Sequential Think

by Dianel555

skill

|

Skill Details

Repository Files

4 files in this skill directory


name: sequential-think description: | Multi-step reasoning engine for complex analysis and systematic problem solving. Use when: (1) Complex debugging scenarios with multiple layers, (2) Architectural analysis and system design, (3) Problems requiring hypothesis testing and validation, (4) Multi-component failure investigation, (5) Performance bottleneck identification. Triggers: "--think", "--think-hard", "--ultrathink", "analyze step by step", "break down this problem", "systematic analysis". IMPORTANT: Do NOT use for simple single-step tasks.

Sequential Think

Structured iterative thinking for complex problem-solving. Standalone CLI only (no MCP dependency).

Execution Methods

Run scripts/sequential_think_cli.py via Bash:

# Process a thought
python scripts/sequential_think_cli.py think \
  --thought "First, let me analyze the problem structure..." \
  --thought-number 1 \
  --total-thoughts 5

# Continue thinking chain
python scripts/sequential_think_cli.py think \
  --thought "Based on step 1, I hypothesize that..." \
  --thought-number 2 \
  --total-thoughts 5

# Revise a previous thought
python scripts/sequential_think_cli.py think \
  --thought "Reconsidering step 1, I realize..." \
  --thought-number 3 \
  --total-thoughts 5 \
  --is-revision \
  --revises-thought 1

# Branch into alternative path
python scripts/sequential_think_cli.py think \
  --thought "Alternative approach: what if we..." \
  --thought-number 4 \
  --total-thoughts 6 \
  --branch-from 2 \
  --branch-id "alt-approach"

# Final thought (complete chain)
python scripts/sequential_think_cli.py think \
  --thought "Conclusion: the solution is..." \
  --thought-number 5 \
  --total-thoughts 5 \
  --no-next

# View thought history
python scripts/sequential_think_cli.py history [--format json|text]

# Clear thought history
python scripts/sequential_think_cli.py clear

Core Principles

Iterative Thinking Process

  • Each tool call = one "thought" in the chain
  • Build upon, question, or revise previous thoughts
  • Express uncertainty when it exists

Dynamic Thought Count

  • Start with initial estimate of totalThoughts
  • Adjust up/down as understanding evolves
  • Add more thoughts even after reaching initial end

Hypothesis-Driven Approach

  1. Generate hypotheses as potential solutions emerge
  2. Verify hypotheses based on chain-of-thought steps
  3. Repeat until satisfied with solution

Completion Criteria

  • Only set nextThoughtNeeded: false when truly finished
  • Must have satisfactory, verified answer
  • Don't rush to conclusion

When to Use

Scenario Use Sequential Think
Complex debugging (3+ layers) ✅ Yes
Architectural analysis ✅ Yes
Multi-component investigation ✅ Yes
Performance bottleneck analysis ✅ Yes
Root cause analysis ✅ Yes
Simple explanation ❌ No
Single-file change ❌ No
Straightforward fix ❌ No

Parameters

Parameter Type Required Description
thought string Yes Current thinking step content
thoughtNumber int Yes Current position in sequence (1-based)
totalThoughts int Yes Estimated total thoughts needed
nextThoughtNeeded bool No Whether more thinking needed (default: true)
isRevision bool No Whether this revises previous thinking
revisesThought int No Which thought number is being reconsidered
branchFromThought int No Branching point thought number
branchId string No Identifier for current branch
needsMoreThoughts bool No Signal that more thoughts needed beyond estimate

Output Format

{
  "thoughtNumber": 3,
  "totalThoughts": 5,
  "nextThoughtNeeded": true,
  "branches": ["alt-approach"],
  "thoughtHistoryLength": 3
}

Workflow Pattern

Phase 1: Problem Decomposition

Thought 1: Identify problem scope and constraints
Thought 2: Break into sub-problems
Thought 3: Identify dependencies between sub-problems

Phase 2: Hypothesis Generation

Thought 4: Generate initial hypothesis
Thought 5: Identify evidence needed to verify

Phase 3: Verification & Iteration

Thought 6: Test hypothesis against evidence
Thought 7: Revise if needed (isRevision=true)
Thought 8: Branch if alternative path promising

Phase 4: Conclusion

Final Thought: Synthesize findings, provide answer (nextThoughtNeeded=false)

Best Practices

  1. Start with estimate, adjust as needed

    • Initial totalThoughts is just a guess
    • Increase if problem more complex than expected
    • Decrease if solution found early
  2. Use revisions for course correction

    • Mark isRevision=true when reconsidering
    • Reference revisesThought for clarity
  3. Branch for alternative approaches

    • Use branchFromThought to explore alternatives
    • Give meaningful branchId names
  4. Filter irrelevant information

    • Each thought should advance toward solution
    • Ignore tangential details
  5. Don't rush completion

    • Only nextThoughtNeeded=false when truly done
    • Verify hypothesis before concluding

Anti-Patterns

Prohibited Correct
Use for simple tasks Reserve for complex multi-step problems
Skip thought numbers Always increment correctly
Conclude without verification Verify hypothesis before final thought
Ignore previous thoughts Build upon or explicitly revise
Fixed totalThoughts Adjust as understanding evolves

Integration with Other Tools

With ACE-Tool

ACE-Tool → align current state → Sequential Think → analyze and plan

With Context7

Sequential Think → coordinate analysis → Context7 → provide official patterns

With Serena

Serena → symbol-level exploration → Sequential Think → systematic analysis

Related Skills

Attack Tree Construction

Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.

skill

Grafana Dashboards

Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.

skill

Matplotlib

Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.

skill

Scientific Visualization

Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.

skill

Seaborn

Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.

skill

Shap

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model

skill

Pydeseq2

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

skill

Query Writing

For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations

skill

Pydeseq2

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

skill

Scientific Visualization

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

skill

Skill Information

Category:Skill
Last Updated:1/30/2026