weekly-review

weekly-review is a skill for Claude Code, Codex from fredoliveira/agents. It costs 36 tokens per session (896 once invoked), scanned A, original, MIT.

A weekly note tool for turning daily notes from a calendar week into a short review. It extracts findings, links, action items, and ideas for focused notes, then writes them into the matching weekly note.

In plain words
What is it for?
Use it to review the last completed week or a chosen week, collect existing daily notes, and update that week's Review section.
Why use it?
It removes the need to reread and manually combine seven days of notes into a weekly summary.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to review the last completed week or a chosen week…

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fredoliveira/agents/weekly-review
Install

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.

Any agent
npx skills add fredoliveira/agents --skill weekly-review
Clone the repo
git clone --depth 1 https://github.com/fredoliveira/agents

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for weekly-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/fredoliveira/agents/weekly-review.svg)](https://agentmods.dev/skills/fredoliveira/agents/weekly-review)
Your own site
<a href="https://agentmods.dev/skills/fredoliveira/agents/weekly-review"><img src="https://agentmods.dev/badge/skills/fredoliveira/agents/weekly-review.svg" alt="Measured on agentmods" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 896 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00036 $0.00896
Opus 5 $0.00018 $0.00448
Sonnet 5 $0.00007 $0.00179
Haiku 4.5 $0.00004 $0.00090

Measured 7d ago against content hash 34d289a5c4b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

weekly-review 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.

skills/weekly-review/SKILL.md · 82 lines

How it starts

The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Weekly Review

Purpose

Process a calendar week's daily notes to generate a summary that captures key insights, tracks action items, and identifies ideas worth developing into atomic notes. The summary is written into the ## Review section of that week's weekly note.

Instructions

1. Determine the target week

  • Default to the last completed week (Monday–Sunday). If the user names a specific week or date, use that instead.
  • Compute the weekly note name in gggg-[W]ww format. Use the shell to stay accurate:
    • Last completed week: date -v-7d -v-mon +%G-W%V (e.g. 2026-W24)
    • Current week: date +%G-W%V
  • The weekly note lives at Weekly/<gggg-[W]ww>.md (e.g. Weekly/2026-W24.md).
  • Compute the Monday–Sunday date range for that week (e.g. date -v-7d -v-mon +%Y-%m-%d for the Monday, then add days).

2. Locate and read the week's daily notes

  • Find daily notes in Daily/YYYY/MM-Month/YYYY-MM-DD.md format for each of the 7 days.
  • Read each note that exists, in chronological order. Skip days with no note.

3. Extract and summarize

For each daily note, identify:

Category What to look for
Key findings Insights, learnings, realizations, decisions made
Relevant links URLs, references, resources worth revisiting
Action items Tasks mentioned, TODOs, follow-ups needed

4. Identify emergent topics

Look for recurring themes or ideas that appeared multiple times across the week. These are candidates for atomic notes.

Signs of an emergent topic:

  • Mentioned in more than one of the daily notes
  • An insight that surprised or shifted thinking
  • A connection between previously unrelated ideas
  • Something worth remembering long-term

5. Write the review into the weekly note

The weekly note's ## Review section already contains three empty subsections (created from the template):

Read the full file on GitHub · 82 lines

Changes

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.

  1. 7d ago First seen · 82 lines · 36 tokens per session scan A 34d289a5c4b9

Subscribe to this mod's changes

weekly-review is a skill published in the GitHub repository fredoliveira/agents (24 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 896 once invoked, about $0.0002 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 2026-08-30.

Related

Other skills, from other repositories

audit-para

Read-only PARA classification audit for the Obsidian vault, followed by an optional explicit-fix flow. Use when the user asks to audit PARA, check whether notes are in the right place, find projects missing outcomes/deadlines, or runs /audit-para.

kriscard/Skills · 58 tokens

spot-drift

Drift check for Obsidian: compare stated intentions against 30 days of vault behavior and surface verified priority gaps or avoidance questions. Use when the user asks "what am I avoiding?", "am I drifting?", "check my priorities vs behavior", "what's getting neglected?", or runs /spot-drift.

kriscard/Skills · 67 tokens

shell-env

Stow-first shell environment editing for zsh, aliases, tmux/sesh, Starship, Ghostty, yabai, Git, and dotfiles packages. Use when the user wants to add or change terminal config, aliases, shell startup, or Stow-managed symlinks. Route Neovim-specific work to neovim and whole-system health checks to audit.

kriscard/Skills · 79 tokens

maintain

Runs a read-only health check on the Obsidian vault — finds broken links, orphaned notes, tag inconsistencies, and wiki issues, then reports prioritized fixes. Make sure to use this skill whenever the user says "vault health check", "fix broken links", "clean up my vault", "vault maintenance", or runs /maintain. Never…

kriscard/Skills · 88 tokens

weekly

Review one week of Obsidian evidence and prepare the next weekly note.

kriscard/Skills · 17 tokens

til

TIL capture for explicit requests to save learned material from a session into an engaging Obsidian note. Use when the user runs /til or asks to save, document, or write up what they learned.

kriscard/Skills · 44 tokens