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 skills add TechNickAI/ai-coding-config --skill brainstorm-synthesisgit clone --depth 1 https://github.com/TechNickAI/ai-coding-configWrote 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/technickai/ai-coding-config/brainstorm-synthesis)<a href="https://agentmods.dev/skills/technickai/ai-coding-config/brainstorm-synthesis"><img src="https://agentmods.dev/badge/skills/technickai/ai-coding-config/brainstorm-synthesis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/technickai/ai-coding-config/brainstorm-synthesis"><img src="https://agentmods.dev/badge/skills/technickai/ai-coding-config/brainstorm-synthesis.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00032 | $0.01064 |
| Opus 5.5 | $0.00013 | $0.00426 |
| Sonnet 5.5 | $0.00006 | $0.00213 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
brainstorm-synthesis 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 25d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This is the F-thread (fusion) pattern: multiple independent analyses converge into one superior answer. Use it when the decision is hard enough that diverse viewpoints add value.
Skip for straightforward implementations, well-established patterns, or decisions where one approach is obviously correct.
Standard perspectives:
- Pragmatist: Simplest solution that works, minimize complexity
- Architect: Long-term maintainability, extensibility, patterns
- Performance: Efficiency, scalability, resource usage
- Security: Attack surface, data protection, access control
- User-focused: UX impact, developer experience, API ergonomics
Domain-specific perspectives (add based on problem):
- Data engineer: For data pipeline decisions
- DevOps: For deployment and infrastructure choices
- Testing: For testability and verification concerns
- Integration: For API and system boundary decisions
Choose perspectives that will genuinely disagree. Similar viewpoints waste the pattern's value. The goal is productive tension that surfaces trade-offs.
Launch agents in parallel using the Task tool. Each agent receives:
- The problem statement
- Their assigned perspective
- Instructions to return: recommended approach, key trade-offs, complexity estimate, risks
Wait for all agents to complete. Agents work independently without seeing each other's responses.
Synthesize by identifying where agents agree (high-confidence elements), where they disagree (trade-off zones), and which perspective's concerns are most relevant given actual constraints.
Produce a unified recommendation that incorporates the strongest elements from multiple approaches while maintaining coherence.
Trade-off resolution: When agents disagree, evaluate based on actual project constraints. The pragmatist might win for a prototype; the architect for a core system.
Risk integration: Incorporate security and performance concerns as constraints on the chosen approach rather than alternative approaches.
Complexity calibration: If most agents flag high complexity, the problem may need decomposition before a single solution emerges.
Recommended Approach: Clear description of the unified solution
Why This Approach: Key factors that made this the best choice
Integrated Trade-offs:
- [Trade-off 1]: How the solution handles it
- [Trade-off 2]: How the solution handles it
Perspectives Incorporated:
- From Pragmatist: [element included]
- From Architect: [element included]
- From Security: [constraint applied]
Complexity: [Low | Medium | High] with brief justification
Dissenting Views: Any perspective whose core concern wasn't fully addressed, and why the trade-off was acceptable
Return a concise recommendation that autotask can incorporate into the plan. The full synthesis rationale goes into the design decisions section of the eventual PR.
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.
- 25d ago First seen · 141 lines · 32 tokens per session scan A a16005e0e341
brainstorm-synthesis is a skill published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 26d ago), licensed MIT. It adds 32 tokens to every session and 1,064 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-22.
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