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Monthly Review

CommunityPopular
hoangsonww
monthly-review

Compile a month-over-month retrospective from Agent Monitor data — sessions, cost, token volumes, completion rate, top projects by working directory, and notable shifts versus the prior month. Uses daily_sessions/daily_events (365d) from analytics, the session list, and the pricing cost breakdown. Use when doing a monthly retrospective or planning the month ahead.

Overview

Publisherhoangsonww
RepositoryClaude-Code-Agent-Monitor
Skill namemonthly-review
Stars
1.1K
Forks
260
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Monthly Review 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/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-productivity/skills/monthly-review
  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/hoangsonww/Claude-Code-Agent-Monitor.git /tmp/Claude-Code-Agent-Monitor
mkdir -p .claude/skills
cp -r /tmp/Claude-Code-Agent-Monitor/plugins/ccam-productivity/skills/monthly-review .claude/skills/monthly-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Monthly Review 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 Monthly Review 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 Monthly Review 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.

Monthly Review

Generate a month-over-month productivity retrospective from Agent Monitor data.

Input

The user provides: $ARGUMENTS

This may be:

  • "this month" or empty (default: the current calendar month to date)
  • "last month" for the previous full calendar month
  • A specific month: "2026-02" or "February 2026"

The comparison period is always the immediately preceding calendar month.

Data Sources

EndpointReturns
GET /api/analyticsdaily_sessions and daily_events (365d) for monthly bucketing and trends; tokens (total_input/output/cache_read/cache_write — baselines pre-summed); tool_usage (top 20); sessions_by_status
GET /api/sessions?limit=500Sessions with started_at, ended_at, status, model, cwd, cost, and metadata (turn_count, thinking_blocks) — for per-project (cwd) grouping and completion rate
GET /api/pricing/costtotal_cost and per-model breakdown (input/output/cache tokens, cost, matched_rule)

Report Sections

1. Month at a Glance

Compare the target month to the prior month in a table:

MetricThis MonthLast MonthChange
SessionsNN▲/▼ N%
Total Cost$X.XXXX$X.XXXX▲/▼ N%
Tokens (in/out/cache)NN▲/▼ N%
Completion RateN%N%▲/▼ N pts
Active DaysNN▲/▼

Derive monthly buckets from daily_sessions / daily_events. Completion rate = completed sessions / total sessions for the month (from sessions_by_status and the filtered session list).

2. Top Projects (by cwd)

Group the month's sessions by cwd. For the top 5–8 projects, list session count, total cost, completion rate, and dominant model. Note any project that newly appeared or dropped off versus last month.

3. Cost & Token Breakdown

From /api/pricing/cost, show cost per model and the dominant token type. Compute cache hit rate = total_cache_read / (total_cache_read + total_input) and compare to last month. Currency to 4 decimals.

4. Tool & Workflow Shifts

From tool_usage, highlight the tools that rose or fell most month-over-month, and any new tool adopted. Flag rising error/Compaction activity if present.

5. Notable Shifts & Narrative

Three to five plain-language observations: what changed, why it likely changed, and what it implies (e.g., "cost up 22% but sessions flat → heavier per-session work").

6. Focus for Next Month

Two to four prioritized, actionable goals grounded in the numbers above.

Output

  • Markdown report with emoji-light, scannable section headers.
  • Tables for all month-over-month comparisons; ▲ / ▼ for deltas.
  • Currency in USD to 4 decimals; tokens with thousands separators.
  • Lead with a 2–3 sentence executive summary, then the sections in order.
  • Cite only numbers returned by the API; if a month has no data, say so explicitly.

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 Monthly Review AI skill do?

Compile a month-over-month retrospective from Agent Monitor data — sessions, cost, token volumes, completion rate, top projects by working directory, and notable shifts versus the prior month. Uses daily_sessions/daily_events (365d) from analytics, the session list, and the pricing cost breakdown. Use when doing a monthly retrospective or planning the month ahead.

Why use Monthly Review on TypingMind?

Because you install it once and use it with any model. Monthly Review 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 Monthly Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-productivity/skills/monthly-review. 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 Monthly Review?

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 Monthly Review?

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

Is the Monthly Review AI skill free?

Yes. It is published on GitHub by hoangsonww 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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