# Send CrabPath to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "crabpath", "name": "CrabPath", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/jonathangu/crabpath", "canonicalUrl": "https://clawhub.ai/jonathangu/crabpath", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/crabpath", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=crabpath", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "CHANGELOG.md", "CONTRIBUTING.md", "README.md", "REPRODUCE.md", "SKILL.md", "benchmarks/external/README.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/crabpath" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/crabpath", "downloadUrl": "https://openagent3.xyz/downloads/crabpath", "agentUrl": "https://openagent3.xyz/skills/crabpath/agent", "manifestUrl": "https://openagent3.xyz/skills/crabpath/agent.json", "briefUrl": "https://openagent3.xyz/skills/crabpath/agent.md" } } ``` ## Documentation ### CrabPath Pure graph core: zero required deps and no network calls. Caller provides callbacks. ### Design Tenets No network calls in core No secret discovery (no dotfiles, keychain, or env probing) No subprocess provider wrappers Embedder identity in state metadata; dimension mismatches are errors One canonical state format (state.json) ### Quick Start from crabpath import split_workspace, HashEmbedder, VectorIndex graph, texts = split_workspace("./workspace") embedder = HashEmbedder() index = VectorIndex() for nid, content in texts.items(): index.upsert(nid, embedder.embed(content)) ### Embeddings and LLM callbacks Default: HashEmbedder (hash-v1, 1024-dim) Real: callback embed_fn / embed_batch_fn (e.g., text-embedding-3-small) LLM routing: callback llm_fn using gpt-5-mini (example) ### Session Replay replay_queries(graph, queries) can warm-start from historical turns. ### CLI --state is preferred: crabpath query TEXT --state S [--top N] [--json] crabpath query TEXT --state S --chat-id CID crabpath doctor --state S crabpath info --state S crabpath init --workspace W --output O --embedder openai crabpath query TEXT --state S --llm openai crabpath inject --state S --type TEACHING [--type DIRECTIVE] Real-time correction flow: python3 query_brain.py --chat-id CHAT_ID python3 learn_correction.py --chat-id CHAT_ID ### Quick Reference crabpath init/query/learn/inject/health/doctor/info query_brain.py --chat-id and learn_correction.py for real-time correction pipelines query_brain.py traversal limits: beam_width=8, max_hops=30, fire_threshold=0.01 Hard traversal caps: max_fired_nodes and max_context_chars (defaults None; query_brain.py defaults max_context_chars=20000) examples/correction_flow/, examples/cold_start/, examples/openai_embedder/ ### API Reference Core lifecycle: split_workspace load_state save_state ManagedState VectorIndex Traversal and learning: traverse TraversalConfig TraversalConfig.beam_width, .max_hops, .fire_threshold, .max_fired_nodes, .max_context_chars, .reflex_threshold, .habitual_range, .inhibitory_threshold TraversalResult apply_outcome Runtime injection APIs: inject_node inject_correction inject_batch Maintenance helpers: suggest_connections, apply_connections suggest_merges, apply_merge measure_health, autotune, replay_queries Embedding utilities: HashEmbedder OpenAIEmbedder default_embed default_embed_batch openai_llm_fn LLM routing callbacks: chat_completion Graph primitives: Node Edge Graph split_workspace generate_summaries ### CLI Commands crabpath init --workspace W --output O [--sessions S] [--embedder openai] crabpath query TEXT --state S [--top N] [--json] [--chat-id CHAT_ID] crabpath learn --state S --outcome N --fired-ids a,b,c [--json] crabpath inject --state S --id NODE_ID --content TEXT [--type CORRECTION|TEACHING|DIRECTIVE] [--json] [--connect-min-sim 0.0] crabpath inject --state S --id NODE_ID --content TEXT --type TEACHING crabpath inject --state S --id NODE_ID --content TEXT --type DIRECTIVE crabpath health --state S crabpath doctor --state S crabpath info --state S crabpath replay --state S --sessions S crabpath merge --state S [--llm openai] crabpath connect --state S [--llm openai] crabpath journal [--stats] query_brain.py --chat-id CHAT_ID learn_correction.py --chat-id CHAT_ID ### Traversal defaults beam_width=8 max_hops=30 fire_threshold=0.01 reflex_threshold=0.6 habitual_range=0.2-0.6 inhibitory_threshold=-0.01 max_fired_nodes (hard node-count cap, default None) max_context_chars (hard context cap, default None; query_brain.py default is 20000) ### Paper https://jonathangu.com/crabpath/ ## Trust - Source: tencent - Verification: Indexed source record - Publisher: jonathangu - Version: 11.2.1 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-30T16:55:25.780Z - Expires at: 2026-05-07T16:55:25.780Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/crabpath) - [Send to Agent page](https://openagent3.xyz/skills/crabpath/agent) - [JSON manifest](https://openagent3.xyz/skills/crabpath/agent.json) - [Markdown brief](https://openagent3.xyz/skills/crabpath/agent.md) - [Download page](https://openagent3.xyz/downloads/crabpath)