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/alonf/mcppythondemo/agent-conductnpx skills add alonf/MCPPythonDemo --skill agent-conductgit clone --depth 1 https://github.com/alonf/MCPPythonDemoWhat 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 | $0.00012 | $0.00296 |
| Opus 5 | $0.00006 | $0.00148 |
| Sonnet 5 | $0.00002 | $0.00059 |
| Haiku 4.5 | $0.00001 | $0.00030 |
Grade A, and why
agent-conduct 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 yesterday.
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.
This is a copy
100% identical to agent-conduct — 48 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Context
Every squad agent must follow these two hard rules. They were previously duplicated in every charter. Now they live here as a shared skill, loaded once.
Patterns
Product Isolation Rule (hard rule)
Tests, CI workflows, and product code must NEVER depend on specific agent names from any particular squad. "Our squad" must not impact "the squad." No hardcoded references to agent names (Flight, EECOM, FIDO, etc.) in test assertions, CI configs, or product logic. Use generic/parameterized values. If a test needs agent names, use obviously-fake test fixtures (e.g., "test-agent-1", "TestBot").
Peer Quality Check (hard rule)
Before finishing work, verify your changes don't break existing tests. Run the test suite for files you touched. If CI has been failing, check your changes aren't contributing to the problem. When you learn from mistakes, update your history.md.
Anti-Patterns
- Don't hardcode dev team agent names in product code or tests
- Don't skip test verification before declaring work done
- Don't ignore pre-existing CI failures that your changes may worsen
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.
- yesterday First seen · 25 lines · 12 tokens per session scan A 9af6ff3cf378
agent-conduct is a skill published in the GitHub repository alonf/MCPPythonDemo (0 stars, last pushed 4mo ago), licensed MIT. It adds 12 tokens to every session and 296 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agent-conduct, differing in 48 lines, and is treated as a copy.
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