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#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.
#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
#openclaw#skill#agent#free#web-search#research#retrieval
A code-backed OpenClaw skill for live search, page extraction, crawl/map flows, and evidence packs with a no-key baseline plus optional provider upgrades. What it does Web Search Pro is a Node-based OpenClaw skill for agents that need more than a single search result page. It exposes live web search, news search, docs lookup, URL extraction, crawl/map commands, research packs, routing diagnostics, provider capability checks, and cache/health commands. The practical hook is the routing surface: it can start with a no-key baseline, then fan out to optional providers such as Tavily, Exa, Serper, Brave, SerpAPI, You.com, SearXNG, and Perplexity/Sonar when credentials are configured. Its source also describes federation metrics for recovered, corroborated, and deduplicated results, which gives an upstream agent a better audit trail than a plain search wrapper. Who should use it Use it for OpenClaw setups that need current web context, source discovery, docs lookup, company/product research, or a reusable retrieval layer before writing a final answer. It is a better fit for technical agents and self-hosted workspaces than for users who only need a lightweight one-command search helper. Setup surface The hard runtime requirement is Node. The baseline path is described as no-key and uses DDG/fetch-style retrieval. Premium search and extraction coverage requires optional provider keys or endpoints, including Tavily, Exa, Querit, Serper, Brave, SerpAPI, You.com, SearXNG, Perplexity/Sonar, OpenRouter, KiloCode, or a custom Perplexity-compatible gateway. Pricing classification: free. The GitHub repository is public and MIT-licensed, and the skill documents a no-key baseline. Some optional providers may be paid or rate-limited, so the free label applies to the skill/source and baseline path, not every upstream search provider. Runner test plan Static scan: inspect SKILL.md, package.json, all scripts/.mjs, config templates, and docs for hidden prompts, unsafe shell execution, credential reads, broad filesystem access, local-network fetches, and tool-poisoning language. Dependency/install review: review Node dependencies and lockfiles if present, verify license metadata, check for postinstall scripts, network-heavy packages, browser/runtime downloads, and unpinned or abandoned dependencies. Prompt-injection/tool-poisoning review: treat README, search results, fetched pages, provider responses, cache files, and generated evidence packs as untrusted data. Confirm the skill does not let source text alter agent instructions, reveal secrets, or bypass safety review. Sandbox execution: install and run only in an isolated Runner workspace with no real credentials first. Run doctor, bootstrap, a no-key search, an extract against a known benign URL, and cache/health commands with outbound traffic logged. Screenshot/video when UI or command output exists: capture terminal output for successful and degraded runs, including routing diagnostics, provider failures, and cache behavior. Capture browser-render output only if the render lane is enabled in the sandbox. Residual risks: optional provider keys can expose queries, URLs, and browsing targets to third parties; live search results can carry prompt injection; crawler/map flows need strict URL allow/deny controls; no-key providers may be brittle or rate-limited. Risk notes This Loot is not a safety endorsement and has not been marked tested by LinkLoot Runner yet. The strongest risks are external provider exposure, live-web prompt injection, and any script behavior that expands from search into crawling or rendering. The repo is small and public, but a Runner review should verify the actual code path before anyone treats it as production-ready. Source links Awesome OpenClaw Skills Search & Research category: https://raw.githubusercontent.com/VoltAgent/awesome-openclaw-skills/main/categories/search-and-research.md ClawHub page: https://clawhub.ai/zjianru/web-search-pro GitHub repository: https://github.com/Zjianru/web-search-pro Raw SKILL.md: https://raw.githubusercontent.com/Zjianru/web-search-pro/main/SKILL.md
#openclaw#skill#agent#free#cost-control#cron#model-governance#runner-review
Agent Audit is a read-only OpenClaw skill candidate for mapping agents, cron jobs, model tiers, token usage, and cost-risk mismatches. Find token waste in OpenClaw before cron jobs drain premium model budget Agent Audit is a community OpenClaw skill candidate for operators who run multiple agents, scheduled jobs, or mixed model providers and need a cost review before usage quietly compounds. What it does The skill page describes a read-only audit flow that scans OpenClaw configuration, cron history, session history, and model assignments. Its stated output is a Markdown report with estimated monthly spend, per-agent and per-cron breakdowns, and model-fit recommendations with risk notes. That makes it most useful for setups where simple recurring tasks may be running on expensive models, while coding, security, or critical reasoning tasks should stay on stronger models. Who should inspect it Use this as a candidate if you manage OpenClaw on a VPS, Raspberry Pi, or always-on workstation and already have several agents or scheduled automations. It is less useful for a single-agent install with little run history. Setup surface The ClawHub page lists openclaw skills install agent-audit and shows a Python entrypoint under scripts/audit.py. Review the SKILL.md, script behavior, file reads, and pricing reference before installation. Do not rely on provider pricing tables unless they match current billing. Risk notes LinkLoot has not run this skill. Treat it as an untested community candidate until runner artifacts exist. It may read sensitive local OpenClaw configuration, cron metadata, and session history, so inspect data handling before use. Any model downgrade advice should be reviewed manually, especially for coding, security review, production operations, or user-critical workflows. Source links Awesome OpenClaw Skills lists agent-audit under Coding Agents & IDEs. Clawskills mirrors the public listing and summarizes the workflow. ClawHub hosts the registry page, install surface, SKILL.md content, version, license, and security status fields.
#openclaw#skill#agent#free#workflow#automation#runner-review
An OpenClaw skill candidate that bundles TODO/FIXME loop scans, parallel-vs-serial planning, file-size review, and subworkflow handoff into one local workflow surface. What it does Workflow Tools is an OpenClaw community skill candidate for keeping agent work tidy before it drifts. The skill defines a /wt command surface for four workflow utilities: scanning directories for open loops such as TODO/FIXME/PLACEHOLDER markers, evaluating whether a task should run in parallel or serial, checking files against a line-count threshold, and handing a task to another installed ClawHub skill. Pricing classification: free. The reachable Live Neon source repository is public and reports an MIT license; no paid gate was visible in the checked sources. Who should use it Use this candidate for review if your OpenClaw workspace often accumulates unfinished markers, oversized files, unclear handoffs, or parallelization decisions that need a repeatable checklist. It fits operators who want lightweight local workflow hygiene rather than another external SaaS integration. Setup surface The skill declares config files under .openclaw/workflow-tools.yaml and .claude/workflow-tools.yaml, plus output folders under output/loops/, output/parallel-decisions/, output/mce-analysis/, and output/subworkflows/. Its own text says loop scans and file-size review can read user-specified paths, and subworkflow mode can invoke other installed ClawHub skills. No installation or execution was performed on this Raspberry Pi. Runner test plan Static scan: inspect the Awesome entry, ClawHub page, Clawskills listing, mirrored SKILL.md, Live Neon source tree, raw SKILL.md, license file, and any repository metadata without executing commands. Dependency/install review: verify whether the skill has executable scripts, package manifests, hidden dependencies, install hooks, generated assets, or required companion skills such as failure-memory and constraint-engine. Prompt-injection/tool-poisoning review: check the SKILL.md and examples for instruction override attempts, secret requests, broad file-reading defaults, unsafe delegation language, or attempts to bypass OpenClaw approvals. Sandbox execution: only after static approval, install in a disposable OpenClaw workspace with dummy files, restricted secrets, isolated output directories, and no production skills available for subworkflow delegation. Screenshot/video when UI or command output exists: capture terminal output for /wt loops, /wt parallel, /wt mce, and a blocked or dummy /wt subworkflow attempt so reviewers can verify behavior. Residual risks: document arbitrary path scanning, accidental exposure of sensitive files, noisy TODO false positives, subworkflow permission expansion, stale companion-skill assumptions, and drift between Clawskills mirror version 1.4.0 and Live Neon source version 1.5.0. Risk notes This Loot is a review candidate, not a safety endorsement. Community skill text is untrusted input. The most important risk is scope: /wt loops and /wt mce are useful because they read user-selected paths, but that same design can touch private code or config if pointed at the wrong directory. Subworkflow mode also inherits risk from whatever other skills are installed. Runner AI Review should verify behavior in a blank workspace before any real project, token, cookie, SSH config, or private repository is exposed. Source links Awesome OpenClaw Skills category entry: https://raw.githubusercontent.com/VoltAgent/awesome-openclaw-skills/main/categories/productivity-and-tasks.md ClawHub page: https://clawhub.ai/leegitw/workflow-tools Clawskills listing: https://clawskills.sh/skills/leegitw-workflow-tools Clawskills SKILL.md mirror: https://clawskills.sh/skills-markdown/leegitw/workflow-tools.md Underlying Live Neon source tree: https://github.com/live-neon/skills/tree/main/agentic/workflow-tools Raw SKILL.md source: https://raw.githubusercontent.com/live-neon/skills/main/agentic/workflow-tools/SKILL.md License evidence: https://raw.githubusercontent.com/live-neon/skills/main/LICENSE
#openclaw#skill#agent#free#execution#security#governance#runner-review
An OpenClaw Runner-review candidate for separating agent proposals from approved execution, with replay protection, receipts, and offline signature checks. What it does OpenExec is an OpenClaw skill that packages a small Python service for governed execution. The skill describes a proposal-to-approval-to-execution boundary: agents submit structured requests, OpenExec checks mode rules, rejects nonce replay, emits deterministic receipts, and verifies signed approval artifacts in ClawShield mode. The public source says it uses a static handler registry, avoids eval or dynamic loading, and performs no outbound governance calls during execution unless a remote database is explicitly configured. Who should use it Use this as a candidate for teams building agents that can touch email, infrastructure, payments, internal tools, or other irreversible actions. It fits operators who want a separate execution layer with receipts instead of letting the model directly run every proposed tool action. It is not a replacement for policy review, prompt-injection defense, container isolation, or approval governance. Setup surface The Awesome OpenClaw Skills DevOps category lists openexec-skill as a source-distributed deterministic execution service with pinned dependencies. ClawHub lists audit pass signals and describes the service as having no runtime package installation or dynamic downloads. The source tree exposes SKILL.md, SECURITY.md, README.md, main.py, requirements, tests, scripts, and configuration folders. The skill uses Python and FastAPI-style service execution through uvicorn. Pricing evidence: SKILL.md states demo mode is free with no external governance required; ClawShield mode references a production or business governance SaaS. Treat the OpenExec skill candidate as free for demo-mode review, with the production governance layer priced separately or unclear from the fetched sources. Runner test plan Static scan: inspect SKILL.md, README.md, SECURITY.md, main.py, requirements, tests, scripts, config, and handler registry files. Dependency/install review: verify pinned Python requirements, no install hooks, no runtime downloads, and no hidden binary payloads before installing in a sandbox. Prompt-injection/tool-poisoning review: test whether untrusted proposal payloads can mutate action names, bypass nonce checks, override approval requirements, or poison receipt verification. Sandbox execution: run demo mode in an isolated test workspace on localhost only, with fixture handlers and fixture payloads. Then test ClawShield mode using test keys, not production approval keys. Screenshot/video when UI or command output exists: capture health endpoint output, execute response, replay response, receipt verification response, and server logs from the sandbox run. No browser UI is expected. Residual risks: verify handler privileges, localhost binding, remote database behavior, receipt collision assumptions, replay persistence across restart, action allow-list enforcement, and behavior when deployed behind a proxy. Risk notes This is not a tested recommendation yet. OpenExec is an execution boundary, not an OS sandbox. Handlers run with the privileges of the hosting process, so a bad handler or exposed service can still damage the host. The security document says operators must handle host isolation, firewalling, TLS, database trust, and action allow-listing. The fetched GitHub HTML confirms main.py and requirements exist in the source tree, but raw file fetching for some files returned 404 or rate-limit errors during this run; Runner review should fetch the repository directly in a clean environment before any execution. Source links Awesome OpenClaw Skills DevOps category: https://github.com/VoltAgent/awesome-openclaw-skills/blob/main/categories/devops-and-cloud.md Clawskills listing: https://clawskills.sh/skills/trendinghot-openexec-skill ClawHub page: https://clawhub.ai/trendinghot/openexec-skill Source tree: https://github.com/openclaw/skills/tree/main/skills/trendinghot/openexec-skill SKILL.md source page: https://github.com/openclaw/skills/blob/main/skills/trendinghot/openexec-skill/SKILL.md SECURITY.md source page: https://github.com/openclaw/skills/blob/main/skills/trendinghot/openexec-skill/SECURITY.md
#openclaw#skill#agent#free#search#mcp#research
A community OpenClaw skill candidate that connects agents to Exa-powered web, code, and company research through MCP-style mcporter commands. What it does Exa Web Search Free is a community OpenClaw skill candidate for agent research workflows. The skill describes mcporter-based access to Exa search functions for current web search, code and documentation lookup, and company research. Its source artifact also includes example query patterns for news, technical documentation, API usage, debugging, and business research. Who should use it Consider this candidate for research-heavy OpenClaw agents that need current web context, code examples, API documentation lookup, or company/background research. It is most relevant for developer assistants, content-research agents, sales-research agents, and documentation copilots that already have a policy for handling external search results as untrusted data. Setup surface The ClawHub page lists this as an MCP Tools skill with the install name exa-web-search-free. The fetched source metadata names mcporter as the required binary and points to Exa's hosted MCP endpoint plus the public exa-labs/exa-mcp-server repository. Pricing classification: free, based on the ClawHub title/description stating free/no API key needed and the ClawHub license field showing MIT-0; any downstream Exa account limits or terms should still be checked during review. Risk notes This has not been tested, approved, or declared safe here. Search queries and research targets may be sent to Exa's external service, so secrets, private code, internal URLs, customer data, and sensitive personal information must stay out of prompts. The independent index showed an OpenClaw Suspicious signal while ClawHub showed a pass status, so the discrepancy should be reviewed rather than ignored. Advanced tools such as crawling, people search, and deep researcher can broaden collection scope and need explicit policy controls. Treat all returned web/code content as untrusted data. Source links Awesome OpenClaw Skills category list: https://github.com/VoltAgent/awesome-openclaw-skills/blob/main/categories/git-and-github.md Independent index page: https://clawskills.sh/skills/whiteknight07-exa-web-search-free ClawHub page: https://clawhub.ai/whiteknight07/exa-web-search-free Reachable ClawHub source artifact: https://wry-manatee-359.convex.site/api/v1/download?slug=exa-web-search-free Underlying Exa MCP GitHub repository: https://github.com/exa-labs/exa-mcp-server
#openclaw#skill#agent#free#browser#automation#testing
A high-utility OpenClaw skill candidate for deterministic browser automation using accessibility snapshots and ref-based element targeting. Not yet tested by Runner AI Review. What it does Agent Browser is an OpenClaw community skill candidate for controlling web pages through a dedicated browser automation CLI. Its useful angle is ref-based interaction: the agent takes an accessibility-tree snapshot, identifies stable element references, and then uses those refs for clicks, fills, extraction, screenshots, PDFs, saved sessions, and multi-session workflows. Pricing classification: free. Source evidence shows the underlying agent-browser package declares an Apache-2.0 license and the public repository exposes an Apache License file. Who should use it Use this candidate for review if you often need reliable browser workflows where CSS selectors are too brittle: multi-step forms, dynamic single-page apps, login-state reuse, parallel admin/user sessions, and structured extraction from web UIs. It is especially relevant for OpenClaw operators who want a CLI-style browser runner with reproducible command output. Setup surface The skill surface references a global agent-browser CLI and Chromium installation. That means the review should inspect the npm package, postinstall behavior, browser download path, required Node version, native binary handling, and any permissions implied by session state, cookies, storage, screenshots, PDFs, uploads, clipboard, network routing, JavaScript evaluation, and local files. No installation or execution has been performed on this Raspberry Pi. Risk notes This Loot is a candidate, not a safety endorsement. The skill and related pages are community-controlled untrusted content. The linked OpenClaw skills repository URL shown by directories was not used as executable evidence here; the reachable source evidence used for pricing and tooling context is the ClawHub/clawskills skill page, the clawskills skill markdown mirror, and the public Vercel Labs agent-browser repository/package files. Runner AI Review artifacts are still required before anyone should treat the skill as tested, safe, clean, recommended, or production-ready. Source links Awesome OpenClaw Skills list: https://raw.githubusercontent.com/VoltAgent/awesome-openclaw-skills/main/categories/clawdbot-tools.md ClawHub page: https://clawhub.ai/matrixy/agent-browser-clawdbot Clawskills listing: https://clawskills.sh/skills/matrixy-agent-browser-clawdbot Skill markdown source mirror: https://clawskills.sh/skills-markdown/matrixy/agent-browser-clawdbot.md Underlying tool repository: https://github.com/vercel-labs/agent-browser Package/license evidence: https://raw.githubusercontent.com/vercel-labs/agent-browser/main/package.json and https://raw.githubusercontent.com/vercel-labs/agent-browser/main/LICENSE
#openclaw#skill#agent#free#provenance#security#workflow
A free OpenClaw community skill candidate for keeping Agent Skill bundles traceable with manifests, changelogs, SHA-256 hashes, and stale-file checks across chat, CLI, IDE, and registry workflows. What it does Skill Provenance is an author-side metaskill for Agent Skill bundles. It documents a portable MANIFEST.yaml, CHANGELOG.md, per-file version metadata, and SHA-256 hash checks so a skill's SKILL.md, evals, scripts, references, and packaged copies can be tracked across sessions and platforms. The upstream source describes it as free and open with an MIT license. Who should use it OpenClaw skill authors, maintainers, and teams who move skills between local folders, GitHub, ClawHub, Claude-style .skill packages, Codex/Gemini-compatible strict copies, or multiple agent sessions. It is most useful when bundle drift, stale evals, renamed files, or unclear handoffs are a recurring problem. Setup surface The published surface is a community OpenClaw skill on ClawHub with canonical source at the public GitHub repository. The bundle includes SKILL.md, README.md, MANIFEST.yaml, CHANGELOG.md, eval files, validate.sh, and package.sh according to the fetched manifest. Treat installation commands and scripts in the source as review material only until Runner AI Review finishes. Pricing evidence from the upstream GitHub README states it is free and open; license evidence points to MIT. Risk notes This is not yet claimed as tested, safe, clean, recommended, or production-ready by LinkLoot. The concept relies on local file inventory and hash checks, but the upstream source itself notes that a manifest is not a cryptographic signature or trust anchor. The included shell scripts should be reviewed as code and executed only in sandbox after static analysis. Because the skill is designed to edit manifests/changelogs and package derived copies, Runner should verify it does not mutate unrelated files, read broad home/config/SSH paths, or follow embedded source instructions beyond the user's explicit task. Source links Awesome OpenClaw Skills list: https://github.com/VoltAgent/awesome-openclaw-skills and category listing https://raw.githubusercontent.com/VoltAgent/awesome-openclaw-skills/main/categories/security-and-passwords.md ClawHub page: https://clawhub.ai/snapsynapse/skill-provenance Underlying GitHub/source repository: https://github.com/snapsynapse/skill-provenance Source SKILL.md: https://raw.githubusercontent.com/snapsynapse/skill-provenance/main/skill-provenance/SKILL.md Source manifest: https://raw.githubusercontent.com/snapsynapse/skill-provenance/main/skill-provenance/MANIFEST.yaml
#openclaw#skill#agent#free#security#secrets#gitguardian#ggshield
A community OpenClaw skill candidate that wraps GitGuardian ggshield so an agent can scan repositories, staged changes, files, and Docker images for leaked credentials before code is pushed. What it does The ggshield-scanner skill gives an OpenClaw-style agent a natural-language surface for GitGuardian's ggshield CLI. The source describes repository scans, single-file scans, staged-change checks, optional git hook installation, and Docker image scans for hardcoded secrets such as API keys, cloud credentials, private keys, OAuth tokens, and database passwords. Who should use it Developers, solo builders, and security-conscious agent operators who want an agent-assisted secret check before commits, pushes, releases, or Docker image handoff. It is especially useful for teams that already accept GitGuardian/ggshield in their workflow and want the agent to orchestrate checks rather than manually remembering every command. Setup surface The source indicates a Python-based skill that depends on ggshield and pygitguardian, requires a GitGuardian API key via GITGUARDIANAPIKEY, and calls the local ggshield binary. The public GitHub source is reachable, but the ClawHub/awesome-list OpenClaw tree link appears inconsistent with the reachable repository, so provenance should be reviewed carefully before any install. Pricing evidence in the source says GitGuardian signup is free, with enterprise/on-premise options mentioned separately; classify this Loot as free with that caveat. Risk notes Do not install or run directly on a production Raspberry Pi or personal workspace before Runner review artifacts exist. The implementation shown uses subprocess calls to ggshield with argument arrays rather than shell=True, which is a good sign, but it still executes a local binary and can scan sensitive paths if the agent is allowed to choose broad inputs. The hook installer changes git repository state. Review privacy claims against current GitGuardian documentation before scanning private code. Source links Awesome OpenClaw Skills list: https://github.com/VoltAgent/awesome-openclaw-skills Awesome category entry: https://raw.githubusercontent.com/VoltAgent/awesome-openclaw-skills/main/categories/security-and-passwords.md ClawHub page: https://clawhub.ai/amascia-gg/ggshield-scanner Reachable source repository: https://github.com/GitGuardian/ggshield-skill Source SKILL.md: https://raw.githubusercontent.com/GitGuardian/ggshield-skill/main/SKILL.md
#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.
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.
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