// [AI & Tools] Use when you need to apply structured, reflective problem-solving for complex tasks requiring multi-step analysis, revision capability, and hypothesis verification.
[HINT] Download the complete skill directory including SKILL.md and all related files
| name | sequential-thinking |
| version | 1.0.0 |
| description | [AI & Tools] Use when you need to apply structured, reflective problem-solving for complex tasks requiring multi-step analysis, revision capability, and hypothesis verification. |
| license | MIT |
Goal: Solve complex problems through structured, reflective thought sequences with dynamic adjustment and revision.
Workflow:
Key Rules:
Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).
Structured problem-solving via manageable, reflective thought sequences with dynamic adjustment.
Thought 1/5: [Initial analysis]
Adjust dynamically as understanding evolves.
Thought 5/8 [REVISION of Thought 2]: [Corrected understanding]
- Original: [What was stated]
- Why revised: [New insight]
- Impact: [What changes]
Thought 4/7 [BRANCH A from Thought 2]: [Approach A]
Thought 4/7 [BRANCH B from Thought 2]: [Approach B]
Compare explicitly, converge with decision rationale.
Thought 6/9 [HYPOTHESIS]: [Proposed solution]
Thought 7/9 [VERIFICATION]: [Test results]
Iterate until hypothesis verified.
Mark final: Thought N/N [FINAL]
Complete when:
Explicit: Use visible thought markers when complexity warrants visible reasoning or user requests breakdown.
Implicit: Apply methodology internally for routine problem-solving where thinking aids accuracy without cluttering response.
Optional scripts for deterministic validation/tracking:
scripts/process-thought.js - Validate & track thoughts with historyscripts/format-thought.js - Format for display (box/markdown/simple)See README.md for usage examples. Use when validation/persistence needed; otherwise apply methodology directly.
Load when deeper understanding needed:
references/core-patterns.md - Revision & branching patternsreferences/examples-api.md - API design examplereferences/examples-debug.md - Debugging examplereferences/examples-architecture.md - Architecture decision examplereferences/advanced-techniques.md - Spiral refinement, hypothesis testing, convergencereferences/advanced-strategies.md - Uncertainty, revision cascades, meta-thinking[IMPORTANT] Use
TaskCreateto break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.
AI Mistake Prevention — Failure modes to avoid on every task:
Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.
Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.
MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.
MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.
IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
IMPORTANT MUST ATTENTION add a final review todo task to verify work quality
[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.