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 notque/vexjoy-agent --skill topic-brainstormergit clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/topic-brainstormer)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/topic-brainstormer"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/topic-brainstormer/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/notque/vexjoy-agent/topic-brainstormer"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/topic-brainstormer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00019 | $0.01166 |
| Opus 5.5 | $0.00008 | $0.00466 |
| Sonnet 5.5 | $0.00004 | $0.00233 |
| Haiku 4.5 | $0.00002 | $0.00117 |
Grade A, and why
topic-brainstormer 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Topic Brainstormer
Generate blog topic ideas that align with a content identity built around solving frustrating technical problems. Three phases: ASSESS, DECIDE, GENERATE. Every topic must pass the three-question content quality filter before presentation.
Deep References
| Signal | Load | Content |
|---|---|---|
| Filtering topics: three-question test, category examples | references/content-filter.md |
Full quality filter with green/red/yellow topic categories, salvage patterns |
| Scoring: impact, vex, resolution matrix | references/priority-scoring.md |
Rubrics per dimension, score ranges, calibration, worked examples |
| Mining: problem sources, gap analysis, expansion | references/topic-sources.md |
Mining prompts, signal strength, gap types, expansion strategies |
Phase 1: ASSESS
Gather context about existing content and available sources.
- Read all posts in the content directory. Document post count, content clusters, technologies covered, last post date.
- Identify available sources: problem mining (recent debugging, errors, config struggles), gap analysis (cross-references leading nowhere), tech expansion (adjacent technologies).
- Extract all "see also" and cross-reference mentions. Flag any pointing to content that does not exist.
Gate: Content landscape documented, at least 2 sources identified with material.
Phase 2: DECIDE
Generate candidates and filter through the content quality test.
- Mine 5-10 raw candidates from at least 2 sources. Capture: source type, raw topic, initial vex signal.
- Apply the content quality filter -- every candidate must answer YES to all three:
- Was there genuine frustration? Real time lost, failed attempts, unclear docs, unexpected behavior.
- Is there a satisfying resolution? Clear fix, understanding gained, prevention strategy, "a-ha moment."
- Would this help others? Reproducible problem, actionable solution, relatable frustration.
- Reject failing candidates. Document each rejection: topic, failed question, reason.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 26d ago Changed · -69 lines · +1 tokens per session 445ea2b3dcc6
- 27d ago Changed · -160 lines 8452e36b7a15
- 1mo ago First seen · 265 lines · 19 tokens per session scan A 26ff03cc0293
topic-brainstormer is a skill published in the GitHub repository notque/vexjoy-agent (442 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 1,166 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 2026-09-03.
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