Install
$ agentstack add skill-cogni-ai-ou-cogni-ai-agent-skills-brainstorm ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Skill brainstorm
A cognitive framework and protocol for exploring options, breaking down complexities, and summarizing information.
Core Process
- Context Gathering & Research: Aggressively gather facts, existing data, and constraints before formulating any conclusions.
- Explore Options: Generate multiple orthogonal approaches or alternative options. Do not settle for the first apparent solution.
- Deconstruct Complexities: Break down the problem space into atomic, manageable components.
- Visual Summarization: Synthesize the gathered data and complexities into simple, easy-to-read Mermaid diagrams (e.g., mindmaps, block diagrams, or flowcharts).
Core Principles
- Design-It-Twice Protocol: ALWAYS generate at least two distinct architectural paths before recommending a preferred solution.
- Divergent Before Convergent: Ensure a broad exploration of the problem space (divergent thinking) before narrowing down to specific recommendations (convergent thinking).
- Fact-Based Exploration: Anchor all generated options in empirical data retrieved from the codebase, project memory, or provided context.
- Recursive Decomposition: Break every complex objective into its atomic components to manage cognitive load and ensure precision.
- Visual Clarity First: Use diagrams early to establish a shared mental model before diving into deep technical or textual analysis.
Diagnostics and Usage Patterns
- Component Architecture Visualization: Map high-level structural components with a Mermaid
block-betadiagram. - Context & Ecosystem Mapping: Map out the current ecosystem, constraints, and known unknowns using a Mermaid
mindmapbefore defining architectural changes. - Diagramming Focus:
Default to high-level topological or structural diagrams (block-beta, flowchart, mindmap, quadrantChart, radar-beta) to visualize options and establish facts.
- Flow & Logic Breakdown: Detail sequential states and dependencies using a Mermaid
flowchart. - Root Cause & Priority Mapping: Use
ishikawa-beta,quadrantChart, orradar-betafor evaluating alternatives, prioritization, and deep-dive problem exploration.
Brainstorming - Problem Breakdown
When you need to explore a complex problem, use this step-by-step visual approach to ensure all facts are gathered and complexity is reduced:
Step 1: Context & Ecosystem Mapping
Before proposing any changes, gather all relevant facts and constraints. Map the existing environment using a mindmap.
%% This diagram visualizes the existing ecosystem and constraints
mindmap
root((System Context))
Dependencies
External API
Database
Constraints
Performance
Security
Known Unknowns
Rate limits
Step 2: Component Architecture Visualization
Break the problem into structural parts and orthogonal options using a block-beta diagram so the options can be compared effectively.
%% This block diagram shows multiple architectural options
block-beta
columns 3
space Option1 space
FrontendA DatabaseA CacheA
space Option2 space
FrontendB DatabaseB CacheB
Step 3: Flow & State Modeling
Finally, visualize the behavior, state changes, or sequential logic required for the proposed options using a flowchart.
%% This flowchart explores a process logic option
flowchart LR
Start --> CheckState{Is Valid?}
CheckState -->|Yes| Process[Process Data]
CheckState -->|No| Reject[Reject Request]
Step 4: Root Cause Exploration (If Applicable)
When brainstorming around a systemic issue or failure, use an ishikawa-beta (fishbone) diagram to aggressively deconstruct contributing factors before jumping to conclusions.
%% This diagram categorizes contributing factors to a problem
ishikawa-beta
Core Problem or Failure
Infrastructure
Network latency
Codebase
Tech debt
Missing tests
Dependencies
Deprecated API
Step 5: Prioritization Mapping
When multiple paths, options, or tasks are generated, map them onto a quadrantChart to evaluate trade-offs like effort versus impact.
%% This diagram visualizes task or option prioritization
quadrantChart
title Option Prioritization
x-axis Low Effort --> High Effort
y-axis Low Impact --> High Impact
quadrant-1 Quick Wins
quadrant-2 Strategic
quadrant-3 Time Sinks
quadrant-4 Fill-ins
"Option A": [0.2, 0.8]
"Option B": [0.8, 0.9]
"Option C": [0.7, 0.3]
Step 6: Trade-off Analysis
For complex architectural decisions, use a radar-beta diagram to score options across multiple competing dimensions.
%% This diagram scores options across various constraints
radar-beta
title Architectural Trade-offs
axis Performance, Security, Maintainability, Cost-Efficiency, Scalability
curve Performance {8, 7, 6, 4, 9}
curve Scalability {6, 9, 8, 7, 5}
What to Avoid
- Assumption-Driven Brainstorming: Relying on guesses instead of factual context gathered through tools.
- False Dichotomies: Assuming only two opposing solutions exist without exploring orthogonal architectural paths.
- Overcomplicated Diagrams: Creating massive, unreadable diagrams. Break them into smaller, focused visual summaries.
- Premature Convergence: Proposing a final solution without explicitly documenting the discarded alternative options.
Related Skills
- brainstorm-agent-runs: You MUST load this skill when identifying agentic runs in CI/CD for a Pull Request.
- brainstorm-github-pr: You MUST load this skill when asked to analyze or brainstorm a Pull Request.
- critical-thinking: You MUST load this skill when evaluating the options generated during brainstorming.
- mermaid: You MUST load this skill when constructing standard Mermaid diagrams.
- mermaid-beta: You MUST load this skill when using experimental Mermaid diagrams.
- minizinc: You MUST load this skill when executing or deeply modeling constraint satisfaction problems.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Cogni-AI-OU
- Source: Cogni-AI-OU/cogni-ai-agent-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.