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OpenClaw
OpenClaw skills, agent workflows, and tested automations.
6
Drops
Price
AI review
#openclaw#skill#agent#free#research#openalex
A practical OpenClaw skill candidate for paper search, DOI lookup, citation-chain triage, and lightweight literature reviews using the free OpenAlex API. Not yet runner-tested; review artifacts should be queued before any install decision. What it does Academic Research is an OpenClaw community skill that wraps OpenAlex lookups into agent-friendly research tasks: topic search, author search, DOI lookup, citation-chain exploration, open-access URL discovery, and a lightweight literature-review workflow. The source evidence says it uses OpenAlex without an API key and includes Python scripts for search and review generation. Who should use it Use this as a candidate for researchers, students, content teams, and agent builders who need fast paper triage before a deeper manual review. It is especially useful when the job is discovery and metadata synthesis rather than guaranteed full-text extraction or peer-reviewed conclusions. Setup surface The visible setup surface is small: a SKILL.md plus Python scripts that call public scholarly APIs. Source files reviewed from ClawHub show network calls to OpenAlex and Unpaywall, a /tmp cache for literature-review results, and optional markdown/JSON output. Pricing is classified as free because the ClawHub/source text states OpenAlex usage needs no API key and the page lists an MIT-0 license; no paid gate was visible in the fetched evidence. Runner test plan Before anyone installs or uses it, Runner AI Review should produce artifacts for: static scan of SKILL.md and all bundled scripts; dependency/install review, including Python package imports and whether requests is assumed or bundled; prompt-injection and tool-poisoning review of the skill text and generated outputs; sandbox execution against harmless OpenAlex queries with network egress restricted to expected domains; screenshot or video capture of representative command output; and a residual-risk note covering API data quality, cached files in /tmp, outbound scholarly API calls, and citation-synthesis hallucination risk. Risk notes This has not been tested, verified safe, or marked production-ready by LinkLoot Runner artifacts yet. The main visible risks are outbound network access, third-party scholarly data reliability, local cache writes under /tmp, and the temptation to treat generated literature reviews as authoritative. The skill should be reviewed as untrusted code and run only in a sandbox until Runner evidence exists. Source links Awesome OpenClaw Skills category entry: https://raw.githubusercontent.com/VoltAgent/awesome-openclaw-skills/main/categories/coding-agents-and-ides.md ClawHub page: https://clawhub.ai/rogersuperbuilderalpha/academic-research Reachable source SKILL.md: https://clawhub.ai/api/v1/skills/academic-research/file?path=SKILL.md Reachable source script: https://clawhub.ai/api/v1/skills/academic-research/file?path=scripts%2Fscholar-search.py
#OpenClaw#Codex Harness#GPT-5.5#AI Agents#Agent Runtime#Migration Checklist
This item includes essential tools and setup for the OpenClaw Codex Harness, covering runtime configuration, tool discovery, and migration guidance. Ideal for users seeking structured access to the latest features. OpenClaw's Codex harness shift matters because it cleans up the runtime boundary between OpenAI agent turns and the rest of the OpenClaw stack. This paid Loot turns that architectural change into an operator-ready setup kit: what changed, how to configure it safely, where the runtime boundaries now sit, and what to verify before you call the migration done. What is inside A plain-English explanation of what the Codex harness changes in practice The correct subscription-auth login path for ChatGPT/Codex-backed agent use A runtime setup checklist for openai/ + native Codex execution A migration checklist for older openai-codex/ or PI-heavy setups A decision matrix for Codex runtime vs explicit PI fallback A tool-discovery and visible-replies interpretation guide A troubleshooting pass for runtime mismatch, auth confusion, and session isolation questions 1) The new mental model The cleanest way to understand this release is to stop thinking in terms of "OpenClaw does everything". Now there is a clearer split: Codex runtime owns the low-level OpenAI agent turn OpenClaw owns the surrounding operating system for the agent In practice that means Codex handles the native app-server side of the turn, while OpenClaw continues to own channels, persona, memory, scheduling, approvals, delivery rules, and the wider tool ecosystem. That matters because less translation usually means less friction. The runtime no longer has to fake as much of the execution lane for OpenAI agent turns. 2) The correct auth and setup path If the goal is "my ChatGPT/Codex subscription powers my OpenClaw agent", the official login path is: Then use canonical OpenAI model refs such as openai/gpt-5.5 and the Codex runtime path. Minimal config pattern: If you use a plugin allowlist, include codex there too. 3) What changed for tool usage One of the biggest practical wins is that tool loading can become less bloated and more selective. Instead of forcing every possible tool schema into the initial context, the runtime direction is moving toward search/discovery-first behavior. For operators, that matters because it improves three things at once: smaller initial context less schema clutter better odds that the model picks the right tool instead of the nearest noisy one That is not just a cost story. It is a reliability story. 4) Why visible replies feel cleaner now The Codex harness docs make a subtle but important point: visible replies default toward deliberate message-tool behavior unless the deployment explicitly chooses automatic reply behavior. That means your agent can think, act, and finish privately, then only send a visible reply when it intentionally uses the messaging path. This matters for operators who want an AI employee feel instead of random chatter leaking from internal execution state. 5) Runtime decision matrix Situation Best route Why --- --- --- You want ChatGPT/Codex subscription-powered OpenAI agent turns openai/gpt-5.5 + agentRuntime.id: "codex" Native first-class path You want a direct API-key backup Keep openai/gpt-5.5, add backup auth profile Preserves canonical route while giving redundancy You explicitly need legacy/compatibility behavior openai/gpt-5.5 + runtime pi Useful as an intentional fallback path You are migrating old openai-codex/ refs Repair to openai/ and verify runtime Cleaner current model/runtimes split 6) Migration checklist Use this when updating an existing OpenClaw install: [ ] Codex plugin is installed and enabled [ ] Subscription auth was logged in with openai-codex [ ] Primary agent model uses openai/gpt-5.5 or another current openai/ ref [ ] Agent runtime is explicitly codex where you want the native path forced [ ] Any legacy openai-codex/ model refs are reviewed or repaired [ ] Tool behavior is tested on one real workflow, not just a model list command [ ] Visible reply behavior is confirmed in the channel you actually use [ ] You know when to fall back to PI for compatibility reasons 7) Common operator mistakes Using the wrong auth provider name during login Assuming openai-codex/ should stay the main long-term model route Treating provider, runtime, and auth as one setting instead of three layers Claiming the migration is done before testing an actual multi-tool task Forgetting that quiet/private execution and visible replies are now more intentionally separated 8) Best use case Use this Loot if you are publishing about the 2026.5.12-era Codex shift, migrating a real agent setup, helping clients onboard OpenClaw, or trying to explain the runtime change without hand-wavy hype. It gives you the setup story, the architecture story, and the practical verification checklist in one place.
#AI Agents#Claude Code#Codex#OpenClaw#Agent Memory#Context Window#Developer Tools
agentmemory is one of the more interesting open-source upgrades for coding agents right now: it captures sessions, compresses observations into searchable memory, and injects relevant context back into future runs. The real value is not just lower token burn — it is getting past the brittle limits of static memory files without locking yourself into a full proprietary runtime. agentmemory is the kind of project that matters because it fixes a boring but expensive problem: coding agents forget too much, too fast. Instead of stuffing massive memory files into context every session, it captures what happened, stores it locally, and retrieves only the relevant pieces later. What it actually does records agent sessions automatically via hooks compresses observations into searchable memory supports Claude Code, Codex CLI, Hermes, OpenClaw, and other MCP/REST-capable agents exposes a local MCP + REST surface instead of forcing one editor or one runtime ships with a local viewer so you can inspect what the system remembers Why people care The repo has already crossed 2.8k+ GitHub stars, and the pitch is easy to understand: fewer wasted tokens, less repeated explanation, and better recall across long coding projects. From the project’s own benchmark material: 95.2% R@5 on retrieval-only LongMemEval-S 92% fewer input tokens per session is the headline claim in the README/site internal quality docs show a drop from 22,610 tokens with built-in memory/grep to 3,142 tokens for retrieved results in one 240-observation evaluation at 1,000 observations, the project argues most static built-in memory becomes effectively invisible while searchable memory still covers the full corpus Security and privacy read This looks stronger than many “memory for agents” projects on the privacy front, but there are still a few things worth saying plainly: good: self-hosted by default, no external database stack required good: Apache-2.0 licensed and openly benchmarked with reproducibility docs in the repo good: the comparison docs explicitly claim secret/privacy filtering before storage and audit trails for mutations good: the project publishes a real security policy with private reporting channels and version support guidance watch out: memory is still stored locally on disk, so sensitive prompts/tool outputs should be treated as sensitive local data watch out: peer-to-peer sync/federation and external model providers change the trust boundary immediately watch out: installation commonly starts with npx, and the repo also documents upgrade flows that can mutate the runtime/workspace intentionally Best use cases long-running Claude Code or Codex projects teams bouncing between multiple coding agents projects where architecture decisions get forgotten between sessions workflows that keep hitting /compact, memory caps, or context-window waste Why this is more than hype A lot of memory projects stop at “vector DB for chats.” agentmemory feels more practical because it combines: automatic capture hybrid retrieval cross-agent support local viewer + replay OpenClaw and Hermes integrations out of the box That combination is why this one is worth watching even if you are skeptical of benchmark marketing. Bottom line If you use Claude Code, Codex, Hermes, or OpenClaw heavily, agentmemory is one of the most credible open-source attempts so far to turn “agent memory” from a brittle text file into an actual system. Just keep the claim honest: the real breakthrough is not infinite magic memory — it is more durable, searchable memory with far better token efficiency and fewer context-window failures.
#OpenClaw#ChatGPT#Claude#Kimi#DeepSeek#Buyer Guide#AI Agents
If you care about OpenClaw + wallet efficiency, the answer is not one universal winner. It depends on whether you want flat monthly cost, cheap API scale, or lowest policy risk. Fast ranking Best for Pick Why --------- best overall for solo OpenClaw use ChatGPT subscription (Codex OAuth) officially supported in OpenClaw docs, no API key needed, best flat-cost path best cheap API backend Kimi / Moonshot strong OpenClaw support, large context, good coding/agent positioning best ultra-budget API experiments DeepSeek simple API path, broad agent-tool compatibility, low-cost usage style safest enterprise-style path OpenAI or Anthropic API key cleanest policy story and least auth ambiguity riskiest subscription path Claude Pro/Max via setup-token technically works, but OpenClaw docs explicitly warn Anthropic has blocked some outside-Claude-Code subscription usage before What to avoid Claude subscription as your main production path if you hate policy risk any provider choice based only on benchmark hype without checking auth/support posture expensive API-first setups if your real usage is mostly personal agent workflows that fit better under a flat subscription Best pick by user type Solo tinkerer / daily driver: ChatGPT subscription Builder chasing cheap API throughput: Kimi Experimenter on strict budget: DeepSeek Team / production / compliance-sensitive: API keys, not subscriptions
PicoClaw is a fascinating ultra-light agent project — but it is not a clean 1:1 OpenClaw replacement
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#PicoClaw#OpenClaw#AI Agents#Go#RISC-V#Self-Hosting
PicoClaw offers a lightweight AI agent experience built for diverse hardware, emphasizing compact design and broad architecture support. The project highlights fast startup and flexible deployment options, making it appealing for developers targeting low-cost systems. Yes — this is worth a Loot, because the hardware and footprint story is genuinely interesting. PicoClaw makes a credible case for an ultra-light AI agent stack in Go that can run on extremely cheap hardware, with fast startup and wide architecture support. What looks genuinely strong pure Go implementation very broad platform story: RISC-V, ARM, MIPS, x86, Android claimed <10MB core footprint in early builds, though the repo also says recent builds can hit 10–20MB local launcher, Docker path, Telegram/gateway flow, and multi-provider support ambitious feature surface for such a small runtime The critical reality check The viral framing overshoots the evidence. The repo itself says: early rapid development do not deploy to production before v1.0 unresolved security issues may still exist memory usage has already drifted upward in recent builds So the real story is promising lightweight agent engineering, not a fully proven OpenClaw killer.
#NVIDIA#AI Models#API#Free Tools#Developer Workflow#OpenClaw
This resource highlights how to access a broad set of NVIDIA-hosted AI models with your own API key. It is useful for builders comparing free model access, hosted inference options, and practical experimentation routes. A compact workflow for trying Nvidia-hosted AI models for free while the offer is available. This is useful if you want to test models like GLM, Kimi, or DeepSeek from your IDE or your OpenClaw setup without building the integration from scratch. Quick setup Best use cases quick model comparison testing API-based coding workflows prototyping with hosted inference wiring models into IDEs like Cursor or similar tools experimenting inside an OpenClaw instance Compact takeaway If you want a low-friction way to try a broad range of current AI models, Nvidia Build is a strong shortcut: create an account, generate a key, copy the example code, and plug it into your workflow.
Blog
Articles in OpenClaw
5/16/20266 min
Why OpenClaw 2026.5.12 Feels Like a Bigger Deal Than a Normal Update
OpenClaw 2026.5.12 is not just another feature drop. It sharpens the runtime boundary around OpenAI agent turns, makes ChatGPT subscription-backed setup more practical, and moves the platform closer to cleaner agent architecture.
5/4/20267 min
OpenAI opening ChatGPT subscriptions to OpenClaw-style agents is a much bigger move than it looks
This is not just another login update. It may be the first serious attempt to turn a mainstream AI subscription into the default intelligence layer for autonomous open-source agents.
5/2/20267 min
Ling-2.6-1T is making a serious case for useful intelligence per token
Ling-2.6-1T is not just another open model launch. Its trillion-parameter scale, execution-first positioning, and lower-token-overhead strategy make it especially relevant for builders running agents and real production workflows.
4/26/20269 min
DeepSeek V4 vs OpenAI vs Anthropic: The New Cost-Performance Shock in Frontier AI
DeepSeek V4 changes the AI buying conversation because it combines a 1M-token context window, OpenAI- and Anthropic-compatible APIs, tool calling, and sharply lower pricing. The real question is not whether it replaces OpenAI or Anthropic everywhere, but where it delivers the best value — and where its rivals still have the stronger product stack.