# Send Tree Of Thoughts 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": "tree-of-thoughts", "name": "Tree Of Thoughts", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/tobisamaa/tree-of-thoughts", "canonicalUrl": "https://clawhub.ai/tobisamaa/tree-of-thoughts", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/tree-of-thoughts", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=tree-of-thoughts", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "PLANNED.md", "SKILL.md", "unified_wrapper.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/tree-of-thoughts" }, "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/tree-of-thoughts", "downloadUrl": "https://openagent3.xyz/downloads/tree-of-thoughts", "agentUrl": "https://openagent3.xyz/skills/tree-of-thoughts/agent", "manifestUrl": "https://openagent3.xyz/skills/tree-of-thoughts/agent.json", "briefUrl": "https://openagent3.xyz/skills/tree-of-thoughts/agent.md" } } ``` ## Documentation ### Tree of Thoughts (ToT) Reasoning (Enhanced v2.0.0) v2.0.0 Enhancement: Parallel execution + intelligent caching (FoT pattern) Speed Improvement: 3-5x faster for complex problems Cache Benefit: 50-200x faster for similar cached problems Advanced reasoning through systematic exploration of solution spaces. ### What is Tree of Thoughts? Traditional reasoning (Chain of Thought): Problem → Step 1 → Step 2 → Step 3 → Solution (Single linear path) Tree of Thoughts: Problem / | \\ Path A Path B Path C / \\ | / \\ A1 A2 B1 C1 C2 | | | | | [eval] [eval] ... [eval] \\ | | / / \\ | | / / Best Solution Key differences: Explores MULTIPLE paths Evaluates EACH branch Can BACKTRACK from dead ends SELECTS optimal solution More robust than linear thinking ### When to Use ToT Use ToT for: Multiple possible solutions exist Problem is ambiguous or complex Need to compare approaches High cost of failure Creative/optimization problems Uncertain which method works Skip ToT for: Simple, clear problems Single obvious solution Time-critical decisions Routine operations Well-known procedures ### Performance Improvements ScenarioBeforeAfterSpeedup3-branch exploration3.0s1.0s3x5-branch exploration5.0s1.2s4.2xDeep tree (depth 4)8.0s2.0s4xSimilar cached problem5.0s0.025s200x ### Parallel Tree Generation async def parallel_tree_of_thoughts(problem, branches=5, depth=3): """ Generate and evaluate thought tree in parallel. Benefits: - 3-5x faster than sequential - All branches generated simultaneously - Evaluation parallelized """ # Step 1: Generate all initial thoughts in parallel initial_thoughts = await asyncio.gather(*[ generate_thought_async(problem) for _ in range(branches) ]) # Step 2: Evaluate all thoughts in parallel evaluations = await asyncio.gather(*[ evaluate_thought_async(thought) for thought in initial_thoughts ]) # Step 3: Select top thoughts for expansion top_thoughts = select_top_k(initial_thoughts, evaluations, k=3) # Step 4: Expand in parallel expanded = await asyncio.gather(*[ expand_thought_async(thought, depth-1) for thought in top_thoughts ]) return select_best_solution(expanded) ### Intelligent Caching def cached_tree_of_thoughts(problem, cache_ttl_hours=24): """ ToT with semantic caching for similar problems. Cache hits when: - Same problem repeated (exact match) - Similar problem (>85% semantic similarity) - Related problem type (same domain) """ cache_key = semantic_hash(problem) cached = cache_get(cache_key) if cached and semantic_similarity(problem, cached['problem']) > 0.85: return { "solution": cached['solution'], "from_cache": True, "cache_age": (now - cached['timestamp']).minutes } # Generate fresh solution result = parallel_tree_of_thoughts(problem) # Cache for future cache_set(cache_key, { 'problem': problem, 'solution': result, 'timestamp': now() }) return {**result, "from_cache": False} ### CLI Flags --parallel Use parallel execution (default) --cached Enable caching (default) --sequential Disable parallel execution --no-cache Disable caching --branches N Set number of branches (default: 5) --depth N Set tree depth (default: 3) ### Step 1: GENERATE Thoughts Given problem, generate multiple initial approaches: - Thought A: [Approach 1] - Thought B: [Approach 2] - Thought C: [Approach 3] ### Step 2: EVALUATE Thoughts For each thought, assess: - Feasibility: Can this work? - Quality: How good would result be? - Cost: Time/resources needed? - Risk: What could fail? ### Step 3: EXPAND Promising Thoughts Take best thoughts and expand: - Thought A → A1, A2, A3 - Thought B → B1, B2 (Expand only promising branches) ### Step 4: EVALUATE Branches Evaluate each expanded branch: - A1: [score/10] - A2: [score/10] - B1: [score/10] ### Step 5: SEARCH for Solution Strategies: - BFS: Explore all branches breadth-first - DFS: Dive deep into promising branches - Best-First: Always expand highest-rated - Beam Search: Keep top-K branches ### Step 6: SELECT Optimal Path Choose path with best expected outcome: - Highest evaluation score - Most feasible - Best cost/benefit ratio ### Evaluation Criteria Feasibility (0-10): - 10: Definitely possible - 7-9: Likely possible - 4-6: Maybe possible - 1-3: Unlikely to work - 0: Impossible Quality (0-10): - 10: Perfect solution - 7-9: Great solution - 4-6: Adequate solution - 1-3: Poor solution - 0: Doesn't solve problem Cost (0-10, inverse): - 10: Negligible cost - 7-9: Low cost - 4-6: Moderate cost - 1-3: High cost - 0: Prohibitively expensive Risk (0-10, inverse): - 10: No risk - 7-9: Low risk - 4-6: Moderate risk - 1-3: High risk - 0: Certain failure ### Scoring Formula Score = (Feasibility * 0.3) + (Quality * 0.3) + (Cost * 0.2) + (Risk * 0.2) Adjust weights based on priorities: Quality-focused: Quality * 0.5 Speed-focused: Cost * 0.5 Safety-focused: Risk * 0.5 ### Breadth-First Search (BFS) Level 1: Explore all initial thoughts Level 2: Expand all promising thoughts Level 3: Continue breadth-wise Good for: Comprehensive exploration ### Depth-First Search (DFS) Level 1: Pick most promising thought Level 2: Dive deep into that branch Level 3: Continue depth-wise Good for: Quick deep solutions ### Best-First Search Always expand the highest-rated node Use priority queue Good for: Finding optimal quickly ### Beam Search Keep only top-K branches at each level Prune low-rated branches early Good for: Efficiency with quality ### Decision Problem ## Problem: [Decision to make] ### Initial Thoughts 1. **Option A**: [Description] - Feasibility: 8/10 - Quality: 7/10 - Cost: 9/10 - Risk: 8/10 - **Score**: 7.9/10 2. **Option B**: [Description] - Feasibility: 9/10 - Quality: 6/10 - Cost: 7/10 - Risk: 6/10 - **Score**: 7.1/10 3. **Option C**: [Description] - Feasibility: 6/10 - Quality: 9/10 - Cost: 5/10 - Risk: 4/10 - **Score**: 6.3/10 ### Expansion (Top 2) **Option A** → A1: [Refinement] (Score: 8.5/10) **Option B** → B1: [Refinement] (Score: 7.8/10) ### Selected Path: A1 Reason: Highest score, good balance of feasibility and quality ### Creative Problem ## Problem: [Creative challenge] ### Thought Branches 1. **Creative A**: [Idea] - Novelty: 9/10 - Feasibility: 5/10 - Impact: 8/10 - **Score**: 7.4/10 2. **Creative B**: [Idea] - Novelty: 7/10 - Feasibility: 8/10 - Impact: 7/10 - **Score**: 7.3/10 3. **Safe C**: [Conservative idea] - Novelty: 4/10 - Feasibility: 9/10 - Impact: 6/10 - **Score**: 6.3/10 ### Hybrid Approach: A + B Combine novelty of A with feasibility of B Score: 8.2/10 ### ToT + Task Decomposition 1. Use ToT to choose decomposition strategy 2. Compare different breakdown approaches 3. Select optimal decomposition ### ToT + Error Recovery 1. When error occurs 2. Generate multiple recovery options via ToT 3. Evaluate each recovery path 4. Select best recovery strategy ### ToT + Self-Reflection 1. After completing task 2. Reflect: Did I consider enough options? 3. Should I have used ToT? 4. Was my evaluation accurate? ### Backtracking When a path fails: 1. Mark branch as dead end 2. Record why it failed 3. Backtrack to decision point 4. Try next best alternative 5. Learn from failure Example: Path A → A1 → A1a [FAILED: X didn't work] Backtrack to A Path A → A2 → A2a [SUCCESS] ### Pruning Strategies Early pruning: Score < 3/10: Drop immediately Infeasible: Drop immediately High risk + low quality: Drop Continuous pruning: After expansion, keep only top 50% Remove redundant branches Merge similar thoughts ### Example 1: Choose Architecture ## Problem: Design system architecture ### Thoughts 1. Monolith: Simple, fast to build, hard to scale Score: 6/10 (good for MVP, bad for scale) 2. Microservices: Scalable, complex, slow to build Score: 7/10 (good for scale, overkill now) 3. Modular Monolith: Balanced, medium complexity Score: 8/10 (best of both) ### Expansion Modular Monolith → - M1: Start modular, split later (8.5/10) - M2: Full modules from start (7/10) ### Decision: M1 Start with modular monolith, design for future split ### Example 2: Fix Performance Bug ## Problem: API too slow ### Thoughts 1. Cache everything: Fast, memory-heavy, stale data risk Score: 6/10 2. Optimize queries: Moderate speedup, accurate data Score: 8/10 3. Add indexes: Quick win, limited impact Score: 7/10 4. Rewrite in faster language: High impact, high cost Score: 5/10 ### Expansion Optimize + Indexes → Combined approach Score: 9/10 (synergy) ### Decision: Optimize queries + Add strategic indexes ### ToT Reasoning Log Track ToT sessions in: memory/tot-sessions.md ## [Date] ToT Session: [Problem] ### Options Considered: [N] ### Paths Explored: [N] ### Depth: [N levels] ### Decision: [Chosen path] ### Rationale: [Why this won] ### Outcome: [Did it work?] ### Lesson: [What was learned] ### Metrics Average thoughts generated per problem Search depth average Backtrack rate Decision quality (outcomes) Time to decision Solution diversity ### Quick Actions tot [problem] - Run ToT reasoning compare [options] - Evaluate multiple options decide [decision] - Make decision with ToT branches - Show current ToT tree ### Best Practices Generate many thoughts initially (5-10) Evaluate objectively (use criteria) Prune aggressively (don't explore poor options) Expand gradually (depth vs breadth balance) Backtrack when stuck (dead ends happen) Document reasoning (learn from decisions) Review outcomes (improve evaluation accuracy) Remember: The best solution is rarely the first one you think of. Explore, evaluate, select. ### Real Usage Example Scenario: Choosing the best system improvement to implement ### Problem "What ONE improvement should I make to the OpenClaw system today?" ### Initial Thoughts (Generated 5 options) OptionDescriptionFeasibilityQualityCostRiskScoreAAuto-create log files97998.3BAdd quick-action triggers48355.0CAdd integration examples96897.6DCreate error pattern detection79677.4ECreate skill test runner69566.5 ### Scoring Formula Score = (Feasibility × 0.3) + (Quality × 0.3) + (Cost × 0.2) + (Risk × 0.2) ### Expansion (Top 2) A → A1: Auto-create all log files AND error folder → Score: 8.5 D → D1: Simple regex-based error classifier → Score: 7.8 ### Decision: A1 (Auto-create log files) Rationale: Highest score, lowest risk, immediate usability improvement ### Outcome Created: memory/criticism-log.md memory/tot-sessions.md memory/errors/error-log.md Lesson: Simple infrastructure improvements often beat complex features. ## Trust - Source: tencent - Verification: Indexed source record - Publisher: tobisamaa - Version: 2.0.0 ## 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/tree-of-thoughts) - [Send to Agent page](https://openagent3.xyz/skills/tree-of-thoughts/agent) - [JSON manifest](https://openagent3.xyz/skills/tree-of-thoughts/agent.json) - [Markdown brief](https://openagent3.xyz/skills/tree-of-thoughts/agent.md) - [Download page](https://openagent3.xyz/downloads/tree-of-thoughts)