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 agents/activ8-ai/teamwork-mcp/geminigit clone --depth 1 https://github.com/Activ8-AI/Teamwork-MCPWrote 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/activ8-ai/teamwork-mcp/gemini)<a href="https://agentmods.dev/agents/activ8-ai/teamwork-mcp/gemini"><img src="https://agentmods.dev/badge/agents/activ8-ai/teamwork-mcp/gemini.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 | $0.00000 | $0.00734 |
| Opus 5 | $0.00000 | $0.00367 |
| Sonnet 5 | $0.00000 | $0.00147 |
| Haiku 4.5 | $0.00000 | $0.00073 |
Grade A, and why
gemini 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 3d 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.
This is a copy
94% identical to chatgpt — 19 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.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini Agent Instructions — @vizioz/teamwork-mcp
Charter binding: Activ8 AI Operational Execution & Accountability Charter (v1.5).
Start here
docs/SOURCES-OF-TRUTH.md(this repo)docs/AUDIENCE-SURFACE-CONTRACT.md(this repo)- Central canonical map:
https://github.com/Activ8-AI/activ8-ai-unified-mcp-serveratdocs/SOURCES-OF-TRUTH.md
Output contract
Progress | Evidence | Blockers
Seek-First Planning Gate
- No action begins without a plan.
- Verify in order: Notion first, then repo, then local/runtime files.
- Search for existing artifacts before touching or proposing anything new.
- Build on lineage before create-new.
Seek First to Understand + Verify What Exists
- Seek First to Understand: before answering, deciding, or acting, gather context and ensure full comprehension.
- Verify what exists in Notion: never assume. Check Notion first. Confirm presence, accuracy, and status of relevant information before proceeding.
- Search for existing artifacts: look for relevant databases, pages, prior work, and connected surfaces before touching, modifying, or proposing anything new.
- Build on established work: extend, refine, or elevate what exists. Respect artifact lineage.
- Create new only when necessary: new artifacts or structures only when no suitable reference, structure, or precedent exists.
- Fail closed on deviation: if verification is missing, the user correction changes the path, or drift is detected, stop, surface the mismatch, and restart from verified state.
Persistent Learning System Contract
- MAOS is a persistent learning system that happens to execute work.
- Govern work through sensing, thinking, execution, and learning surfaces.
- The minimum learning loop is
Experience -> Extraction -> Structuring -> Storage -> Retrieval -> Application -> Feedback. - Learning is not complete until the result is stored, indexed, and reused automatically.
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.
- 3d ago First seen · 62 lines · 0 tokens per session scan A 4811abfc10b4
gemini is an agent published in the GitHub repository Activ8-AI/Teamwork-MCP (0 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 734 tokens. A static security scan graded it A with 0 findings. It is 94% identical to chatgpt, differing in 19 lines, and is treated as a copy.
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