Deep think maximum cognitive effort protocol mq8kvw92
Skill nexu-io/open-design/plugins/community/deep-think-maximum-cognitive-effort-protocol-mq8kvw92
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Use this plugin when the user wants a maximum-effort reasoning workflow for a complex, high-stakes, or ambiguous task.
SKILL.md
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/deep-think β 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:
| Tier | When | Phases Used |
|---|---|---|
| SIMPLE | Quick but thorough β single-domain analysis, clear question | Phases 1 + 5 only |
| MEDIUM | Research + analysis β multi-source synthesis, comparison | Phases 1-5 |
| COMPLEX | Full adversarial β architecture decisions, multi-system changes, irreversible actions | All 8 phases |
Phase 1: ORIENT (Memory + Context)
Load ALL available context before forming any opinion.
- Read project memory if available (e.g.,
MEMORY.md,SESSION.md, or status logs) - Read known pitfalls and gotchas for the target domain (e.g.,
knowledge/gotchas.mdif present) - Read the most relevant local knowledge, READMEs, or chunk files
- Query any active workspace memory system (e.g., Honcho or agentmemory) when recent session continuity is needed
- Check conversation logs or history for prior attempts or context on this topic
- 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.
- Use a sequential thinking or reasoning tool (such as sequential-thinking MCP, if available) to decompose the problem into component parts
- 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?"
- 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.
- Run web searches for current best practices, official documentation, and known issues
- Use available documentation search tools (such as Context7 MCP, if available) for any referenced libraries, frameworks, or APIs
- Check repository skills and custom workspace procedures (e.g., under
.agent/skills/or similar locations) - Load domain-specific context or chunk files using workspace routing maps (e.g.,
WORKSPACE-AUTOMAP.mdor equivalent) - Review workspace-specific conventions and code style files (e.g.,
knowledge/conventions.mdor 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.
- What exists? List all relevant files, tools, configs currently in place
- What SHOULD exist? Based on research and requirements, what's the ideal state?
- What's the delta? Enumerate every gap between current and ideal
- What's stale? Cross-reference sources for contradictions and outdated information
- What's broken? Check tool health, file integrity, reference validity
Phase 5: SYNTHESIZE (Multi-Perspective Analysis)
Evaluate the problem from multiple expert perspectives.
- Council of Experts β 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?
- Reconcile conflicts between perspectives and sources
- 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.
- Decompose into the smallest possible steps β each independently verifiable
- 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?
- Define measurable success criteria for the overall task
- Sequence steps so failures are caught early (dependencies first)
Phase 7: EXECUTE + VERIFY (Work β Check β Correct)
Execute the plan one step at a time.
- One step at a time. Verify output after each step before proceeding.
- Work β Verify β Self-Correct loop. Do not assume success.
- Anti-Bulk Enforcement: Use the most surgical edit mechanism available. Never rewrite files unless creating from scratch.
- 80% Confidence Threshold: If confidence drops below 80% on a high-stakes decision, STOP and ask the user/product owner for input.
- Follow Karpathy Doctrine: surgical, minimal, explicit.
Phase 8: CHALLENGE (Adversarial Self-Review)
After producing the result, attack it.
- Re-read the original request word by word. Did you actually answer THE question?
- What did you miss? What would a senior expert critique about this output?
- Would you stake your reputation on this? If not, what needs to change?
- Edge cases: What happens under unusual conditions? Empty inputs? Scale? Concurrent use?
- Save key learnings to the appropriate memory layer:
- Debugging insights β update known gotchas (e.g.,
knowledge/gotchas.md) - New patterns β update conventions/standards (e.g.,
knowledge/conventions.md) - Decisions made β update decisions log (e.g.,
knowledge/decisions.md) - Recent continuity worth reusing soon β write a milestone/handoff breadcrumb to the workspace memory system (e.g., Honcho or agentmemory)
- Debugging insights β update known gotchas (e.g.,
What This Workflow Does NOT Do
- It does NOT replace
/reasonfor quick analysis β use /reason for fast reasoning engine activation. - It does NOT save session state β that's
/wrap. - It does NOT consolidate memory β that's
/dream.