OpenClaw went from a side project to the fastest-growing open-source agent ever in a matter of months, and it picks up tools the way it picks up everything else — through community skills. Launched as Clawdbot, briefly Moltbot, now OpenClaw, it runs locally on any model you bring and extends through ClawHub, a registry of installable skills. For a tool author, that registry is the door: a published skill makes your tool a capability OpenClaw can use directly, not just one it has heard of. This is the playbook for getting recommended by OpenClaw, sitting under the broader pillar on generative engine optimization, extended from citation to agent tool-selection.
Key takeaways
- The distinctive lever — ClawHub. A SKILL.md-compliant skill submitted to OpenClaw's community registry becomes a directly installable, discoverable capability — the OpenClaw-native distribution channel.
- The inherited lever — OpenClaw runs any bring-your-own-key model, so without a skill, recommendations inherit the configured model's priors. Broad training-data presence still matters.
- The retrieval lever — OpenClaw web-searches live when asked, so a clean README and an llms.txt make what it finds about your tool accurate and current.
- The naming note — OpenClaw was Clawdbot, then Moltbot, before its current name. Those aliases still carry search traffic and refer to the same product.
How OpenClaw chooses what to recommend
OpenClaw is an autonomous local agent that combines a configured model with installable skills and live search. Its skills registry is the part that distinguishes it.
- Inherit — the user's bring-your-own-key model brings its training priors to any recommendation.
- Install — skills from ClawHub, in the SKILL.md format, become first-class capabilities.
- Configure — Markdown files define the agent's identity, memory, and permissions.
- Search — OpenClaw web-searches live when a task needs current information.
- Act — it runs autonomously on the local machine, using whatever capabilities it has.
Two implications follow. First, as with Hermes, the bring-your-own-key model means breadth of training-data presence beats optimising for any single provider — you want to be the tool the model already knows, whichever model that is. Second, ClawHub is the lever OpenClaw actually hands you: a published skill is the difference between being a name in the model's memory and a capability the agent can install and run, which on an autonomous local agent is the strongest possible form of recommendation.
The playbook
Tactics ordered by leverage, calibrated for OpenClaw. The first is OpenClaw-specific; the rest are the cross-agent fundamentals applied here.
- Publish a SKILL.md skill to ClawHub. This is the direct discovery surface. A skill in the SKILL.md format, submitted to OpenClaw's community registry, makes your tool installable and discoverable by OpenClaw users — the OpenClaw-native equivalent of an MCP server or an AGENTS.md, and the highest-signal lever you have for this agent.
- Maintain broad training-data prevalence. Because OpenClaw runs any model the user configures, strong GitHub and package-registry signal and wide documentation follow you across whatever model they pick. This is the inherited-layer baseline, and OpenClaw's model flexibility makes breadth matter more than depth on one provider.
- Keep a clean README and llms.txt for live search. OpenClaw web-searches when a task requires current information, so well-structured docs and an llms.txt that indexes your key pages improve what it retrieves about your tool — the same assets that serve every other agent and the text engines.
- Cover the aliases. OpenClaw was Clawdbot and Moltbot, and those names still carry search traffic and appear in older discussion. Where you reference the agent in your own docs and content, acknowledge the history so you match how people actually search for it, and so your material reads as current rather than dated.
- Design the skill for autonomous use. OpenClaw runs locally and autonomously, so a skill with clear capability boundaries, sensible defaults, and explicit permission needs is safer for users to install and more likely to be kept. A skill that is ambiguous about what it touches is one users remove.
- Describe capabilities in conventional, legible terms. A skill whose description clearly states what the tool does, in the vocabulary users and models already use, is easier for OpenClaw to select and apply than one with an opaque name or vague scope.
What's different from Hermes Agent, Claude Code, and Codex
OpenClaw is closest to Hermes among the four, sharing the SKILL.md skills model; it differs more sharply from Claude Code and Codex.
- Hermes Agent also uses SKILL.md skills, so a skill built for ClawHub is close to a Hermes skill. The two are the natural pair to target together. The Hermes playbook is at get recommended by Hermes Agent.
- Claude Code centres on MCP servers and Context7 rather than a skills registry, a different packaging path. The Claude Code playbook is at get recommended by Claude Code.
- Codex centres on AGENTS.md, another distinct format. The Codex playbook is at get recommended by Codex.
- The bring-your-own-key model OpenClaw shares with Hermes means, for both, breadth of training-data presence matters more than tuning for one provider's cutoff. For broader context on the agent itself, see the OpenClaw ecosystem guide.
- NVIDIA NemoClaw runs OpenClaw inside a security sandbox, so on a NemoClaw deployment being recommended here is necessary but not sufficient — your tool also has to pass NemoClaw's network-policy allowlist. The governance playbook is at get recommended by NemoClaw.
Measurement
ClawHub installs are the cleanest signal OpenClaw offers. Build the loop in three layers:
- ClawHub installs and usage. If you publish a skill, track its installs and usage from the registry — the most direct evidence the channel is working.
- Direct testing. Run OpenClaw on representative tasks and see whether it reaches for your tool, re-testing after publishing a skill or improving docs.
- Cross-reference the text engines. The docs-and-llms.txt work that helps OpenClaw also helps the answer engines, so an LLM-visibility tracker gives a related read. The Radar's shortlist is at 6 GEO Tools the Radar Actually Recommends; CTAIO Labs tested ten in the visibility tools test.
Related reads
Frequently asked questions
What is OpenClaw, and what were Clawdbot and Moltbot?
They are the same product under three names. Austrian developer Peter Steinberger launched it as Clawdbot in November 2025; it was renamed Moltbot in late January 2026 following a trademark complaint from Anthropic, then renamed OpenClaw a few days later. It is an open-source autonomous agent that runs locally, connects a large language model to real software, and became the fastest-growing open-source project on record, passing 250,000 GitHub stars by early 2026. If you are optimising for it, treat Clawdbot and Moltbot as aliases that still carry real search traffic.
How does OpenClaw decide what to recommend?
Through a mix of its configured model and its skills. OpenClaw runs any bring-your-own-key model, so without an installed skill, recommendations inherit that model's training priors. Its distinctive layer is ClawHub, a community registry of skills in the SKILL.md format: an installed skill becomes a capability the agent uses directly. It also web-searches live when a task calls for it. There is no published ranking spec, so this describes the inputs rather than a disclosed algorithm.
What is ClawHub and why does it matter?
ClawHub is OpenClaw's community skills registry, and it is the agent-native distribution channel for tool authors. A skill packaged in the SKILL.md format and published to ClawHub becomes directly installable and discoverable by OpenClaw users, turning your tool from something the underlying model might mention into a capability the agent can actually use. It is the OpenClaw equivalent of an MCP server for Claude Code or an AGENTS.md for Codex — the single highest-signal lever for this agent.
Does the bring-your-own-key model affect my strategy?
Yes, similarly to Hermes. Because OpenClaw users choose their own model, you cannot optimise for one provider's training cutoff. The durable baseline is broad training-data prevalence — strong GitHub and package-registry signal, wide documentation — that follows you across whatever model a user runs. Pair that breadth with a ClawHub skill for the OpenClaw-native layer, and you cover both the inherited and the agent-specific paths.
Does an llms.txt help with OpenClaw?
It helps the live-search path. OpenClaw web-searches when a task requires current information, so a clean README and an llms.txt that indexes your key documentation improve the accuracy of what it retrieves about your tool. As with the other agents and the text answer engines, it is low-effort and reusable, so maintaining one pays off across multiple surfaces rather than just this one.
How is getting recommended by OpenClaw different from the other agents?
The packaging format and the model situation. OpenClaw and Hermes both use the SKILL.md model — OpenClaw through ClawHub — so a skill built for one is close to a skill for the other, whereas Claude Code uses MCP and Codex uses AGENTS.md. And like Hermes, OpenClaw's bring-your-own-key design means there is no single model cutoff to plan around. The shared baseline of strong public signal and clean docs helps across all four agents.
How do I measure whether this is working?
Through ClawHub installs and direct testing. If you publish a skill, track its installs and usage from ClawHub as the most direct signal. Test by running OpenClaw on representative tasks to see whether it reaches for your tool, and correlate installs and sign-ups with agent-driven discovery. Because there is no recommendation analytics surface, measurement is proxies plus direct tests, the same posture as for the other agents.
What is OpenClaw, and what were Clawdbot and Moltbot?
They are the same product under three names. Austrian developer Peter Steinberger launched it as Clawdbot in November 2025; it was renamed Moltbot in late January 2026 following a trademark complaint from Anthropic, then renamed OpenClaw a few days later. It is an open-source autonomous agent that runs locally, connects a large language model to real software, and became the fastest-growing open-source project on record, passing 250,000 GitHub stars by early 2026. If you are optimising for it, treat Clawdbot and Moltbot as aliases that still carry real search traffic.
How does OpenClaw decide what to recommend?
Through a mix of its configured model and its skills. OpenClaw runs any bring-your-own-key model, so without an installed skill, recommendations inherit that model's training priors. Its distinctive layer is ClawHub, a community registry of skills in the SKILL.md format: an installed skill becomes a capability the agent uses directly. It also web-searches live when a task calls for it. There is no published ranking spec, so this describes the inputs rather than a disclosed algorithm.
What is ClawHub and why does it matter?
ClawHub is OpenClaw's community skills registry, and it is the agent-native distribution channel for tool authors. A skill packaged in the SKILL.md format and published to ClawHub becomes directly installable and discoverable by OpenClaw users, turning your tool from something the underlying model might mention into a capability the agent can actually use. It is the OpenClaw equivalent of an MCP server for Claude Code or an AGENTS.md for Codex — the single highest-signal lever for this agent.
Does the bring-your-own-key model affect my strategy?
Yes, similarly to Hermes. Because OpenClaw users choose their own model, you cannot optimise for one provider's training cutoff. The durable baseline is broad training-data prevalence — strong GitHub and package-registry signal, wide documentation — that follows you across whatever model a user runs. Pair that breadth with a ClawHub skill for the OpenClaw-native layer, and you cover both the inherited and the agent-specific paths.
Does an llms.txt help with OpenClaw?
It helps the live-search path. OpenClaw web-searches when a task requires current information, so a clean README and an llms.txt that indexes your key documentation improve the accuracy of what it retrieves about your tool. As with the other agents and the text answer engines, it is low-effort and reusable, so maintaining one pays off across multiple surfaces rather than just this one.
How is getting recommended by OpenClaw different from the other agents?
The packaging format and the model situation. OpenClaw and Hermes both use the SKILL.md model — OpenClaw through ClawHub — so a skill built for one is close to a skill for the other, whereas Claude Code uses MCP and Codex uses AGENTS.md. And like Hermes, OpenClaw's bring-your-own-key design means there is no single model cutoff to plan around. The shared baseline of strong public signal and clean docs helps across all four agents.
How do I measure whether this is working?
Through ClawHub installs and direct testing. If you publish a skill, track its installs and usage from ClawHub as the most direct signal. Test by running OpenClaw on representative tasks to see whether it reaches for your tool, and correlate installs and sign-ups with agent-driven discovery. Because there is no recommendation analytics surface, measurement is proxies plus direct tests, the same posture as for the other agents.
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