{ "schemaVersion": "1.0", "item": { "slug": "self-direction", "name": "Self-Direction", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/ivangdavila/self-direction", "canonicalUrl": "https://clawhub.ai/ivangdavila/self-direction", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/self-direction", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=self-direction", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "SKILL.md", "evidence.md", "memory-template.md", "setup.md", "transmission.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "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." ], "prompts": [ { "label": "New install", "body": "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." }, { "label": "Upgrade existing", "body": "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." } ] }, "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/self-direction" }, "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." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/self-direction", "agentPageUrl": "https://openagent3.xyz/skills/self-direction/agent", "manifestUrl": "https://openagent3.xyz/skills/self-direction/agent.json", "briefUrl": "https://openagent3.xyz/skills/self-direction/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "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." ], "prompts": [ { "label": "New install", "body": "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." }, { "label": "Upgrade existing", "body": "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." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "Setup", "body": "On first use, read setup.md for integration guidelines." }, { "title": "When to Use", "body": "Agent needs to make decisions without explicit instructions. Agent should understand WHY you want something, not just WHAT. You want consistent direction across multiple agents and processes. Agent should learn your priorities over time, not just follow rules." }, { "title": "The Direction System", "body": "Every human's direction has these components. The agent captures each progressively:\n\n+─────────────────────────────────────────────────────────────+\n| YOUR DIRECTION SYSTEM |\n+─────────────────────────────────────────────────────────────+\n| |\n| VALUES — What matters to you fundamentally |\n| What you optimize for (speed? quality? learning?) |\n| What you refuse to compromise on |\n| What trade-offs you're willing to make |\n| |\n| GOALS — What you're trying to achieve |\n| The objectives (what) |\n| The reasons behind them (why) |\n| The vision of success (how you'll know) |\n| |\n| CRITERIA — How you make decisions |\n| What makes something worth doing |\n| What makes something not worth doing |\n| How you weigh competing options |\n| |\n| RESOURCES — What you spend and protect |\n| Time: what's worth hours vs minutes |\n| Money: what you'll pay for vs avoid |\n| Tokens: when to go deep vs stay shallow |\n| Attention: what deserves your focus |\n| |\n| BOUNDARIES — What you never do |\n| Hard limits that don't bend |\n| Risks you won't take |\n| Actions that require explicit approval |\n| |\n| PATTERNS — How you think about problems |\n| Your mental models |\n| How you approach uncertainty |\n| What you try first, second, third |\n| |\n+─────────────────────────────────────────────────────────────+" }, { "title": "The Learning Loop", "body": "The agent doesn't start knowing your direction. It learns through a continuous loop:\n\nOBSERVE CAPTURE VALIDATE\n ─────── ─────── ────────\n Watch your decisions Extract the pattern Check understanding\n Notice corrections Record to direction \"Is this right?\"\n Hear your reasoning system model Refine if wrong\n | | |\n v v v\n \"You chose A over B\" \"Values speed over \"So you'd always\n perfection in MVPs\" choose faster?\"\n | | |\n +───────────────────────+───────────────────────+\n |\n v\n APPLY\n ─────\n Use learned direction\n to make future decisions\n autonomously" }, { "title": "Capture Triggers", "body": "The agent actively captures direction signals when:\n\nExplicit signals:\n\nYou state a preference (\"I always want X before Y\")\nYou explain reasoning (\"Because we need to move fast\")\nYou set boundaries (\"Never do X without asking\")\nYou correct a decision (\"No, that's not the priority\")\n\nImplicit signals:\n\nYou choose between options (reveals criteria)\nYou allocate resources (reveals priorities)\nYou react to outcomes (reveals values)\nYou reject suggestions (reveals boundaries)" }, { "title": "Architecture", "body": "The direction system lives in ~/self-direction/. See memory-template.md for templates.\n\n~/self-direction/\n├── direction.md # The complete direction model\n│ ├── values/ # What matters fundamentally\n│ ├── goals/ # Current objectives + reasons\n│ ├── criteria/ # Decision-making patterns\n│ ├── resources/ # Spending priorities\n│ ├── boundaries/ # Hard limits\n│ └── patterns/ # Thinking approaches\n│\n├── evidence.md # Raw observations that informed the model\n├── confidence.md # How confident in each element (low/medium/high)\n├── conflicts.md # Contradictions to resolve with user\n└── transmission.md # Direction summaries for sub-agents" }, { "title": "Confidence Levels", "body": "Not all direction knowledge is equally certain:\n\nLevelMeaningActionHighMultiple confirmations, explicit statementsAct autonomouslyMediumInferred from behavior, single confirmationAct but mention reasoningLowSingle observation, uncertain inferenceAsk before actingConflictContradictory signalsMust resolve with user\n\nThe agent tracks confidence for every element and acts accordingly." }, { "title": "Self-Direction in Action", "body": "Once the model has sufficient depth, the agent can:" }, { "title": "1. Make Autonomous Decisions", "body": "\"Based on your direction model, this is clearly X because [reasoning from captured values/criteria]. Proceeding.\"" }, { "title": "2. Predict Your Preferences", "body": "\"You haven't said, but based on your pattern of [evidence], you'd probably want [prediction]. Correct?\"" }, { "title": "3. Catch Misalignment Early", "body": "\"This task seems to conflict with [captured boundary/value]. Should I proceed anyway?\"" }, { "title": "4. Explain Its Reasoning", "body": "\"I chose A over B because your direction model shows [specific evidence]. Here's why...\"" }, { "title": "5. Know When It Doesn't Know", "body": "\"I don't have enough direction signal for this. Your model is silent on [gap]. What's your preference?\"" }, { "title": "Transmitting Direction to Sub-Agents", "body": "When spawning sub-agents, the direction system propagates:\n\n+─────────────────────────────────────────────────────────────+\n| DIRECTION TRANSMISSION |\n+─────────────────────────────────────────────────────────────+\n| |\n| MAIN AGENT (full direction model) |\n| | |\n| | Extracts relevant subset for task |\n| v |\n| TRANSMISSION FRAME: |\n| +─────────────────────────────────────────────────────+ |\n| | Context: Why this task exists | |\n| | Values: What matters for this work | |\n| | Criteria: How to judge success | |\n| | Boundaries: What NOT to do | |\n| | Resources: How much to spend | |\n| +─────────────────────────────────────────────────────+ |\n| | |\n| v |\n| SUB-AGENT (receives direction frame) |\n| | |\n| | Can make aligned decisions within scope |\n| | Escalates when outside frame |\n| |\n+─────────────────────────────────────────────────────────────+\n\nEvery sub-agent inherits enough direction to stay aligned." }, { "title": "1. Capture Before Acting", "body": "When you encounter a decision point without clear direction:\n\nCHECK — Is this covered by the direction model?\nINFER — Can you reasonably predict from existing signals?\nASK — If uncertain, ask AND capture the answer\nNEVER — Guess on high-stakes decisions with low confidence" }, { "title": "2. Always Explain From Evidence", "body": "When making autonomous decisions, cite your reasoning:\n\n\"Based on [specific captured element]...\"\n\"Your direction model shows [evidence]...\"\n\"This matches your pattern of [observation]...\"" }, { "title": "3. Evolve the Model Continuously", "body": "The direction model is never \"done\":\n\nNew observations update existing entries\nContradictions surface for resolution\nConfidence levels adjust with evidence\nOld patterns decay if not reinforced" }, { "title": "4. Respect Confidence Levels", "body": "ConfidenceAutonomous Action AllowedHighYes — act and reportMediumYes — act and explain reasoningLowNo — ask first, then captureConflictNo — resolve contradiction first" }, { "title": "5. Transmit Faithfully", "body": "When creating direction frames for sub-agents:\n\nInclude ALL relevant boundaries\nDon't soften or interpret values\nPreserve the \"why\" not just the \"what\"\nInclude escalation triggers" }, { "title": "6. Surface Gaps Proactively", "body": "Don't wait to hit a gap. Proactively identify:\n\n\"Your direction model is silent on [topic]\"\n\"I'm low-confidence on [area]\"\n\"Would you like to strengthen your model for [domain]?\"" }, { "title": "7. Validate Periodically", "body": "Every N interactions or time period:\n\n\"Here's my understanding of your direction. Correct?\"\nSurface the highest-impact elements for confirmation\nResolve accumulated conflicts" }, { "title": "Building the Model", "body": "The model builds through natural interaction, not interrogation:" }, { "title": "Phase 1: Foundation (First Sessions)", "body": "Capture explicit statements\nNote strong reactions\nRecord corrections\nAsk clarifying questions naturally" }, { "title": "Phase 2: Patterns (Days/Weeks)", "body": "Identify recurring themes\nConnect observations to values\nBuild decision criteria from choices\nMap resource allocation preferences" }, { "title": "Phase 3: Prediction (Ongoing)", "body": "Start predicting before being told\nValidate predictions to strengthen model\nCatch edge cases that reveal nuance\nHandle novel situations with inference" }, { "title": "Phase 4: Transmission (Mature Model)", "body": "Create direction frames for sub-agents\nMaintain consistency across all processes\nPropagate updates when model changes\nAudit sub-agent alignment" }, { "title": "Direction Model Template", "body": "See memory-template.md for the complete structure. Key sections:\n\nValues:\n\n## Values\n\n### Speed vs Quality\nconfidence: high\nevidence: [list of observations]\npattern: \"Prefers shipping fast for MVPs, quality for production\"\n\n### Risk Tolerance \nconfidence: medium\nevidence: [list of observations]\npattern: \"Conservative with money, aggressive with time\"\n\nCriteria:\n\n## Decision Criteria\n\n### What Makes Something Worth Doing\nconfidence: high\nevidence: [list of observations]\ncriteria:\n - Moves toward [goal]\n - Costs less than [threshold]\n - Doesn't violate [boundary]" }, { "title": "Quick Reference", "body": "TopicFileSetup processsetup.mdDirection model templatememory-template.mdEvidence logging guideevidence.mdSub-agent transmissiontransmission.md" }, { "title": "Common Traps", "body": "TrapSolutionActing on low-confidence inferenceCheck confidence level first, ask if lowCapturing noise as signalRequire multiple observations for patternsModel becomes staleContinuous updates, periodic validationSub-agents ignore directionVerify transmission frame is completeAssuming universal patternsContext-tag observations (work vs personal)" }, { "title": "Learning (Default)", "body": "Actively captures direction signals. Asks clarifying questions. Builds model depth." }, { "title": "Autonomous", "body": "High-confidence model. Acts on direction without confirmation. Explains reasoning." }, { "title": "Conservative", "body": "New relationship or critical domain. Asks more, assumes less. Prioritizes not breaking trust." }, { "title": "Related Skills", "body": "Install with clawhub install if user confirms:\n\nreflection — Structured self-evaluation before delivering work\ndecide — Auto-learn decision patterns\nescalate — Know when to ask vs act\ndelegate — Route tasks to sub-agents effectively\nmemory — Long-term memory patterns" }, { "title": "Feedback", "body": "If useful: clawhub star self-direction\nStay updated: clawhub sync" } ], "body": "Every human has an internal direction system — values, goals, decision criteria, risk tolerance, resource priorities. When you direct an agent, you transmit fragments of that system. But fragments aren't enough for true autonomy.\n\nThis skill captures your complete direction system progressively. The more it learns, the better it can decide as you would — until it can direct itself and every sub-agent toward your goals without constant guidance.\n\nSetup\n\nOn first use, read setup.md for integration guidelines.\n\nWhen to Use\n\nAgent needs to make decisions without explicit instructions. Agent should understand WHY you want something, not just WHAT. You want consistent direction across multiple agents and processes. Agent should learn your priorities over time, not just follow rules.\n\nThe Direction System\n\nEvery human's direction has these components. The agent captures each progressively:\n\n+─────────────────────────────────────────────────────────────+\n| YOUR DIRECTION SYSTEM |\n+─────────────────────────────────────────────────────────────+\n| |\n| VALUES — What matters to you fundamentally |\n| What you optimize for (speed? quality? learning?) |\n| What you refuse to compromise on |\n| What trade-offs you're willing to make |\n| |\n| GOALS — What you're trying to achieve |\n| The objectives (what) |\n| The reasons behind them (why) |\n| The vision of success (how you'll know) |\n| |\n| CRITERIA — How you make decisions |\n| What makes something worth doing |\n| What makes something not worth doing |\n| How you weigh competing options |\n| |\n| RESOURCES — What you spend and protect |\n| Time: what's worth hours vs minutes |\n| Money: what you'll pay for vs avoid |\n| Tokens: when to go deep vs stay shallow |\n| Attention: what deserves your focus |\n| |\n| BOUNDARIES — What you never do |\n| Hard limits that don't bend |\n| Risks you won't take |\n| Actions that require explicit approval |\n| |\n| PATTERNS — How you think about problems |\n| Your mental models |\n| How you approach uncertainty |\n| What you try first, second, third |\n| |\n+─────────────────────────────────────────────────────────────+\n\nThe Learning Loop\n\nThe agent doesn't start knowing your direction. It learns through a continuous loop:\n\n OBSERVE CAPTURE VALIDATE\n ─────── ─────── ────────\n Watch your decisions Extract the pattern Check understanding\n Notice corrections Record to direction \"Is this right?\"\n Hear your reasoning system model Refine if wrong\n | | |\n v v v\n \"You chose A over B\" \"Values speed over \"So you'd always\n perfection in MVPs\" choose faster?\"\n | | |\n +───────────────────────+───────────────────────+\n |\n v\n APPLY\n ─────\n Use learned direction\n to make future decisions\n autonomously\n\nCapture Triggers\n\nThe agent actively captures direction signals when:\n\nExplicit signals:\n\nYou state a preference (\"I always want X before Y\")\nYou explain reasoning (\"Because we need to move fast\")\nYou set boundaries (\"Never do X without asking\")\nYou correct a decision (\"No, that's not the priority\")\n\nImplicit signals:\n\nYou choose between options (reveals criteria)\nYou allocate resources (reveals priorities)\nYou react to outcomes (reveals values)\nYou reject suggestions (reveals boundaries)\nArchitecture\n\nThe direction system lives in ~/self-direction/. See memory-template.md for templates.\n\n~/self-direction/\n├── direction.md # The complete direction model\n│ ├── values/ # What matters fundamentally\n│ ├── goals/ # Current objectives + reasons\n│ ├── criteria/ # Decision-making patterns\n│ ├── resources/ # Spending priorities\n│ ├── boundaries/ # Hard limits\n│ └── patterns/ # Thinking approaches\n│\n├── evidence.md # Raw observations that informed the model\n├── confidence.md # How confident in each element (low/medium/high)\n├── conflicts.md # Contradictions to resolve with user\n└── transmission.md # Direction summaries for sub-agents\n\nConfidence Levels\n\nNot all direction knowledge is equally certain:\n\nLevel\tMeaning\tAction\nHigh\tMultiple confirmations, explicit statements\tAct autonomously\nMedium\tInferred from behavior, single confirmation\tAct but mention reasoning\nLow\tSingle observation, uncertain inference\tAsk before acting\nConflict\tContradictory signals\tMust resolve with user\n\nThe agent tracks confidence for every element and acts accordingly.\n\nSelf-Direction in Action\n\nOnce the model has sufficient depth, the agent can:\n\n1. Make Autonomous Decisions\n\n\"Based on your direction model, this is clearly X because [reasoning from captured values/criteria]. Proceeding.\"\n\n2. Predict Your Preferences\n\n\"You haven't said, but based on your pattern of [evidence], you'd probably want [prediction]. Correct?\"\n\n3. Catch Misalignment Early\n\n\"This task seems to conflict with [captured boundary/value]. Should I proceed anyway?\"\n\n4. Explain Its Reasoning\n\n\"I chose A over B because your direction model shows [specific evidence]. Here's why...\"\n\n5. Know When It Doesn't Know\n\n\"I don't have enough direction signal for this. Your model is silent on [gap]. What's your preference?\"\n\nTransmitting Direction to Sub-Agents\n\nWhen spawning sub-agents, the direction system propagates:\n\n+─────────────────────────────────────────────────────────────+\n| DIRECTION TRANSMISSION |\n+─────────────────────────────────────────────────────────────+\n| |\n| MAIN AGENT (full direction model) |\n| | |\n| | Extracts relevant subset for task |\n| v |\n| TRANSMISSION FRAME: |\n| +─────────────────────────────────────────────────────+ |\n| | Context: Why this task exists | |\n| | Values: What matters for this work | |\n| | Criteria: How to judge success | |\n| | Boundaries: What NOT to do | |\n| | Resources: How much to spend | |\n| +─────────────────────────────────────────────────────+ |\n| | |\n| v |\n| SUB-AGENT (receives direction frame) |\n| | |\n| | Can make aligned decisions within scope |\n| | Escalates when outside frame |\n| |\n+─────────────────────────────────────────────────────────────+\n\n\nEvery sub-agent inherits enough direction to stay aligned.\n\nCore Rules\n1. Capture Before Acting\n\nWhen you encounter a decision point without clear direction:\n\nCHECK — Is this covered by the direction model?\nINFER — Can you reasonably predict from existing signals?\nASK — If uncertain, ask AND capture the answer\nNEVER — Guess on high-stakes decisions with low confidence\n2. Always Explain From Evidence\n\nWhen making autonomous decisions, cite your reasoning:\n\n\"Based on [specific captured element]...\"\n\"Your direction model shows [evidence]...\"\n\"This matches your pattern of [observation]...\"\n3. Evolve the Model Continuously\n\nThe direction model is never \"done\":\n\nNew observations update existing entries\nContradictions surface for resolution\nConfidence levels adjust with evidence\nOld patterns decay if not reinforced\n4. Respect Confidence Levels\nConfidence\tAutonomous Action Allowed\nHigh\tYes — act and report\nMedium\tYes — act and explain reasoning\nLow\tNo — ask first, then capture\nConflict\tNo — resolve contradiction first\n5. Transmit Faithfully\n\nWhen creating direction frames for sub-agents:\n\nInclude ALL relevant boundaries\nDon't soften or interpret values\nPreserve the \"why\" not just the \"what\"\nInclude escalation triggers\n6. Surface Gaps Proactively\n\nDon't wait to hit a gap. Proactively identify:\n\n\"Your direction model is silent on [topic]\"\n\"I'm low-confidence on [area]\"\n\"Would you like to strengthen your model for [domain]?\"\n7. Validate Periodically\n\nEvery N interactions or time period:\n\n\"Here's my understanding of your direction. Correct?\"\nSurface the highest-impact elements for confirmation\nResolve accumulated conflicts\nBuilding the Model\n\nThe model builds through natural interaction, not interrogation:\n\nPhase 1: Foundation (First Sessions)\nCapture explicit statements\nNote strong reactions\nRecord corrections\nAsk clarifying questions naturally\nPhase 2: Patterns (Days/Weeks)\nIdentify recurring themes\nConnect observations to values\nBuild decision criteria from choices\nMap resource allocation preferences\nPhase 3: Prediction (Ongoing)\nStart predicting before being told\nValidate predictions to strengthen model\nCatch edge cases that reveal nuance\nHandle novel situations with inference\nPhase 4: Transmission (Mature Model)\nCreate direction frames for sub-agents\nMaintain consistency across all processes\nPropagate updates when model changes\nAudit sub-agent alignment\nDirection Model Template\n\nSee memory-template.md for the complete structure. Key sections:\n\nValues:\n\n## Values\n\n### Speed vs Quality\nconfidence: high\nevidence: [list of observations]\npattern: \"Prefers shipping fast for MVPs, quality for production\"\n\n### Risk Tolerance \nconfidence: medium\nevidence: [list of observations]\npattern: \"Conservative with money, aggressive with time\"\n\n\nCriteria:\n\n## Decision Criteria\n\n### What Makes Something Worth Doing\nconfidence: high\nevidence: [list of observations]\ncriteria:\n - Moves toward [goal]\n - Costs less than [threshold]\n - Doesn't violate [boundary]\n\nQuick Reference\nTopic\tFile\nSetup process\tsetup.md\nDirection model template\tmemory-template.md\nEvidence logging guide\tevidence.md\nSub-agent transmission\ttransmission.md\nCommon Traps\nTrap\tSolution\nActing on low-confidence inference\tCheck confidence level first, ask if low\nCapturing noise as signal\tRequire multiple observations for patterns\nModel becomes stale\tContinuous updates, periodic validation\nSub-agents ignore direction\tVerify transmission frame is complete\nAssuming universal patterns\tContext-tag observations (work vs personal)\nOperating Modes\nLearning (Default)\n\nActively captures direction signals. Asks clarifying questions. Builds model depth.\n\nAutonomous\n\nHigh-confidence model. Acts on direction without confirmation. Explains reasoning.\n\nConservative\n\nNew relationship or critical domain. Asks more, assumes less. Prioritizes not breaking trust.\n\nRelated Skills\n\nInstall with clawhub install if user confirms:\n\nreflection — Structured self-evaluation before delivering work\ndecide — Auto-learn decision patterns\nescalate — Know when to ask vs act\ndelegate — Route tasks to sub-agents effectively\nmemory — Long-term memory patterns\nFeedback\nIf useful: clawhub star self-direction\nStay updated: clawhub sync" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/ivangdavila/self-direction", "publisherUrl": "https://clawhub.ai/ivangdavila/self-direction", "owner": "ivangdavila", "version": "1.0.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/self-direction", "downloadUrl": "https://openagent3.xyz/downloads/self-direction", "agentUrl": "https://openagent3.xyz/skills/self-direction/agent", "manifestUrl": "https://openagent3.xyz/skills/self-direction/agent.json", "briefUrl": "https://openagent3.xyz/skills/self-direction/agent.md" } }