Meeting Cadence Optimizer

读取 Fulcra 中 Evening Debrief（日评分、会议数、节奏反馈）与 Morning Check-In（精力）记录，用 Python 脚本确定性计算各会议数下的平均日评分、最佳会议数与 Pearson 相关系数，并按数据量给出不足/低/中/高置信度；输出精简可执行的建议，可选写回 Cadence Analysis 注解。

keng009

热度 16.59

最近更新 2026-06-26 11:49:07

分类：数据依赖：代码执行 / 第三方 API应用场景：报告撰写 / 日程安排

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add meeting-cadence-optimizer

Metadata

name: meeting-cadence-optimizer
description: Analyze how the user's meeting load correlates with how their days actually feel -- using their own evening-debrief and morning check-in history -- and recommend an optimal meeting cadence. Use this whenever the user says things like "am I overbooked", "too many meetings", "optimize my schedule", "meeting cadence", "meeting burnout", "what's my meeting sweet spot", or wonders whether their schedule is wearing them down. Also good as a weekly (Sunday/Monday) review. Reads back Fulcra annotations and computes the numbers deterministically. Needs at least ~a week of evening debriefs to say anything useful. Do NOT use it to read raw calendar data alone or for objective health pulls.

Meeting Cadence Optimizer

Find the meeting load where the user's days actually go well -- grounded in their own logged data, not vibes. This skill reads back their Evening Debrief records (day rating, meeting count, cadence feedback) and Morning Check-In records (energy), then computes the relationship between meeting density and day quality. Let the data do the convincing.

Prerequisites

Fulcra CLI authenticated (uv tool run fulcra-api auth login).
Run with uv run --python 3.12.
Meaningful output needs history: this works once there are several evening-debrief entries with meeting_count and day_rating. The skill reports a confidence level so you know how much to trust it.

Analyze (read-only)

uv run --python 3.12 ~/.claude/skills/meeting-cadence-optimizer/scripts/cadence.py analyze --days 30

Returns, computed from the user's own records:

data_points and a confidence gate: insufficient (<7 days), low (7-13), medium (14-29), high (30+).
avg_meeting_count, avg_day_rating
day_rating_by_meeting_count -- the average day rating at each meeting count (the core table)
best_rated_meeting_count -- the count with the highest average rating (the "sweet spot")
meeting_count_vs_rating_correlation -- Pearson r (negative = more meetings, worse days)
too_many_feedback_count -- how often they flagged "too many"

How to present it

Match your confidence to the data, and only state numbers the script returned -- never invent a correlation:

insufficient: "I need about a week of evening debriefs before I can find your pattern -- keep wrapping up your days and I'll have something soon." Don't push numbers.
low: "Early signal, low confidence -- here's what I'm seeing so far..."
medium / high: Lead with the sweet spot and the table, e.g. "Your days average 7.4 with 3-4 meetings but drop to 5.1 at 6+. Your sweet spot looks like 4. You've flagged 'too many' 5 times this month." Then offer one or two concrete moves: cap daily meetings, add buffers after long ones, protect a focus block.

Keep recommendations actionable and few. The goal is a decision, not a dashboard.

Save the analysis (optional)

To record the analysis as a Fulcra annotation (useful for a weekly cadence so trends are themselves trackable):

uv run --python 3.12 .../cadence.py save --days 30 --dry-run   # preview
uv run --python 3.12 .../cadence.py save --days 30             # write

Writes a "Cadence Analysis" moment annotation and reports verified_matches. If there's no data yet, save skips the write rather than recording an empty analysis.

Weekly use

This is a natural Sunday-evening or Monday-morning review. If the user wants it automated, it can be scheduled to run weekly and surface the result in their next morning briefing.

Privacy

Meeting patterns and day ratings are personal. Don't surface them publicly. Never print API tokens.

Where this fits

The analytical capstone of the Fulcra concierge reflection loop (shared lib in ~/.fulcra-concierge/lib). It consumes what evening-debrief (meeting count + day rating) and subjective-checkin (energy) record -- so its quality grows directly with how consistently those two are used.