deepthink
Skill2 starsChanged 2 months agoMITView on GitHub
Deep analysis using parallel reasoning with iterative refinement
4 other projects use a version of this.
Adopted by
Most recently taken up by
- wxhou/deepthinktheir own version, first seen Oct 20, 2026
- KeyArgo/skills-my-hermestheir own version, first seen Jun 17, 2026
- okikusan-public/stock_skills_2their own version, first seen Apr 27, 2026
- TheNight-Watch/deepthink-skilltheir own version, first seen Mar 12, 2026
Other versions4 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 4
- okikusan-public/stock_skills_2★ 32same name
- TheNight-Watch/deepthink-skill★ 2same name
- KeyArgo/skills-my-hermes★ 1same name
- wxhou/deepthink★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository12 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- remote/CLAUDE.mdCLAUDE.md
- auditSkill
- decision-boardSkill
- doublecheckSkill
- filesSkill
- handoffSkill
- hpcSkill
- loadSkill
- planSkill
- taskSkill
- triageSkill
The file
We split this skill into the rules it gives an agent
10 rules. 0 are common, 9 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
9
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 25
If you lack understanding of the problem domain, codebase, or relevant materials:
- Distinctive: line 27
Read relevant files in full
- Distinctive: line 30
If you already have sufficient context from the current conversation, proceed directly.
- Distinctive: line 38
When spawning each sub-agent, you must include in the prompt:
- Distinctive: line 46
Do NOT assume sub-agents will figure out context from a thin prompt.
- Distinctive: line 52
When spawning sub-agents, instruct them to:
- Distinctive: line 56
Only conclude when they have genuinely exhausted their reasoning on the problem
- Distinctive: line 79
Before synthesizing, review each agent's output for:
- Very specific: line 108
If continuing to another cycle, synthesize and write to ANALYSIS_[descriptor]_cycle[N].md:
- Distinctive: line 120
If this is the last cycle, move to Final Output.
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Problem
$ARGUMENTS
Configuration
Defaults: 5 agents for Cycle 1, 3 agents for subsequent cycles, 2–3 cycles (2 if early convergence, 3 if significant disagreement persists). Adjust if the user specifies (e.g., "use 10 agents", "do 4 rounds", "go deeper").
Your Task
Achieve higher-quality analysis through parallel independent reasoning with iterative refinement.
You (main agent) orchestrate and synthesize. Sub-agents analyze based on the context you provide.
Phase 0: Context Preparation
Before spawning any sub-agents, ensure you have sufficient context.
If you lack understanding of the problem domain, codebase, or relevant materials:
- Explore the project structure
- Read relevant files in full
- Gather whatever context is needed to deeply understand the problem
If you already have sufficient context from the current conversation, proceed directly.
Critical: Sub-agents will only know what you tell them. They start cold with no access to your conversation history or accumulated understanding.
Spawning Sub-Agents: Context Requirements
When spawning each sub-agent, you must include in the prompt:
- Full problem statement with background and goals
- Relevant file contents (or key excerpts) that inform the analysis
- Key findings you've already established
- Specific constraints or requirements the user has mentioned
- The assigned lens for that agent's perspective
Do NOT assume sub-agents will figure out context from a thin prompt. Give them everything they need to reason well.
Sub-Agent Instructions
When spawning sub-agents, instruct them to:
- Be extremely thorough—consider the problem deeply until fully satisfied with their analysis
- Spend maximum effort; do not satisfice or stop at "good enough"
- Start from the provided context, then explore further to deepen understanding, fill gaps, and verify assumptions
- Only conclude when they have genuinely exhausted their reasoning on the problem
- Produce: analysis, conclusion, confidence (high/medium/low), key assumptions made
Cycle 1: Divergent Analysis
Spawn N Opus sub-agents to analyze independently:
- Each works in isolation (no shared context beyond what you provide)
- Assign each a distinct lens. Examples:
- Skeptic: Assumes hidden problems, stress-tests assumptions
- Advocate: Builds the strongest case, finds opportunities
- First-principles: Reasons from fundamentals, ignores convention
- Pragmatist: Focuses on real-world constraints and tradeoffs
- Edge-case hunter: Explores boundaries and failure modes
- Framing challenger: Questions whether the problem as stated contains false premises, hidden assumptions, or misconceptions—should we be solving a different problem?
- Historian: Looks for analogies, precedents, what's been tried before
- Contrarian: Argues for the least popular position to ensure it gets heard
- Systems thinker: Examines second-order effects, feedback loops, emergent behavior
- Steelman: Constructs the strongest possible counter-argument to the emerging consensus
- (Adapt lenses to problem domain as appropriate)
Verification Pass
Before synthesizing, review each agent's output for:
- Logical errors or invalid inferences
- Unsupported claims or circular reasoning
- Internal contradictions
- Unstated assumptions that may not hold
Flag or discount flawed reasoning. Note which analyses survive verification intact.
Synthesis
Collect analyses, then synthesize and write to ANALYSIS_[descriptor]_cycle1.md:
- Agreement: High-confidence conclusions (multiple verified agents concur)
- Disagreement: Conflicting views and which reasoning wins
- Unique insights: Things only one agent caught (validate or discard)
- Open questions: What remains uncertain
- Confidence map: Overall confidence per conclusion
Cycle 2+: Convergent Refinement
Spawn a new round of N Opus sub-agents:
- Pass the previous cycle's synthesis as context along with the original problem context
- Agents critique it, identify weaknesses, propose improvements
- Focus on: unresolved disagreements, weak reasoning, missing perspectives
Verification Pass
Same as Cycle 1—review each agent's critique for logical validity before incorporation.
Synthesis
If continuing to another cycle, synthesize and write to ANALYSIS_[descriptor]_cycle[N].md:
- Updated conclusions with refined confidence levels
- How disagreements were resolved (or why they remain open)
- What changed from previous cycle and why
Each cycle tightens the analysis.
Cycle Continuation Check
After Cycle 2, assess:
- If convergence: Key conclusions stable, disagreements resolved or clearly irresolvable without user input → proceed to Final Output
- If significant unresolved disagreement: Agents still genuinely conflicting on substance (not matters requiring user decision, but actual analytical uncertainty) → run Cycle 3
If this is the last cycle, move to Final Output.
Final Output
Write ANALYSIS_[descriptor]_final.md containing:
- Synthesized answer with confidence levels
- Key disagreements and how they were resolved
- Assumptions & limitations: What this analysis depends on
- Recommendations for further investigation (if any), or decisions requiring user input
Length: The final output can be as long as needed for the task. Do not pad or add fluff, but if the task is genuinely complex, use all the space required to address it thoroughly.
Provide a brief summary in chat pointing to the final file.
Use Opus only—the value comes from parallel high-quality reasoning.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

