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 skills add tomzx/agents --skill stakeholder-announcementgit clone --depth 1 https://github.com/tomzx/agentsWrote 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/skills/tomzx/agents/stakeholder-announcement)<a href="https://agentmods.dev/skills/tomzx/agents/stakeholder-announcement"><img src="https://agentmods.dev/badge/skills/tomzx/agents/stakeholder-announcement/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/skills/tomzx/agents/stakeholder-announcement"><img src="https://agentmods.dev/badge/skills/tomzx/agents/stakeholder-announcement.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 | $0.00064 | $0.01118 |
| Opus 5 | $0.00032 | $0.00559 |
| Sonnet 5 | $0.00013 | $0.00224 |
| Haiku 4.5 | $0.00006 | $0.00112 |
Grade A, and why
stakeholder-announcement 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 7d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stakeholder Announcement
Drafts a structured progress announcement from recent work context (overall summary, GitHub activity, or explicit description) and posts it to the appropriate stakeholder Slack channels. Delegates posting to post-slack-message.
Prerequisites
SLACK_TOKEN,SLACK_COOKIE, andSLACK_USERin.env(same credentials used byslack-kb-individual)- post-slack-message skill available
- Activity context: either an overall summary file (e.g.,
{NOTES_DIR}/{YEAR}/{MONTH}/{DAY}.overall.md), a GitHub activity file, or an explicit topic description from the user
Inputs
- topic: the subject of the announcement (e.g., "batch inference GSA creation", "B300 cluster preparation", "WIF setup complete"). If omitted, inferred from the context file.
- context: path to a summary file to draw from. Defaults to today's overall summary if it exists.
- channels: target Slack channels. If not specified, inferred from the topic and stakeholder map (see below).
- mode:
--yesfor immediate send; otherwise review mode (user confirms before posting).
Stakeholder Channel Map
Default channel mapping by topic area. The user can override at any step.
| Topic area | Default channel(s) |
|---|---|
| Batch inference / batch API | #batch-api-discussion, #ml-cp-batch |
| GPU infrastructure / clusters | #ml-infra-prod, #team-ml-infra-gpu |
| WIF / identity / auth | #cloud-platform-k8s |
| Data platform / offline inference | #proj-data-platform-gpu-offline-inference |
| Davies / ML control plane | #ml-cp-eng |
| General ML infra | #ml-infra-prod |
Steps
1. Gather context
Read the context file (overall summary, GitHub activity, or user-provided description). Extract:
- What was done (accomplishments, merged PRs, created resources)
- What stakeholders need to do (actions requested from them, e.g., "create terraform to give GSA access")
- Relevant links (PR URLs, documentation links, issue links)
- Timeline or next steps
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.
- 7d ago First seen · 112 lines · 64 tokens per session scan A 5cf9c8c6f4c4
stakeholder-announcement is a skill published in the GitHub repository tomzx/agents (6 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 1,118 once invoked, about $0.0003 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 2026-09-03.
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