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Weekly Pacing

Organization
pnp
weekly-pacing

Analyzes the user's upcoming or current work week using Work IQ signals (calendar, email volume, and semantic themes) and produces a visualized pacing report — classifying each day as Rest, Light, Focus, or Heavy based on meeting load, back-to-back density, communication volume, and available deep-work time. Designed for anxious users: leads with reassurance, names rest days first, surfaces the dominant themes of the week so it feels intentional, flags which days need focus, and ends with one concrete suggestion — never a list of warnings. Use when user asks to "show my week", "how does my week look", "is my week busy", "weekly pacing", "weekly workload", "do I have rest time", "which days are heavy", "where can I breathe this week", "workiq my week", "scan my week", "weekly rhythm check", "what's the shape of my week", or expresses anxiety about an upcoming week ("I'm dreading next week", "is this week going to be brutal"). Do NOT use for: daily briefings (use daily-briefing), single-meeting prep (use meeting-intel), scheduling new meetings (use schedule-meeting), or calendar cleanup actions (use calendar-management).

Overview

Publisherpnp
Repositorycopilot-prompts
Skill nameweekly-pacing
Stars
894
Forks
307
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by pnp on GitHub. Read the source before you install it.

Installation

Install the Weekly Pacing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/pnp/copilot-prompts/tree/main/samples/skills/weekly-pacing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/pnp/copilot-prompts.git /tmp/copilot-prompts
mkdir -p .claude/skills
cp -r /tmp/copilot-prompts/samples/skills/weekly-pacing .claude/skills/weekly-pacing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Weekly Pacing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Weekly Pacing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Weekly Pacing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Weekly Pacing Report

A calm, visual read on the user's week using Work IQ signals. The goal is clarity — never overwhelm.

Tone Rules (CRITICAL)

The user has anxiety about workload. Every output must:

  • Lead with the good news — name rest days and lighter days FIRST, before heavy ones.
  • Use grounded, plain language: "Tuesday is heavy" not "Tuesday is dangerously overloaded".
  • Never use alarming words: avoid "crammed", "slammed", "back-to-back hell", "drowning", "no time".
  • Always end with one concrete, gentle suggestion — not a list of warnings.
  • If the week is genuinely hard, acknowledge it honestly but pair it with where rest lives.
  • Frame the week as a shape, not a score. Never produce a numeric stress rating.

Workflow

1. Resolve the week

  • Determine the target week from the user's phrasing ("this week", "next week", "the week of May 25").
  • Use the user's local time zone from system context.
  • Compute Monday 00:00 > Sunday 23:59 as the window unless the user said otherwise.

2. Pull Work IQ signals in parallel

Run these tool calls in parallel — they are independent:

a. Calendar — ListCalendarView for the week, include attendees and showAs.

b. Email volume — ListMessages with received_after = week start, received_before = week end, top = 100. Group by day received.

c. Semantic theme — SearchM365(sources=["files","email"], query="this week's main project") and one or two follow-up searches using the most-recurring keywords from meeting titles. Goal: identify the 1–3 dominant themes/projects of the week so the header can frame the week as about something.

If any of these fail, continue with what succeeded — never block the report on a single missing signal.

3. Compute per-day signals

For each day:

  • Meeting hours: total accepted/organized event time (exclude declined, tentative, all-day OOO).
  • Back-to-back blocks: count of consecutive meetings with <15 min gap.
  • Largest contiguous free block during 9am–5pm (the user's "breathing room").
  • First / last meeting: signals early start or late end.
  • Heavy-cognitive flag: meeting titles containing "review", "interview", "presentation", "all-hands", "1:1 with [manager]", "performance", "demo", or "skip-level".
  • Email load: count of messages received that day. Weight lightly — only meaningful as a compounding signal.

4. Classify each day

Start with the calendar-based label, then bump up one level if email load is in the top 25% of the week.

LabelCriteria
Rest≤1 meeting hour AND largest free block ≥4h
Light≤2.5 meeting hours AND largest free block ≥2.5h
Focus2.5–4.5 meeting hours OR 1 cognitively-heavy meeting with room around it
Heavy>4.5 meeting hours OR ≥3 back-to-back blocks OR 2+ cognitively-heavy meetings

Weekend days default to Rest unless events exist.

5. Render the visualized report

Invoke the render-ui skill to produce an Adaptive Card with:

  1. Theme header (TextBlock, large): one reassuring line that names what the week is about. Examples:

    • "This week is mostly about the Q3 launch — Wednesday and Friday give you space to think."
    • "A lighter week than last. The Reston review on Tuesday is the only heavy lift."
    • "Three project threads this week (launch, hiring, planning). Friday afternoon is yours."
  2. Day-by-day chart (Chart.HorizontalBar): one bar per weekday showing meeting hours. Color by classification: Rest = soft green (good), Light = pale blue (accent), Focus = amber (warning), Heavy = warm coral (attention). Avoid pure red.

  3. Day cards (one Container per day) showing:

    • Day name + date + classification label (as a Badge)
    • Meeting hours · largest free block · # of back-to-backs · email count
    • One gentle note per day: e.g. "Protected 2-4pm for focus" or "Light morning — good for deep work" or "Heavy afternoon, quiet morning"
  4. Footer recommendation (TextBlock): ONE concrete suggestion, not a list. Examples:

    • "Consider blocking 9-11am Tuesday before the day fills up."
    • "Friday afternoon is open — a good landing zone for the week."
    • "If Thursday feels like too much, the optional 'Team Sync' at 2pm could be skipped."

6. Inline summary

After the card renders, also write a 2-3 sentence text summary so the user sees the takeaway without clicking. Structure: theme of the week > rest days > one suggestion. Skip exhaustive enumeration.

Anxiety-Aware Guardrails

  • Never display a "stress score" or numeric rating of the week.
  • Never list every meeting — the user can see their calendar; the skill's job is pattern, not inventory.
  • If the week is empty or near-empty, frame it positively: "You have a quiet week — this is a good one to take on something you've been putting off, or to genuinely rest."
  • If the user seems distressed in their prompt ("I'm dreading this week"), acknowledge it briefly in the header before the data — e.g. "It's a real week, but Friday is yours."
  • Do not propose declining meetings unless the user asks. The skill informs, it doesn't act.
  • Email load is a quiet signal — never make it the headline. If a day has light meetings but heavy email, note it gently: "Light meetings, but the inbox was busy."

When NOT to Use

  • Daily summary requests > daily-briefing
  • "Prep me for [specific meeting]" > meeting-intel
  • "Find time for X" or "schedule Y" > schedule-meeting
  • "Clean up my calendar" / "decline low-priority meetings" > calendar-management
  • Multi-week analysis or trend reports > answer inline, no skill needed

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Weekly Pacing AI skill do?

Analyzes the user's upcoming or current work week using Work IQ signals (calendar, email volume, and semantic themes) and produces a visualized pacing report — classifying each day as Rest, Light, Focus, or Heavy based on meeting load, back-to-back density, communication volume, and available deep-work time. Designed for anxious users: leads with reassurance, names rest days first, surfaces the dominant themes of the week so it feels intentional, flags which days need focus, and ends with one concrete suggestion — never a list of warnings. Use when user asks to "show my week", "how does my...

Why use Weekly Pacing on TypingMind?

Because you install it once and use it with any model. Weekly Pacing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Weekly Pacing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pnp/copilot-prompts/tree/main/samples/skills/weekly-pacing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Weekly Pacing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Weekly Pacing?

As many as you like. As long as a model supports skills, you can use Weekly Pacing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Weekly Pacing AI skill free?

Yes. It is published on GitHub by pnp under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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