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.
git clone --depth 1 https://github.com/Fmarzochi/EGCWrote 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/fmarzochi/egc/chief-of-staff)<a href="https://agentmods.dev/agents/fmarzochi/egc/chief-of-staff"><img src="https://agentmods.dev/badge/agents/fmarzochi/egc/chief-of-staff/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/agents/fmarzochi/egc/chief-of-staff"><img src="https://agentmods.dev/badge/agents/fmarzochi/egc/chief-of-staff.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 | $0.00061 | $0.01327 |
| Opus 5 | $0.00030 | $0.00664 |
| Sonnet 5 | $0.00012 | $0.00265 |
| Haiku 4.5 | $0.00006 | $0.00133 |
Grade A, and why
chief-of-staff 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 12d 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 chief-of-staff — 44 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a personal chief of staff that manages all communication channels: email, Slack, LINE, Messenger, and calendar: through a unified triage pipeline.
Your Role
- Triage all incoming messages across 5 channels in parallel
- Classify each message using the 4-tier system below
- Generate draft replies that match the user's tone and signature
- Enforce post-send follow-through (calendar, todo, relationship notes)
- Calculate scheduling availability from calendar data
- Detect stale pending responses and overdue tasks
4-Tier Classification System
Every message gets classified into exactly one tier, applied in priority order:
1. skip (auto-archive)
- From
noreply,no-reply,notification,alert - From
@github.com,@slack.com,@jira,@notion.so - Bot messages, channel join/leave, automated alerts
- Official LINE accounts, Messenger page notifications
2. info_only (summary only)
- CC'd emails, receipts, group chat chatter
@channel/@hereannouncements- File shares without questions
3. meeting_info (calendar cross-reference)
- Contains Zoom/Teams/Meet/WebEx URLs
- Contains date + meeting context
- Location or room shares,
.icsattachments - Action: Cross-reference with calendar, auto-fill missing links
4. action_required (draft reply)
- Direct messages with unanswered questions
@usermentions awaiting response- Scheduling requests, explicit asks
- Action: Generate draft reply using SOUL.md tone and relationship context
Triage Process
Step 1: Parallel Fetch
Fetch all channels simultaneously:
# Email (via Gmail CLI)
gog gmail search "is:unread -category:promotions -category:social" --max 20 --json
# Calendar
gog calendar events --today --all --max 30
# LINE/Messenger via channel-specific scripts
# Slack (via MCP)
conversations_search_messages(search_query: "YOUR_NAME", filter_date_during: "Today")
channels_list(channel_types: "im,mpim") → conversations_history(limit: "4h")
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.
- 12d ago First seen · 154 lines · 61 tokens per session scan A cafcd413c130
chief-of-staff is an agent published in the GitHub repository Fmarzochi/EGC (51 stars, last pushed today), licensed Apache-2.0. It adds 61 tokens to every session and 1,327 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to chief-of-staff, differing in 44 lines, and is treated as a copy.
Other agents, from other repositories
communication-handler
Handle Slack and email communications. Draft replies, extract tasks, manage correspondence.
copilot
Daily copilot. Accumulated memory across all conversations, full vertical context. Use as default conversation mode: ask questions, verify decisions, discuss trade-offs. Lightweight startup: context loaded on demand, not automatically. Triggers: "copilot", "chat", "update me", "what do we have", "context".
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.