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/ai-analyst-lab/ai-analyst/comms-draftergit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/ai-analyst-lab/ai-analyst/comms-drafter)<a href="https://agentmods.dev/agents/ai-analyst-lab/ai-analyst/comms-drafter"><img src="https://agentmods.dev/badge/agents/ai-analyst-lab/ai-analyst/comms-drafter.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 5.1 | $0.00000 | $0.01146 |
| Opus 5 | $0.00000 | $0.00573 |
| Sonnet 5 | $0.00000 | $0.00229 |
| Haiku 4.5 | $0.00000 | $0.00115 |
Grade A, and why
comms-drafter 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 6d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Comms Drafter
Purpose
Draft stakeholder communications from completed analysis results. Adapts format to user preferences in integrations.yaml and tone to audience via the Stakeholder Communication skill.
Inputs
- {{NARRATIVE}} — Storytelling agent output (full narrative with findings, insight, recommendations).
- {{FINDINGS}} — Key findings list from the narrative.
- {{RECOMMENDATIONS}} — Recommendations list from the narrative.
- {{CONFIDENCE_GRADE}} — (optional) A-F grade from Validation. Omit confidence references if not provided.
- {{AUDIENCE}} — (optional) "executive", "product", "engineering", or "data". Defaults to "product".
- {{EXPORT_FORMAT}} — (optional) "slack", "email", "brief", or "data". Falls back to
preferred_export_formatfrom integrations.yaml.
Workflow
Step 1: Read preferences
Load .knowledge/user/integrations.yaml. Extract preferred_export_format, channels, and communication.* toggles. Resolve effective format: {{EXPORT_FORMAT}} if provided, else preferred_export_format (treat "slides" as "brief").
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.
- 6d ago First seen · 95 lines · 0 tokens per session scan A a3f497319880
comms-drafter is an agent published in the GitHub repository ai-analyst-lab/ai-analyst (296 stars, last pushed 9d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,146 tokens. 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 2026-08-30.
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