sd-weekly-review

Synthesizes one week of authorized work evidence into a personal cross-stream review.

Updated Jun 26, 2026
One-click install
npx skills add https://github.com/platypeeps/sd-ai-command-pack --skill sd-weekly-review-platypeeps
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sd-weekly-review
Source: https://github.com/platypeeps/sd-ai-command-pack/tree/main/contrib/sd-weekly-review
Command: npx skills add https://github.com/platypeeps/sd-ai-command-pack --skill sd-weekly-review-platypeeps

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Personal weekly reflection across multiple projects, notes, and communication channels is hard to do honestly: activity gets inflated into outcomes, gaps get filled with guesses, and private sources get overexposed. This Skill turns one bounded week of authorized work and knowledge evidence into a concise, evidence-backed review that keeps outcomes, activity, carryover, lessons, and next-week focus strictly separate. ## Core Features & Use Cases - Evidence-bounded synthesis: Builds a normalized activity ledger from authorized sources, deduplicates by stable origin IDs, and classifies items as outcomes, meaningful activity, decisions, carryover, or lessons without upgrading them. - Privacy and profile governance: Enforces a disclosure ceiling (private-only, internal, outward-safe), resolves worklog and personal profiles only through explicit locators, and never reproduces private paths, tags, or identities. - Calibrated reporting: Classifies source coverage as complete, partial, stale, inaccessible, or missing, and returns an explicit insufficient-evidence result instead of filler when evidence is sparse. - Use Case: On Monday morning, ask for a review of the previous week using your configured worklog profile; receive destination-neutral Markdown with outcomes, carryover, friction patterns, and at most three ranked next-week focus items, each linked to evidence. ## Quick Start Ask the AI to run a weekly review for the previous week in your timezone using your authorized worklog profile and private-only privacy scope.

Frequently Asked Questions about sd-weekly-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a personal weekly review from my work sources?▼

Invoke the skill with a week argument, an IANA timezone, and an explicit worklog profile locator or authorized source inventory. It builds a normalized activity ledger, classifies evidence into outcomes, activity, carryover, and lessons, and returns destination-neutral Markdown.

What is the difference between a weekly review and a status update?▼

A weekly review synthesizes personal reflection across multiple streams and selects next-week focus, while a status update reports objective progress for one project or stakeholder audience. Use sd-status-update for single-project reporting and sd-retro for causal analysis of one event.

Can the weekly review access my private notes and worklog?▼

Only within an explicit authorized boundary. You must supply a worklog profile locator or direct source inventory; the skill never searches private stores globally and never reproduces private paths, tags, identities, or source excerpts in the output.

What happens when there is not enough evidence for a weekly review?▼

Sparse evidence produces a short truthful result: source coverage states, the few supported facts, any carryover, and either the smallest defensible focus item or an explicit insufficient-evidence result. Missing sources are never treated as evidence of no activity.

Does the weekly review publish results or update my tasks automatically?▼

No. The skill is strictly read-only and returns capture-ready Markdown. Publication, note updates, task creation, scheduling, messaging, and profile changes are all marked not run and require separate explicit requests.

Why does the weekly review ask for a timezone before running?▼

The reporting window is defined by local calendar midnights in a resolved IANA timezone, not a fixed 168-hour duration. Without an explicit or authorized profile timezone, calendar boundaries would be ambiguous, so the skill stops and asks rather than guessing.