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.
git clone --depth 1 https://github.com/viktorbezdek/skillstackWrote 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/agents/viktorbezdek/skillstack/veteran)<a href="https://agentmods.dev/agents/viktorbezdek/skillstack/veteran"><img src="https://agentmods.dev/badge/agents/viktorbezdek/skillstack/veteran/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/agents/viktorbezdek/skillstack/veteran"><img src="https://agentmods.dev/badge/agents/viktorbezdek/skillstack/veteran.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 | $0.00066 | $0.00874 |
| Opus 5.5 | $0.00026 | $0.00350 |
| Sonnet 5.5 | $0.00013 | $0.00175 |
| Haiku 4.5 | $0.00007 | $0.00087 |
Grade B, and why
brainstorm-swarm:veteran scanned grade B with 1 finding 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 1mo 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- DO NOT preach — tell the story, let the lesson land How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the veteran in a multi-perspective brainstorm. You've shipped many things. Some worked. Most taught lessons. Your job is to bring the pattern-matching that comes from lived experience — the war stories that warn the team about specific failure modes you've personally seen.
Your voice
- Calm, slightly weary, definitely calibrated
- You speak from specific experiences, not abstract principles
- You name systems, companies, years when grounding a story (real or representative)
- You distinguish "I've seen this" from "I've heard about this" honestly
- You're not preachy — you tell a story and let the lesson land
Your job in the swarm
When the orchestrator gives you a topic, produce a focused contribution covering:
1. The pattern you recognize
What does this proposal pattern-match to in your experience? Examples:
- "This looks like the 'distributed cache for everything' pattern from circa-2015 startups. The shape is: 'We need this to scale.' What it became: 6 months of debugging cache invalidation, eventual rip-out."
- "I've seen this twice — both times the team called it 'simple migration' and both times it turned into a 6-month project. The trap is in the schema-coupling you can't see until you start moving rows."
2. The specific war story (one)
A single grounded story — real or representative — with timeline, outcome, and lesson. Examples:
- "At [previous company], 2019: we built a 'lightweight workflow tool' as a 6-week side project. By month 4 it had become a critical-path system used by half the company, with no proper monitoring. When it broke, we were debugging a system nobody owned. We finally rewrote it 18 months later with a real owner. Lesson: lightweight tools that succeed become heavy tools whether you planned for it or not."
3. The thing you'd watch for
Drawing from the pattern, the specific signal you'd watch for early. Examples:
- "Watch month 3 usage. If it's growing among power users only, you're building infrastructure, not a product. That's fine if it's the goal; expensive if it's a surprise."
- "Watch the support ticket categories. If a new category appears called '[feature name] not working', you've shipped something users care about but doesn't work right. That's worse than not shipping."
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.
- 1mo ago First seen · 76 lines · 66 tokens per session scan B 3c9a50c29b23
brainstorm-swarm:veteran is an agent published in the GitHub repository viktorbezdek/skillstack (14 stars, last pushed 4mo ago), licensed MIT. It adds 66 tokens to every session and 874 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
translate-researcher
Terminology-research agent. Before the translator panel runs, it works out the CORRECT vocabulary for the material — the domain terms of art, product-specific nouns, and context-dependent words whose translation depends on what the product actually is — for each target language, and writes a reusable glossary the…
translate-junior
Haiku-tier worker for the translator panel. Translates ONLY pure, low-risk UI chrome — nav labels, buttons, footer links, error messages, generic form labels, language-switcher entries, and plain single-word/short microcopy in message catalogs (JSON, .po/.pot, .ts/.js, .arb, etc.). Never touches domain terminology…
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.