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/cveralyon/axel-setup/draft-messagegit clone --depth 1 https://github.com/cveralyon/axel-setupWhat 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 | $0.00015 | $0.00170 |
| Opus 5 | $0.00008 | $0.00085 |
| Sonnet 5 | $0.00003 | $0.00034 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
draft-message 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 2d 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.
What it actually says
Help the user structure a message for an important conversation.
- Understand context: who is the audience, what's the topic, what outcome is desired
- Organize key points logically — lead with the most important thing
- Connect technical decisions to business impact where relevant
- Anticipate pushback and prepare responses
- Keep tone professional but direct
Output options:
- Slack-ready — formatted for copy-paste (default)
- Talking points — bullets for a call/meeting
- Email — structured with subject line
Be extra clear, structured, and actionable. Prioritize the key message and strip noise.
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.
- 2d ago First seen · 20 lines · 15 tokens per session scan A c12c6d721191
draft-message is an agent published in the GitHub repository cveralyon/axel-setup (4 stars, last pushed 1mo ago), licensed MIT. It adds 15 tokens to every session and 170 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
Other agents, from other repositories
feature-flow
Build, test, verify, and review an already planned feature. Operates on a feature branch off trunk; prepares a PR but does not merge.
review
Review PR and build output for quality, security, and compliance. Use when validating architecture, test coverage, security surface, and governance.
syllago-author
/home/hhewett/.local/src/syllago/content/agents/syllago-author/AGENT.md.
design
Convert the specification into a clear, actionable technical design with architecture, components, interfaces, and data flows. Use when translating requirements into a buildable system design.
learn
Product retrospective agent. Runs after a release, after a measure agent anomaly flag, or at end of sprint. Maps findings to DORA AI capabilities and produces plan agent action items. Distinct from fawkes learn.md which handles platform incident postmortems.
spec
Convert a human request into a clear, structured specification with requirements, acceptance criteria, and policy alignment. Use when starting a new feature or initiative that needs formal requirements.