# Send RALSTP Consultant 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. 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. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "ralstp-consultant", "name": "RALSTP Consultant", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/thedragosexperience/ralstp-consultant", "canonicalUrl": "https://clawhub.ai/thedragosexperience/ralstp-consultant", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/ralstp-consultant", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=ralstp-consultant", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "scripts/analyze.py" ], "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/ralstp-consultant" }, "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/ralstp-consultant", "downloadUrl": "https://openagent3.xyz/downloads/ralstp-consultant", "agentUrl": "https://openagent3.xyz/skills/ralstp-consultant/agent", "manifestUrl": "https://openagent3.xyz/skills/ralstp-consultant/agent.json", "briefUrl": "https://openagent3.xyz/skills/ralstp-consultant/agent.md" } } ``` ## Documentation ### RALSTP Consultant Based on "Recursive Agents and Landmarks Strategic-Tactical Planning (RALSTP)" by Dorian Buksz, King's College London, 2024. ### 1. Agents Identification Definition: Agents are objects with dynamic types that are active during goal state search. How to identify: Dynamic type = appears as first argument of a predicate in any action's effects Static type = never appears in action effects Example: In Driverlog, truck and driver are dynamic (they're in drive action effects), but location is static Real PDDL Example (RTAM Domain): (:types ambulance police_car tow_truck fire_brigade - vehicle acc_victim vehicle car - subject ... ) Agents: ambulance, police_car, tow_truck, fire_brigade (appear in action effects like at, available, busy) Passive: acc_victim, car (acted upon but don't act) ### 2. Passive Objects Objects that are NOT agents — things being acted upon but don't act themselves. Packages, cargo, data, files, victims in RTAM ### 3. Agent Dependencies Definition: Relationships between agents based on what preconditions they satisfy for other agents. Types: Independent — agents that don't depend on each other Dependent — agents that need other agents' preconditions satisfied Conflicting — agents that interfere with each other ### 4. Entanglement Definition: When agents fight for shared resources (time, space, locations, etc.) Measurement: Count of shared predicates Conflict frequency in goal states Real PDDL Example (RTAM - Road Traffic Accident): (:durative-action confirm_accident :parameters (?V - police_car ?P - subject ?A - accident_location) :condition (and (at start (at ?V ?A)) (at start (at ?P ?A)) ...) :effect (and (at end (certified ?P)) ...) ) (:durative-action untrap :parameters (?V - fire_brigade ?P - acc_victim ?A - accident_location) :condition (and (at start (certified ?P)) (at start (available ?V)) ...) ) Entanglement: police_car must certify BEFORE fire_brigade can untrap Resource conflict: Both need to be at same accident_location Availability: fire_brigade busy during untrap → others must wait ### 5. Landmarks Definition: Facts that must be true in any valid plan (from goals back to initial state). Types: Fact landmarks — propositions that must hold Action landmarks — actions that must be executed Relaxed landmarks — landmarks considering only positive effects (ignoring deletes) Real PDDL Example (RTAM - sequential dependencies): Goal: (delivered victim1) ∧ (delivered car1) Required sequence of fact landmarks: 1. (certified victim1) ← police must confirm 2. (untrapped victim1) ← fire must free them 3. (aided victim1) ← ambulance must treat 4. (loaded victim1 ambulance) ← ambulance must load 5. (at victim1 hospital) ← deliver to hospital 6. (delivered victim1) ← FINAL Action landmarks: - confirm_accident → untrap → first_aid → load_victim → unload_victim → deliver_victim ### 6. Strategic vs Tactical Strategic: Abstract planning level. Solve "what needs to happen first" ignoring details. Tactical: Detailed execution level. Solve "exactly how to do it". ### 7. Difficulty Metrics From the thesis, difficulty increases with: More agents in goal state More entangled agents (conflicting dependencies) More inactive dynamic objects not in goal Buksz Complexity Score ≈ Agent Count × Entanglement Factor ### Implementation Note (Natural Language vs PDDL) This skill operates in two modes: Conceptual Mode (Default): Uses the LLM to apply RALSTP methodology to natural language problems (e.g., "Plan a marketing launch"). No PDDL files are required. The agent identifies Agents/Landmarks conceptually. Formal Mode (Optional): If you provide PDDL domain/problem files, the included scripts/analyze.py can be run to mathematically extract agents and landmarks. The instructions below apply to both modes, but "Real PDDL Examples" are provided for technical context. ### Usage For any complex problem, just describe it and I'll apply RALSTP: RALSTP analyze: I need to migrate 1000 VMs from datacentre A to B with minimal downtime ### Output Format ## RALSTP Analysis ### Agents Identified - [list agents and their types] ### Passive Objects - [list objects being acted upon] ### Dependency Graph - [which agents depend on which] ### Difficulty Assessment - Agent Count: X - Entanglement: Low/Medium/High - Estimated Complexity: [score] ### Strategic Phase - [high-level plan ignoring details] ### Tactical Phase - [detailed execution] ### Decomposition Suggestion - Split by: [agent type / landmark / location] - Parallelize: [what can run concurrently] - Risks: [potential conflicts/entanglements] ### When to Use USE for: Multi-step workflows with multiple actors Migration/tasks with dependencies Resource contention problems Complex orchestrations SKIP for: Simple Q&A Single-task problems ### Reference PhD Thesis: "Recursive Agents and Landmarks Strategic-Tactical Planning (RALSTP)" — Dorian Buksz, King's College London, 2024. ### Example: RTAM Domain (IPC-2014) Domain: Road Traffic Accident Management Source: https://github.com/potassco/pddl-instances/tree/master/ipc-2014/domains/road-traffic-accident-management-temporal-satisficing ### Full Analysis Agents (4): ambulance — transports victims to hospital police_car — certifies accident/victims tow_truck — recovers vehicles fire_brigade — untraps victims, extinguishes fires Passive Objects: acc_victim — people needing help car — vehicles involved in accident accident_location, hospital, garage Dependencies (Critical Path): police_car → fire_brigade → ambulance → hospital ↓ ↓ ↓ certify untrap deliver Landmarks Chain (must execute in order): confirm_accident (police at scene) untrap (fire frees victim) first_aid (ambulance treats) load_victim → unload_victim → deliver_victim load_car → unload_car → deliver_vehicle Entanglement: Multiple vehicles must be at same location (accident scene) Vehicles have limited availability (busy during actions) Sequence constraints: can't deliver before certify Difficulty: High — 4 agents, tight dependencies, shared locations ## Trust - Source: tencent - Verification: Indexed source record - Publisher: thedragosexperience - Version: 1.0.1 ## 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/ralstp-consultant) - [Send to Agent page](https://openagent3.xyz/skills/ralstp-consultant/agent) - [JSON manifest](https://openagent3.xyz/skills/ralstp-consultant/agent.json) - [Markdown brief](https://openagent3.xyz/skills/ralstp-consultant/agent.md) - [Download page](https://openagent3.xyz/downloads/ralstp-consultant)