Self-Discover Reasoning Skill#

An AI agent skill for self-composing task-specific reasoning structures. Based on Zhou et al. (2024) — SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures.

Platform Auto-Detection#

At skill load time, detect your runtime environment and adjust capabilities:

CapabilityHow to CheckFallbackFile systemCan you read references/discovery-templates.md?Use the inline templates below insteadPersistent memoryCan you write to memory/?Store discovery notes in conversation context onlyLong contextIs your context window > 32K tokens?Cap at Level 2 (skip full discovery)Tool accessCan you call external tools?Use mental reasoning only

Detection rules:

• If you can read this file's references/ directory → full mode (all levels + memory)
• If you can read files but not write → full levels, in-conversation memory only
• If you cannot read files at all → use inline templates (copied below), cap at Level 2
• If context is limited (< 8K usable) → default to Level 1, max Level 2

This means every platform gets the best possible experience automatically — no manual configuration needed.

Inline Discovery Templates (for environments without file access)#

If you cannot read references/discovery-templates.md, use these directly:

Level 1 Internal Prompt#

text

Before answering: (1) What type of reasoning does this task need? (2) Pick 1-2 modules from the seed list. (3) Apply them. Deliver.

Level 2 Internal Prompt#

text

1. SELECT: From the seed modules, choose 3-5 relevant to this task.
2. ADAPT: Rephrase each selected module to be task-specific.
3. IMPLEMENT: Compose into a step-by-step reasoning structure.
4. Follow the structure to produce the answer.

Level 3 Internal Prompt#

text

1. SELECT: Choose 4-7 modules relevant to the task type.
2. ADAPT: Tailor each to the specific task, adding domain expertise.
3. IMPLEMENT: Build a JSON-like reasoning structure with keys and expected outputs.
4. Execute the structure step-by-step.
5. VERIFY: Check the answer against the structure — did every key get a valid value?
6. If gaps found, refine the structure and re-execute.

When to Activate#

Activate when you are about to solve a reasoning-intensive task — after gathering all information but before producing your answer. Discovery happens before reasoning, not instead of work.

Strong triggers: Multi-step reasoning, math, logic puzzles, planning, debugging, architecture decisions, analytical tasks.

Skip: Simple factual lookups, greetings, formatting requests, single-sentence answers.

Core Process: SELECT → ADAPT → IMPLEMENT → SOLVE#

text

1. SELECT    — Choose relevant reasoning modules from the seed set
2. ADAPT     — Rephrase selected modules to be task-specific
3. IMPLEMENT — Compose modules into a structured reasoning plan (key-value format)
4. SOLVE     — Follow the reasoning structure to produce the final answer

Source: Zhou et al., "Self-Discover: Large Language Models Self-Compose Reasoning Structures" (2024, ICML) — LLMs self-compose atomic reasoning modules into task-intrinsic structures, achieving up to 32% improvement over Chain-of-Thought.

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Seed Reasoning Modules#

These are atomic reasoning skills drawn from cognitive science and prompting research (Fernando et al., 2023; Zhou et al., 2024). The agent selects a subset relevant to each task.

#ModuleDescriptionBest For1Step-by-Step ThinkingBreak reasoning into sequential stepsSequential tasks, procedures2DecompositionBreak problem into sub-problems, solve eachComplex multi-part problems3Critical ThinkingAnalyze from multiple perspectives, question assumptionsEvaluating claims, decisions4Reflective ThinkingSearch for first principles, examine underlying theoryScience, deep analysis5Creative ThinkingGenerate novel approaches, brainstorm alternativesDesign, open-ended tasks6Pattern RecognitionIdentify recurring patterns and regularitiesData analysis, sequences7Analogical ReasoningMap solutions from similar known problemsTransfer learning, explanations8Causal AnalysisIdentify cause-effect relationshipsDebugging, diagnostics9Constraint SatisfactionCheck solutions against all constraintsOptimization, planning10AbstractionExtract general principles from specificsGeneralization, architecture11Hypothesis TestingForm and test hypotheses systematicallyTroubleshooting, debugging12Comparative AnalysisCompare options against defined criteriaDecision-making, trade-offs13Temporal ReasoningReason about sequences and time dependenciesScheduling, process flows14Spatial ReasoningReason about spatial relationshipsLayout, geometry, UI15Counterfactual ThinkingConsider "what if" alternativesRisk analysis, planning16Lateral ThinkingApproach from unconventional anglesInnovation, problem-solving17Deductive ReasoningApply general rules to specific casesLogic, validation18Inductive ReasoningInfer general rules from specific examplesPattern generalization19Probabilistic ThinkingReason under uncertainty with probabilitiesRisk assessment, forecasting20Systems ThinkingConsider interactions between componentsArchitecture, complex systems

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Discovery Depth Levels#

The depth is determined by task complexity, not user preference. The agent auto-selects.

Level 0: Direct Answer (Skip discovery)#

Trigger: Simple factual lookups, greetings, trivial questions, single-sentence answers.

Action: Do nothing extra. Just respond. Cost: 0 additional tokens.

Examples: "What time is it?", "Thanks", simple formatting requests.

Level 1: Single-Module Reasoning#

Trigger: Medium-complexity tasks — explanations, how-to guides, code snippets, questions requiring one type of reasoning.

Action: Select 1-2 relevant modules. Apply mentally. Deliver.

Budget: 1 discovery pass. ~10% overhead on response tokens.

Internal process:

text

After receiving the task:
- What type of reasoning does this need?
- Pick the most relevant module (e.g., "decomposition" for multi-part questions)
- Apply it while composing the answer
- Deliver

Level 2: Multi-Module Composition#

Trigger: Complex tasks — technical architectures, multi-step plans, debugging, anything requiring 3+ reasoning steps or multiple perspectives.

Action: Select 3-5 modules. Adapt to task. Compose into structured plan. Execute.

Budget: 1 full discovery cycle (SELECT + ADAPT + IMPLEMENT). ~25% overhead.

Internal process:

text

1. SELECT 3-5 relevant modules from the seed set
2. ADAPT each to be specific to this task
3. IMPLEMENT into a step-by-step structure
4. Follow the structure to produce the answer

Level 3: Full Self-Discovery#

Trigger: High-stakes tasks — complex math, logic puzzles, production architecture, multi-constraint optimization, or user explicitly requests thorough reasoning.

Action: Full SELECT → ADAPT → IMPLEMENT cycle with 4-7 modules, JSON-like reasoning structure, and verification pass.

Budget: Full discovery cycle + verification. ~40% overhead.

Internal process:

text

1. SELECT 4-7 relevant modules
2. ADAPT with domain-specific tailoring
3. IMPLEMENT into key-value reasoning structure:
   {
     "step_1": { "action": "...", "expected_output": "..." },
     "step_2": { "action": "...", "expected_output": "..." },
     ...
   }
4. Execute each step, filling in values
5. VERIFY: Did every step produce a valid output?
6. If gaps → refine structure, re-execute gap steps only

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Pre-Built Discovery Structures (Quick Templates)#

For common task types, use these pre-composed structures instead of running full discovery:

Coding / Debugging#

json

{
  "step_1": { "action": "Reproduce: Identify the exact error and trigger condition", "output": "error description" },
  "step_2": { "action": "Decompose: Break the code path into segments", "output": "list of code segments" },
  "step_3": { "action": "Hypothesis: Form 2-3 hypotheses for root cause", "output": "ranked hypotheses" },
  "step_4": { "action": "Test: Verify top hypothesis against evidence", "output": "confirmed root cause" },
  "step_5": { "action": "Fix: Implement the fix with edge case handling", "output": "corrected code" }
}

Architecture / Decision#

json

{
  "step_1": { "action": "Decompose: List all requirements and constraints", "output": "requirement matrix" },
  "step_2": { "action": "Compare: Generate 2-3 options", "output": "option summaries" },
  "step_3": { "action": "Evaluate: Score each option against constraints", "output": "comparison table" },
  "step_4": { "action": "Decide: Select best option with justification", "output": "decision + rationale" },
  "step_5": { "action": "Validate: Check for overlooked constraints", "output": "final recommendation" }
}

Math / Logic#

json

{
  "step_1": { "action": "Understand: Restate the problem, identify given and unknown", "output": "problem statement" },
  "step_2": { "action": "Plan: Select relevant formulas/approaches", "output": "solution strategy" },
  "step_3": { "action": "Execute: Apply step-by-step with intermediate results", "output": "workings" },
  "step_4": { "action": "Verify: Substitute answer back or check invariants", "output": "verification" }
}

Analysis / Research#

json

{
  "step_1": { "action": "Scope: Define what needs to be analyzed and why", "output": "analysis scope" },
  "step_2": { "action": "Gather: Identify key facts, data points, or evidence", "output": "evidence list" },
  "step_3": { "action": "Pattern: Find patterns, trends, or anomalies", "output": "findings" },
  "step_4": { "action": "Synthesize: Combine findings into conclusions", "output": "conclusions" },
  "step_5": { "action": "Validate: Check conclusions against original scope", "output": "final analysis" }
}

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Convergence Rules#

Based on the empirical finding from Zhou et al. that self-discovered structures are effective in a single pass:

1. Maximum 1 discovery cycle per task (SELECT → ADAPT → IMPLEMENT is one cycle).
2. Verification pass (Level 3 only): If verification reveals gaps, refine structure once. Do not loop.
3. Stop if the structure covers all aspects of the task.
4. Diminishing returns rule: Discovery overhead should never exceed 40% of the response.

Anti-pattern — DO NOT:

• Run discovery on every single message (use Level 0 for simple Q&A)
• Over-compose modules (more ≠ better; 3-5 modules is optimal for most tasks)
• Re-run discovery if the first answer is reasonable

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Cost Control Strategy#

DepthModulesMax StepsApprox. Token OverheadWhen to UseLevel 000+0%Simple Q&ALevel 11-2Mental10%Most conversationsLevel 23-5Structured plan25%Complex technicalLevel 34-7Full JSON + verify~40%High-stakes only

Principle: Discovery should cost less than the cost of a wrong approach. For low-stakes responses, skip discovery entirely.

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Trigger Conditions Summary#

Auto-Trigger (always on)#

• Task requires multi-step reasoning → at least Level 1
• Task involves 3+ distinct reasoning types → at least Level 2
• Task is math, logic, or planning → at least Level 1

Skip Discovery#

• User is in a hurry (explicit: "quick", "brief", "just tell me")
• Response is under 2 sentences
• Pure social/chat exchange
• Simple factual lookup

User Manual Trigger#

• User says "think carefully", "reason through this" → Level 2+
• User says "this is important", "critical", "production" → Level 3
• User says "discover" or "self-discover" → Level 2+

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Output Format#

Discovery is internal — the user should not see the raw reasoning structure. However:

After using discovery, you MAY append a subtle note:#

Level 1: No note (keep it invisible). Level 2: Optionally: _(response composed via multi-module reasoning)_ Level 3: Optionally: _(reasoning structure self-discovered from [N] modules)_

NEVER:#

• Show the raw JSON reasoning structure to the user
• Make the note prominent or distracting
• Add notes for Level 0 or Level 1 responses

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Cross-Task Transfer (Structure Reuse)#

Inspired by Zhou et al.'s finding that self-discovered structures transfer across model families — the same reasoning structure that works for GPT-4 also helps Llama-2.

When to reuse: When you encounter a task of the same type as one you've previously solved with discovery (e.g., another debugging task, another architecture decision).

How to reuse: Write the discovered structure to memory/discovered-structures.md:

text

## [Task Type]
Structure: [paste the key-value structure]
Effective for: [describe task characteristics]
Date: [today]

Next time you encounter a similar task, check memory first before running discovery again.

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Relationship to Other Reasoning Techniques#

TechniqueSourceHow Self-Discover DiffersChain of ThoughtWei et al. 2022CoT is a single module; Self-Discover composes multipleLeast-to-MostZhou et al. 2022Decomposition only; Self-Discover selects best modules per taskSelf-ConsistencyWang et al. 2022Requires 10-40x more compute; Self-Discover is more efficientTree of ThoughtsYao et al. 2023Explores multiple paths; Self-Discover composes optimal structureStep-Back PromptingZheng et al. 2023Single principle; Self-Discover combines multiple heuristicsOPROYang et al. 2023Optimizes prompts with training data; Self-Discover needs no labels

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Quick Reference Card#

text

DISCOVER? ──→ Simple/trivial? ──→ NO → Just respond
    │
    YES
    │
    ├─ Single reasoning type → LEVEL 1 (1-2 modules, mental)
    ├─ Multi-step / multi-perspective → LEVEL 2 (3-5 modules, structured)
    └─ High-stakes / complex → LEVEL 3 (4-7 modules, full JSON + verify)

SELECT → ADAPT → IMPLEMENT → SOLVE
    │          │          │          │
    Pick      Tailor     Structure  Execute
    modules   to task    the plan   the plan