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
Install / Use
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill monthly-reviewInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
OperationsSupported Platforms
Our assessment of monthly-review
monthly-review scores 81/100 on our quality scale, 620th of 751 Operations skills we index.
Its SKILL.md is 3.3 KB long, well organised into 11 sections and no code examples: a solid amount of guidance for an agent.
With 1,015 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 10 days ago, so monthly-review is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
monthly-review compared with similar skills
All 4 of these similar skills score higher than monthly-review; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| monthly-review (this skill)by hoangsonww | 81 | 1.0k | 10d ago | SKILL.md |
| algorithmic-artby anthropics | 100 | 177.9k | 11d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 11d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 130.2k | 12d ago | SKILL.md |
| ui-ux-pro-maxby nextlevelbuilder | 100 | 130.2k | 12d ago | SKILL.md |
Frequently asked questions
- How do I install monthly-review?
- Run
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill monthly-review. The install tabs above show the steps for each supported agent. - Which AI agents does monthly-review work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is monthly-review safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is monthly-review still maintained?
- The repository was last updated 10 days ago, so monthly-review is actively maintained.
Skill content
View source on GitHubname: monthly-review description: > 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.
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
| Endpoint | Returns |
|----------|---------|
| GET /api/analytics | daily_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=500 | Sessions 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/cost | total_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:
| Metric | This Month | Last Month | Change | |--------|-----------|------------|--------| | Sessions | N | N | ▲/▼ N% | | Total Cost | $X.XXXX | $X.XXXX | ▲/▼ N% | | Tokens (in/out/cache) | N | N | ▲/▼ N% | | Completion Rate | N% | N% | ▲/▼ N pts | | Active Days | N | N | ▲/▼ |
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.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
