Agent skill

Weekly Review

by grandamenium in grandamenium/cortextos

Weekly comprehensive synthesis. An agent skill from grandamenium/cortextos.

MITAuto-check passedKnowledge Management

Install Weekly Review

skills CLI
$ npx skills add grandamenium/cortextos --skill weekly-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install grandamenium/cortextos weekly-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/grandamenium/cortextos.git skills-src && mkdir -p .claude/skills && cp -r skills-src/community/skills/weekly-review .claude/skills/weekly-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
weekly-review
GitHub stars
101
Token cost
~1.2k tokens
SKILL.md length
122 words
Files
1
Skills in repo
55
Repo updated
First seen
Licence
MIT

At a glance

Weekly comprehensive synthesis. An agent skill from grandamenium/cortextos.

  • Works in 4 steps: Data Aggregation → Present Review to User → Interactive Discussion → …
  • Tasks that involve Journaling and reflection
  • SKILL.md covers Phase 1: Data Aggregation, Phase 2: Present Review to User, Phase 3: Interactive Discussion and Phase 4: Update State, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Weekly Review is an agent skill from grandamenium/cortextos. Weekly comprehensive synthesis. Run Sunday evening or when user requests. Reviews week's accomplishments across all agents, evaluates performance, plans next week.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering Journaling and reflection. The licence is MIT.

When your agent uses it

  • Tasks that involve Journaling and reflection

Example prompts

  • “/weekly-review”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Data Aggregation
  2. Present Review to User
  3. Interactive Discussion
  4. Update State

What it can do on your machine

Read from SKILL.md and the folder at commit 6f93838. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Weekly Review loads about 1.2k tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 122 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~44
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from grandamenium/cortextos at commit 6f93838, republished under its MIT licence (© grandamenium). 122 words, ~1,159 tokens.

Download SKILL.mdSave it as .claude/skills/weekly-review/SKILL.md (or your agent's skills folder).
name
weekly-review
description
Weekly comprehensive synthesis. Run Sunday evening or when user requests. Reviews week's accomplishments across all agents, evaluates performance, plans next week.
triggers
weekly review, weekly check-in, end of week, week summary, run weekly review, weekly briefing

Weekly Review

Comprehensive weekly check-in covering all agents' output, goals progress, orchestrator self-evaluation, and next-week planning.

When: Sunday evening (configured in cron) or when user requests. Duration: ~15-30 minutes including user interaction. Output: Memory log, actionable insights, next week plan.


Phase 1: Data Aggregation

bash
# All agent heartbeats
cortextos bus read-all-heartbeats

# All tasks this week
cortextos bus list-tasks
cortextos bus list-tasks --status completed

# This week's memory files (last 7 days)
for i in 0 1 2 3 4 5 6; do
  DATE=$(date -v-${i}d +%Y-%m-%d 2>/dev/null || date -d "$i days ago" +%Y-%m-%d)
  echo "=== $DATE ==="
  cat memory/${DATE}.md 2>/dev/null || echo "(no entry)"
done

# Goals and priorities
cat GOALS.md
cat $CTX_FRAMEWORK_ROOT/orgs/$CTX_ORG/goals.json

# Inbox
cortextos bus check-inbox

Phase 2: Present Review to User

Format into a comprehensive review and send as chunked Telegram messages:

bash
cortextos bus send-telegram $CTX_TELEGRAM_CHAT_ID "<message chunk>"
Review Template
markdown
# Weekly Review - Week of [DATE]

---

## AGENT PERFORMANCE

| Agent | Status | Tasks Completed | Key Wins | Issues |
|-------|--------|----------------|----------|--------|
| [agent] | [heartbeat age] | X | [wins] | [gaps] |

Fleet Health:
- Agents online: X/N
- Agents stale (>5h): [list]
- Coordination events this week: X

---

## PRODUCTIVITY

Tasks this week (all agents combined):
- Completed: X
- In progress: Y
- Blocked: Z

Overnight work:
- Tasks dispatched: X
- Tasks completed: X

---

## GOALS PROGRESS

| Goal | Progress | Status |
|------|----------|--------|
| [north star goal] | [qualitative progress] | [on track / behind / blocked] |

---

## ORCHESTRATOR SELF-EVALUATION

| Dimension | Score (1-10) | Notes |
|-----------|-------------|-------|
| Usefulness | X | [why] |
| Proactivity | X | [why] |
| Coordination | X | [why] |
| Communication | X | [why] |
| Learning | X | [why] |
| **Total** | X/50 | |

What went well: [bullets]
What to improve: [bullets]
Key learnings: [bullets]

---

## SYSTEM IMPROVEMENT PROPOSALS

Based on this week's patterns:

[P1] [Category]: [Name]
- Problem observed: [specific pattern]
- Proposed solution: [concrete action]
- Assign to: [agent]
- Expected impact: [what changes]

[P2] ...

Agent gaps (capabilities needed):
- Missing: [capability]
- Proposed: [new skill or new agent]

---

## NEXT WEEK

Top priorities:
1. [priority]
2. [priority]
3. [priority]

Agent focus next week:
- [agent]: [priority work]

System improvements queued:
- [improvement 1]
- [improvement 2]

Phase 3: Interactive Discussion

After sending the review, ask the user:

  1. What went well this week in your view?
  2. What was challenging or frustrating?
  3. Any changes to priorities for next week?
  4. Any new agents or capabilities needed?

Phase 4: Update State

bash
# Log event
cortextos bus log-event action briefing_sent info --meta '{"type":"weekly_review"}'

# Update heartbeat
cortextos bus update-heartbeat "weekly review complete - next week planned"

# Write to memory
TODAY=$(date -u +%Y-%m-%d)
cat >> "memory/$TODAY.md" << MEMEOF

## Weekly Review - $(date -u +%H:%M:%S)

### Summary
- Total tasks completed this week: X (all agents)
- Agents active: X/N
- Self-eval total: X/50
- Top priorities next week: [list]

### Key Insights
- [insight 1]
- [insight 2]

### System Improvements Queued
- [improvement 1]
MEMEOF

# Update MEMORY.md with persistent learnings
# Add any new patterns, preferences, or system behaviors discovered this week

Custom Metrics

<!-- Added during onboarding — user-specific tracking preferences -->
<!-- Format: add bullet points below, each with the metric name and how to measure it -->
<!-- Example:
- **Platform MRR**: screenshot from your SaaS platform settings, extract MRR number
- **GitHub PRs merged this week**: gh pr list --state merged --json mergedAt | count those in last 7 days
- **Content pieces published**: count from alex agent completed tasks tagged content
-->

Manual Trigger

"Run weekly review" → read .claude/skills/weekly-review/SKILL.md and execute

This is the single source of truth for weekly review.

© grandamenium, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in community/skills/weekly-review of grandamenium/cortextos.

Open the folder on GitHubat commit 6f93838

Compare with similar skills

Weekly Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Weekly Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Weekly Review this skillgrandamenium/cortextos101—~1.2kAutomated safety check: PassMIT
LLM Wikilewislulu/llm-wiki-skill655—~3.7kAutomated safety check: PassNone
Munger Perspectivealchaincyf/munger-skill3791 repos~3.7kAutomated safety check: PassMIT
Daily Journalhuytieu/COG-second-brain1.3k—~1.3kAutomated safety check: PassMIT
Letterboxd Diaryjoe-bell/skills211—~4.2kAutomated safety check: PassMIT
Em Grid Scorermanager-dot-dev/manager-skills114—~5.8kAutomated safety check: PassMIT

Similar skills

  • LLM Wiki

    lewislulu/llm-wiki-skill

    Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers…

    655 GitHub stars~3.7k tokensUpdated 5 mo ago
    Knowledge ManagementAuto-check passed
  • Munger Perspective

    alchaincyf/munger-skill

    查理·芒格的思维框架与表达方式。基于《穷查理宝典》、伯克希尔/Daily Journal股东会、 USC/哈佛演讲、访谈记录、外部批评等50+来源的深度调研, 提炼5个核心心智模型、8条决策启发式和完整的表达DNA。

    379 GitHub starsUsed in 1 repo~3.7k tokens
    Knowledge ManagementAuto-check passed
  • Daily Journal

    huytieu/COG-second-brain

    A passive daily work journal that Claude keeps FOR you so you never have to write it yourself.

    1.3k GitHub stars~1.3k tokensUpdated 8 days ago
    Knowledge ManagementAuto-check passed
  • Letterboxd Diary

    joe-bell/skills

    Fetch recently watched films from a Letterboxd member's diary RSS feed and render them as a compact markdown list, with first-run setup for the username.

    211 GitHub stars~4.2k tokensUpdated 3 days ago
    Knowledge ManagementAuto-check passed
  • Em Grid Scorer

    manager-dot-dev/manager-skills

    Score an Engineering Manager's coverage across all 12 cells of the EM Grid based on their calendar and Slack.

    114 GitHub stars~5.8k tokensUpdated 5 mo ago
    Knowledge ManagementAuto-check passed
  • Digital Brain

    foryourhealth111-pixel/Vibe-Skills

    This skill should be used when the user asks to "write a post", "check my voice", "look up contact", "prepare for meeting", "weekly review", "track goals", or mentions personal brand, content…

    3.6k GitHub stars~1.7k tokensUpdated 1 mo ago
    Knowledge ManagementAuto-check passed

More from grandamenium/cortextos

All 55 skills in this repo
  • Cortext Self Diagnosis

    grandamenium/cortextos

    Diagnose cortextOS itself when the framework misbehaves — an agent has gone silent or wedged, agents are crash-looping, Telegram or agent-to-agent messages are not arriving, crons did not fire, an…

    101 GitHub stars~3.7k tokensUpdated 18 days ago
    Auto-check passed
  • Activity Channel

    grandamenium/cortextos

    You have completed something significant and want the whole org — all agents and the user — to know about it.

    101 GitHub stars~624 tokensUpdated 18 days ago
    Auto-check passed
  • Agentcard Purchase

    grandamenium/cortextos

    You need to make a purchase on behalf of the user — buy a SaaS subscription, pay for an API, purchase a domain, or any transaction requiring a credit card.

    101 GitHub stars~1.1k tokensUpdated 18 days ago
    Auto-check passed
  • Claude To Codex Migration

    grandamenium/cortextos

    Migrate ANY cortextOS agent from the claude-code runtime to the live codex-app-server runtime.

    101 GitHub stars~12k tokensUpdated 18 days ago
    Auto-check: warnings
  • Bus Reference

    grandamenium/cortextos

    Complete cortextos bus CLI reference - all available commands with examples.

    101 GitHub stars~3.8k tokensUpdated 18 days ago
    Auto-check passed
  • Business News Monitor

    grandamenium/cortextos

    Daily cron-driven scan of news/forums/social in a domain to surface market shifts, new competitors, regulatory changes, and net-new opportunities.

    101 GitHub stars~1.3k tokensUpdated 18 days ago
    Auto-check passed

Questions about Weekly Review

What does Weekly Review do?

Weekly comprehensive synthesis. An agent skill from grandamenium/cortextos. Weekly Review is an agent skill from grandamenium/cortextos. Weekly comprehensive synthesis.

When should I use Weekly Review?

Weekly Review fits situations like: tasks that involve Journaling and reflection.

How do I install Weekly Review in Claude Code?

Run `npx skills add grandamenium/cortextos --skill weekly-review -a claude-code`. Or copy the skill folder (community/skills/weekly-review in grandamenium/cortextos) into .claude/skills/weekly-review in your project. Claude Code loads it when a task matches its description.

How do I install Weekly Review in Codex?

Run `npx skills add grandamenium/cortextos --skill weekly-review -a codex`. Or copy the skill folder (community/skills/weekly-review in grandamenium/cortextos) into .agents/skills/weekly-review in your project. Codex loads it when a task matches its description.

Can I use Weekly Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add grandamenium/cortextos --skill weekly-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/weekly-review, .gemini/skills/weekly-review, .github/skills/weekly-review and .opencode/skills/weekly-review in your project.

What does Weekly Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Weekly Review is instructions for the agent only.

Does Weekly Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Weekly Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Weekly Review use?

Weekly Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Weekly Review use?

About 1.2k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Weekly Review?

Skills that share tags, products or a category with Weekly Review: LLM Wiki (lewislulu/llm-wiki-skill, 655 stars), Munger Perspective (alchaincyf/munger-skill, 379 stars), Daily Journal (huytieu/COG-second-brain, 1.3k stars) and Letterboxd Diary (joe-bell/skills, 211 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Weekly Review?

grandamenium (a GitHub user) maintains it in grandamenium/cortextos, which has 101 GitHub stars. The repository holds 55 skills in this directory. The repository was last updated on September 23, 2026.

Source: grandamenium/cortextos on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.