Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/avinashp/agentsatlas/brainstormingnpx skills add AvinashP/AgentsAtlas --skill brainstorminggit clone --depth 1 https://github.com/AvinashP/AgentsAtlasWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/avinashp/agentsatlas/brainstorming)<a href="https://agentmods.dev/skills/avinashp/agentsatlas/brainstorming"><img src="https://agentmods.dev/badge/skills/avinashp/agentsatlas/brainstorming.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00030 | $0.01054 |
| Opus 5 | $0.00015 | $0.00527 |
| Sonnet 5 | $0.00006 | $0.00211 |
| Haiku 4.5 | $0.00003 | $0.00105 |
Grade A, and why
brainstorming scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brainstorming Ideas Into Designs
Turn rough ideas into fully formed designs through natural collaborative dialogue.
The Process
1. Understanding the Idea
Before proposing anything:
- Check out the current project state (files, docs, recent commits)
- Ask questions one at a time to refine the idea
- Prefer multiple choice questions when possible
- Focus on understanding: purpose, constraints, success criteria
One question at a time - Don't overwhelm with multiple questions. If a topic needs more exploration, break it into multiple questions.
2. Exploring Approaches
Once you understand the problem:
- Propose 2-3 different approaches with trade-offs
- Lead with your recommended option and explain why
- Present options conversationally
Example:
For caching, I see three approaches:
1. In-memory cache (recommended) - Simple, no dependencies,
works well for your scale. Trade-off: lost on restart.
2. Redis - Shared across instances, persistent.
Trade-off: Extra infrastructure.
3. File-based - Simple, persistent.
Trade-off: Slower, file locking complexity.
I'd recommend #1 unless you need persistence or multi-instance.
What matters most to you?
3. Presenting the Design
Once you believe you understand what you're building:
- Present the design in sections of 200-300 words
- Ask after each section whether it looks right so far
- Cover: architecture, components, data flow, error handling, testing
- Be ready to go back and clarify if something doesn't make sense
Example section:
## Data Flow
When a user submits a form:
1. Frontend validates input, shows inline errors
2. POST to /api/submit with JSON body
3. Backend validates again (never trust client)
4. Store in database, return ID
5. Frontend shows success with link to view
Does this flow make sense, or should we handle any edge cases differently?
After the Design
Documentation:
- Write the validated design to a design doc if appropriate
- Include: goal, architecture, components, data flow, testing approach
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 158 lines · 30 tokens per session scan A 358f19a68fe4
brainstorming is a skill published in the GitHub repository AvinashP/AgentsAtlas (7 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 1,054 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
vibe-research
Deep research and market validation for app ideas. Use when starting a new project, validating an idea, or when the user says "research my idea", "validate my app", or "help me start a new project".
trellis-brainstorm
Guides collaborative requirements discovery before implementation. Creates task directory, seeds PRD, asks high-value questions one at a time, researches technical choices, and converges on MVP scope. Use when requirements are unclear, there are multiple valid approaches, or the user describes a new feature or complex…
kn-research
Use when you need to understand existing code, find patterns, search project knowledge, investigate current external facts, or explore a large codebase before implementation.
trellis-session-insight
Reach into past AI conversation history through the trellis mem CLI. Use whenever the user asks 'how did we solve X last time', 'have we discussed this before', 'what was the decision on X', 'remind me what we did in this task', '上次怎么解的', '之前讨论过吗', '想起一段对话', or when starting a brainstorm that overlaps prior work…
kn-init
Use at the start of a new session to read project docs, understand context, and see current state.
trellis-channel
Use Trellis channel for live multi-agent collaboration, spawned workers, cross-agent review, progress inspection, forum channels, and channel log debugging.