claude Code or Cursor without installing at all. Who it suits: developers wiring skill discovery into an agent runtime that spans Codex, Claude Code, Cursor, MCP-compatible agents and in-house runners. It competes with skills.sh (browsing and install) and agentskills.io (format and spec), but its bet is narrow — a recommendation and audit layer your agent queries before it writes files or installs third-party code. Execution stays yours.
Behind the Verdict
The pitch here is unusually honest: a listing is guidance, not a warranty. That framing tells you who OpenAgentSkill is for. It's for the engineer who's watched an agent pull a skill off a flat list and start acting, and now wants a ranked recommendation with the risk summary attached to the install command. The registry ships 35,752 indexed skills and 229 with evidence on file, and that gap is the thing to internalize — most listings carry ranking signals, not outcome proof. We'd reach for it when discovery needs to be an API call. POST /api/agent/resolve taking a plain-language task and returning one pick plus alternatives and fit reasons is the shape you want inside a runtime, and format=text responses keep token spend down on every skill list and search call. The npx --dry-run resolve step is a nice bit of paranoia before anything lands on disk. Pass if you need execution handled. There is no sandbox or managed runtime — you get the install path, not the containment. Beginners will bounce off the CLI-first flow, and teams demanding evidence on every listing should know coverage is partial by design. Compared with skills.sh, which is built for humans finding installable skills fast, and agentskills.io, which defines the open format, OpenAgentSkill sits between them as the agent-facing recommendation layer. In practice, pair it with a human-browsable directory and your own runtime controls. Where it bites: the trust profile is only as current as its evidence, so treat risk notes as a starting review prompt, not a clearance.
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Real-world workflow fit
Concrete scenarios for the personas Openagentskill actually fits — and what changes day-one when you adopt it.
You wire POST /api/agent/resolve into a Codex or Claude Code runtime so the agent can describe a task mid-run and get one recommended skill plus alternatives and an install command with permission hints.
Outcome: The agent installs a reviewed skill instead of guessing a repository, and the risk notes travel with the pick rather than living in a separate audit step.
You run npx --dry-run against the registry for a scrape-these-pages task, review the recommended repository, then check GET /api/agent/skills/{name} for repository health and audit signals before rerunning with --yes.
Outcome: You install a skill you have actually inspected, with a record of the install outcome receipt attached, or you decline and try the second-ranked alternative.
You use the Copy-for-AI flow to paste a prompt into your agent and get a recommended skill for slides, images or a website, with the example output from the skill's gallery shown first.
Outcome: You see the result the skill produces before installing anything, and can either proceed with the install command or keep browsing the ranked alternatives.
Use Cases
- Resolve a web scraping task to the best skill with an install command and safety notes attached.
- Discover coding agent skills for pull request review and code generation.
- Find RAG skills to ingest documents and retrieve grounded context.
- Automate browser tasks by selecting and installing browser automation skills.
- Compare skill options for financial data analysis before running any code.
- Integrate the registry API into a custom agent runtime for dynamic skill discovery.
- Build a trusted skill layer for production agent systems that run without human oversight.
- Check a skill's trust profile, permission hints and repository health before installing it into a pipeline.
Limitations
- OpenAgentSkill indexes third-party repositories; a listing, Trust Score or audit is decision support, not a warranty, and the registry supplies an install path with no sandbox, containment or runtime, so execution and rollback remain entirely your responsibility.
- Verified installs are counted only when a compatible agent or integration reports an idempotent install outcome receipt — direct GitHub clones and third-party installer runs are not observable unless they submit a receipt, so install counts understate real usage.
- Only 228 of roughly 35,000 indexed skills currently carry evidence on file, and blocked skills are refused, with verified install evidence recorded only after the installer succeeds.
- Ranking leans on GitHub stars, so long-tail skills with strong workflow fit and modest popularity may not surface.
as of 2026-10-11
Verification history
We have re-verified Openagentskill 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-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
- — 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
- — 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.
Where the pricing makes sense
The company stage and team size where Openagentskill's pricing actually pencils out — and where peers do it cheaper.
Openagentskill's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
Setup time & first value
How long it actually takes to get something useful out of Openagentskill — broken out by persona, not the marketing-page minute.
Agent developer: minutes to fire a first POST /api/agent/resolve or run the npx dry-run against a test task. Platform team: under an hour to wire resolve plus the skill detail endpoint into a runtime and add a review gate. Creator using the Copy-for-AI flow: no setup at all — copy, paste, see the recommendation, decide whether to install.
Switching to or from Openagentskill
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a manual GitHub search: resolve a plain-language task through the API and get one ranked skill with alternatives, instead of grepping star counts across repos.
- →From skills.sh browsing: keep skills.sh for human exploration, and route your agent's mid-task discovery through the resolve endpoint with trust signals attached.
- →From a flat in-house skill list: point the resolve endpoint at the same tasks and compare its ranked picks against your internal list before retiring entries.
- →From an unscored agent marketplace: run your existing install set through GET /api/agent/skills/{name} to pull repository health and audit signals you did not previously track.
- ↗To a managed sandbox runner: export the skills you resolved and reinstall them inside a runtime that contains and rolls back execution, since OpenAgentSkill supplies paths only.
- ↗To agentskills.io: move to the open skill format definition if your need shifts from discovery to authoring and standardizing skill manifests yourself.
- ↗To skills.sh: keep the human-browsable directory as your primary surface if your team picks skills by reading rather than by agent query.
- ↗To an in-house registry: snapshot the skills you have vetted and host the shortlist yourself if you need the audit trail inside your own infrastructure.
Integrations
Resources & Guides
- Documentationopenagentskill.com
Docs · Openagentskill
Full product docs from openagentskill.com
- Resourceopenagentskill.com
Home · Openagentskill
Helpful link from openagentskill.com
- Resourceopenagentskill.com
Blog · Openagentskill
Helpful link from openagentskill.com
- Guideopenagentskill.com
Guides · Openagentskill
In-depth how-to from openagentskill.com
- Resourcegithub.com
Openagentskill · Openagentskill
Helpful link from github.com
Tutorials & Learning
YouTube returned 4 videos for “Openagentskill”, and we withheld 4: 4 could not be judged, because “Openagentskill” 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 Openagentskill.
Official links
Tools that pair well with Openagentskill
Common stack mates teams adopt alongside Openagentskill, with the specific reason each pairing earns its keep.
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CopilotKit
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Pipedream
Pipedream is the integration layer that gives AI agents managed auth plus 10,000+ tools across 3,000+ APIs.
Featured Head-to-Head Comparisons
Openagentskill vs Spider Cloud
Spider Cloud and OpenAgentSkill solve different problems: Spider Cloud is the go-to for extracting fresh web data (crawling, scraping, structured output) at scale with AI enhancements; OpenAgentSkill is a registry for discovering and installing reusable agent skills. Choose Spider Cloud if your bottleneck is getting real-time web content into your AI pipeline. Choose OpenAgentSkill if you need to compose agent workflows from existing capabilities and prioritize safety auditing.
Openagentskill vs Presto Voice
Presto Voice and Openagentskill serve entirely different domains: Presto Voice is a specialized drive-thru voice AI for QSR chains looking to boost revenue and efficiency, while Openagentskill is a free, developer-focused registry for AI agent skill discovery. Choose Presto Voice if you operate a multi-location QSR and need proven ROI from voice ordering and upselling; choose Openagentskill if you build AI agents and need a curated, installable skill index.
Openagentskill vs Temporal Ai
If you need durable, crash-proof execution for multi-step AI workflows or microservices, Temporal is the clear choice—its state capture and recovery features are unmatched. If your main goal is to discover, audit, and install reusable agent skills from a registry, OpenAgentSkill offers a free, purpose-built solution without orchestration overhead. Choose Temporal for reliability, choose OpenAgentSkill for skill discovery.
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Chrome DevTools MCP
Open-source MCP server that gives coding agents live Chrome DevTools for debugging, traces, and browser automation.
CopilotKit
Open-source React framework for agent chat, generative UI, and shared state on any AG-UI backend.
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