agentleFS
lacerbilacerbi/dotclaude/skills/deepthink/SKILL.md

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

  • Other versions4 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 4

    1. okikusan-public/stock_skills_2★ 32same name
    2. TheNight-Watch/deepthink-skill★ 2same name
    3. KeyArgo/skills-my-hermes★ 1same name
    4. 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.

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
  1. Distinctive:

    If you lack understanding of the problem domain, codebase, or relevant materials:

    line 25
  2. Distinctive:

    Read relevant files in full

    line 27
  3. Distinctive:

    If you already have sufficient context from the current conversation, proceed directly.

    line 30
  4. Distinctive:

    When spawning each sub-agent, you must include in the prompt:

    line 38
  5. Distinctive:

    Do NOT assume sub-agents will figure out context from a thin prompt.

    line 46
  6. Distinctive:

    When spawning sub-agents, instruct them to:

    line 52
  7. Distinctive:

    Only conclude when they have genuinely exhausted their reasoning on the problem

    line 56
  8. Distinctive:

    Before synthesizing, review each agent's output for:

    line 79
  9. Very specific:

    If continuing to another cycle, synthesize and write to ANALYSIS_[descriptor]_cycle[N].md:

    line 108
  10. Distinctive:

    If this is the last cycle, move to Final Output.

    line 120

“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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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:

  1. Full problem statement with background and goals
  2. Relevant file contents (or key excerpts) that inform the analysis
  3. Key findings you've already established
  4. Specific constraints or requirements the user has mentioned
  5. 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.

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.