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deep-think-maximum-cognitive-effort-protocol-mq8kvw92

Work through complex or costly decisions with a structured process covering context, research, competing perspectives, planning, execution, and critical review. The workflow adapts its depth to the task and produces recommendations with tradeoffs, risks, and success criteria.

OriginalShow the original description

Use this plugin when the user wants a maximum-effort reasoning workflow for a complex, high-stakes, or ambiguous task.

AI rating80/ 100See how to install

Good for

  • Comparing system architecture options
  • Planning changes across multiple systems
  • Rechecking assumptions in an ambiguous request
  • Reviewing risks before irreversible actions

Overview

What it does

This skill organizes assumptions, gathers evidence, and examines complex problems from several perspectives before producing a recommendation and execution steps. It classifies tasks into three complexity levels and selects the necessary phases. For major changes, it compares the current and desired states, breaks work into small steps, executes and verifies them, and finishes by checking counterarguments and omissions.

When it helps

Use it for architecture choices, changes spanning multiple systems, or actions that are difficult to reverse, when decision quality matters more than speed. It also helps you weigh technical considerations alongside business goals, usability, and operations. The resulting recommendation organizes the proposed approach, tradeoffs, risks, and success criteria.

Example requests

  • “Use /deep-think to compare architecture options and organize the recommendation, risks, and success criteria.”
  • “Build a plan for changes across multiple systems, from checking assumptions through verification after execution.”

Notes

The estimated duration is 5–15 minutes. Some phases use external searches and reference materials, plus memory systems or MCP tools when available. If tools are unavailable, it records the limitations and continues. For high-stakes decisions with confidence below 80%, it is designed to ask the user for input.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodIt identifies architecture decisions and irreversible actions as use cases and assigns phases by task complexity.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodIt specifies eight phases, five checks before each action, and the required contents of recommendations.
SafetyAre sending data, deleting files, and running commands disclosed?
BPartly lackingPre-action checks are specified, but confirmation boundaries for external searches and memory writes are unclear.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingMCP and memory systems are optional, but setup instructions for search capabilities are absent.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 20, 2026. Treat it as a guide and read the original before installing.

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Files included (3)

  • SKILL.md6.6 KB
  • open-design.json701 B
  • references/source-1-mpj79mp4-deep-think.md399 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

/deep-think — Maximum Cognitive Effort Protocol

Goal: Activate the highest level of cognitive architecture for complex, multi-step, or ambiguous tasks where quality matters more than speed. Time: 5-15 minutes depending on complexity tier. When to use: Architecture decisions, multi-system changes, irreversible actions, anything where being wrong is expensive.

This is NOT /reason. /reason = "follow the reasoning engine checklist" (fast, lightweight). /deep-think = "maximum effort with structured thinking, research, gap analysis, multi-perspective debate, and adversarial review" (slow, thorough).


Complexity Gate (decide first)

Before starting, classify the task:

TierWhenPhases Used
SIMPLEQuick but thorough — single-domain analysis, clear questionPhases 1 + 5 only
MEDIUMResearch + analysis — multi-source synthesis, comparisonPhases 1-5
COMPLEXFull adversarial — architecture decisions, multi-system changes, irreversible actionsAll 8 phases

Phase 1: ORIENT (Memory + Context)

Load ALL available context before forming any opinion.

  1. Read project memory if available (e.g., MEMORY.md, SESSION.md, or status logs)
  2. Read known pitfalls and gotchas for the target domain (e.g., knowledge/gotchas.md if present)
  3. Read the most relevant local knowledge, READMEs, or chunk files
  4. Query any active workspace memory system (e.g., Honcho or agentmemory) when recent session continuity is needed
  5. Check conversation logs or history for prior attempts or context on this topic
  6. Identify which projects, modules, or systems are involved

If any tool fails: Note "degraded mode" and continue with available sources. Do NOT stall.


Phase 2: QUESTION (Challenge the Request)

Before solving, challenge the problem statement itself.

  1. Use a sequential thinking or reasoning tool (such as sequential-thinking MCP, if available) to decompose the problem into component parts
  2. Ask explicitly:
    • "Is this the right question? Is there a better framing?"
    • "What assumptions are embedded in this request?"
    • "What would go wrong if I do the obvious thing?"
    • "Has this been attempted before? What happened?"
  3. If the framing reveals a deeper issue, address THAT instead

Phase 3: RESEARCH (Evidence Collection)

Gather evidence from multiple sources. Do NOT rely on training data alone.

  1. Run web searches for current best practices, official documentation, and known issues
  2. Use available documentation search tools (such as Context7 MCP, if available) for any referenced libraries, frameworks, or APIs
  3. Check repository skills and custom workspace procedures (e.g., under .agent/skills/ or similar locations)
  4. Load domain-specific context or chunk files using workspace routing maps (e.g., WORKSPACE-AUTOMAP.md or equivalent)
  5. Review workspace-specific conventions and code style files (e.g., knowledge/conventions.md or local READMEs) for established patterns

Rule: Every factual claim must trace to a retrieved source, not memory.


Phase 4: AUDIT (Gap Analysis)

For architecture and system tasks. Skip for pure analysis questions.

  1. What exists? List all relevant files, tools, configs currently in place
  2. What SHOULD exist? Based on research and requirements, what's the ideal state?
  3. What's the delta? Enumerate every gap between current and ideal
  4. What's stale? Cross-reference sources for contradictions and outdated information
  5. What's broken? Check tool health, file integrity, reference validity

Phase 5: SYNTHESIZE (Multi-Perspective Analysis)

Evaluate the problem from multiple expert perspectives.

  1. Council of Experts — consider the problem as:
    • A Lead Developer: Is this technically sound? What are the edge cases?
    • A Business/Product Strategist: Does this serve the core project objectives and business outcomes?
    • A UX/Ops Pro: Is this maintainable? Will it create friction?
  2. Reconcile conflicts between perspectives and sources
  3. Produce a structured recommendation with:
    • The recommended approach
    • The key tradeoff
    • The risk if wrong
    • The success criteria

Phase 6: PLAN (Surgical Execution Design)

Break the recommendation into executable steps.

  1. Decompose into the smallest possible steps — each independently verifiable
  2. Run the 5-step Pre-Action Verification Protocol on EACH step:
    • Are assumptions stated?
    • Am I targeting the correct file/resource?
    • Is scope minimal?
    • Is action reversible?
    • What are the success criteria?
  3. Define measurable success criteria for the overall task
  4. Sequence steps so failures are caught early (dependencies first)

Phase 7: EXECUTE + VERIFY (Work → Check → Correct)

Execute the plan one step at a time.

  1. One step at a time. Verify output after each step before proceeding.
  2. Work → Verify → Self-Correct loop. Do not assume success.
  3. Anti-Bulk Enforcement: Use the most surgical edit mechanism available. Never rewrite files unless creating from scratch.
  4. 80% Confidence Threshold: If confidence drops below 80% on a high-stakes decision, STOP and ask the user/product owner for input.
  5. Follow Karpathy Doctrine: surgical, minimal, explicit.

Phase 8: CHALLENGE (Adversarial Self-Review)

After producing the result, attack it.

  1. Re-read the original request word by word. Did you actually answer THE question?
  2. What did you miss? What would a senior expert critique about this output?
  3. Would you stake your reputation on this? If not, what needs to change?
  4. Edge cases: What happens under unusual conditions? Empty inputs? Scale? Concurrent use?
  5. Save key learnings to the appropriate memory layer:
    • Debugging insights → update known gotchas (e.g., knowledge/gotchas.md)
    • New patterns → update conventions/standards (e.g., knowledge/conventions.md)
    • Decisions made → update decisions log (e.g., knowledge/decisions.md)
    • Recent continuity worth reusing soon → write a milestone/handoff breadcrumb to the workspace memory system (e.g., Honcho or agentmemory)

What This Workflow Does NOT Do

  • It does NOT replace /reason for quick analysis — use /reason for fast reasoning engine activation.
  • It does NOT save session state — that's /wrap.
  • It does NOT consolidate memory — that's /dream.

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