Claude Skill

cwd-rollup

Roll up Claude Code sessions by working directory (project) from Agent Monitor data — session count, total cost, total tokens, and last-active timestamp per cwd — so per-project activity can be compared at a glance. Use when summarizing where effort and spend went across projects

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Download hoangsonww-claude-code-agent-monitor-plugins_ccam-sessions_skills_cwd-rollup-83d4df5.zip · 1 KB

hoangsonww/claude-code-agent-monitor 1014 238 forks MIT Updated 21h ago

Part of hoangsonww/claude-code-agent-monitor — 86 skills

Install

skills CLI

npx skills add https://github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plugins/ccam-sessions/skills/cwd-rollup

Claude Code

claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install hoangsonww-claude-code-agent-monitor@llmmart

Git git clone https://github.com/hoangsonww/Claude-Code-Agent-Monitor.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole hoangsonww/claude-code-agent-monitor collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

CWD Rollup

Aggregate session activity per working directory (project).

Input

The user provides: $ARGUMENTS

Empty → roll up all working directories.
A path / project substring → restrict the rollup to matching cwds.
top N → keep only the N highest-cost (or highest-count) projects.

Data Sources

Endpoint	Returns
`GET /api/run/cwds`	the distinct working directories that have sessions — the rollup key set
`GET /api/sessions?limit=N`	session list: id, status, model, cwd, started_at, ended_at, cost, metadata (usage_extras with token counts)
`GET /api/pricing/cost`	fleet cost: total_cost, breakdown[{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] — for the fleet total to compute each cwd's share

Report Sections

1. Key set

GET /api/run/cwds for the canonical list of working directories. Apply the $ARGUMENTS filter (substring match) if one was given.

2. Pull sessions

GET /api/sessions?limit=1000. Bucket sessions by cwd.

3. Aggregate per cwd

For each working directory compute:

sessions — count.
cost — sum of the inline cost field across the bucket.
tokens — sum of input / output / cache-read / cache-write from each session's metadata usage_extras (sum the four into a total, and keep input + output as the "billable text" subtotal).
last active — the max started_at (or ended_at) in the bucket.
models — the distinct models seen.

4. Share of fleet

GET /api/pricing/cost for total_cost; show each cwd's cost as a percentage of the fleet total.

5. Ranking

Sort by cost descending by default (or count if the user asked); apply top N.

Output

Markdown table: project (cwd basename) | sessions | total tokens | cost | % of fleet | last active | models. Currency as USD to 4 decimal places; token counts with thousands separators; sort cost-descending. Add a final TOTAL row summing the columns. Only count tokens that the session metadata actually carries — if usage_extras is absent for a session, note it as excluded rather than guessing. If the dashboard is unreachable, tell the user to start it with npm start from the repo root.

Files (claude-code-agent-monitor)

agents

openai.yaml 261 B

interface:
  display_name: "Cwd Rollup"
  short_description: "Roll up Claude Code sessions by working directory (project)..."
  default_prompt: "Use $cwd-rollup to inspect CCAM data and complete this workflow safely."
policy:
  allow_implicit_invocation: false

SKILL.md 2.6 KB

---
name: cwd-rollup
description: >
  Roll up Claude Code sessions by working directory (project) from Agent Monitor
  data — session count, total cost, total tokens, and last-active timestamp per cwd
  — so per-project activity can be compared at a glance. Use when summarizing where
  effort and spend went across projects.
---

# CWD Rollup

Aggregate session activity per working directory (project).

## Input

The user provides: **$ARGUMENTS**

- Empty → roll up **all** working directories.
- A path / project substring → restrict the rollup to matching cwds.
- `top N` → keep only the N highest-cost (or highest-count) projects.

## Data Sources

| Endpoint | Returns |
|----------|---------|
| `GET /api/run/cwds` | the distinct working directories that have sessions — the rollup key set |
| `GET /api/sessions?limit=N` | session list: id, status, model, cwd, started_at, ended_at, cost, metadata (usage_extras with token counts) |
| `GET /api/pricing/cost` | fleet cost: total_cost, breakdown[{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] — for the fleet total to compute each cwd's share |

## Report Sections

### 1. Key set
`GET /api/run/cwds` for the canonical list of working directories. Apply the
`$ARGUMENTS` filter (substring match) if one was given.

### 2. Pull sessions
`GET /api/sessions?limit=1000`. Bucket sessions by `cwd`.

### 3. Aggregate per cwd
For each working directory compute:
- **sessions** — count.
- **cost** — sum of the inline `cost` field across the bucket.
- **tokens** — sum of input / output / cache-read / cache-write from each session's
  metadata `usage_extras` (sum the four into a total, and keep input + output as the
  "billable text" subtotal).
- **last active** — the max `started_at` (or `ended_at`) in the bucket.
- **models** — the distinct models seen.

### 4. Share of fleet
`GET /api/pricing/cost` for `total_cost`; show each cwd's cost as a percentage of the
fleet total.

### 5. Ranking
Sort by cost descending by default (or count if the user asked); apply `top N`.

## Output

Markdown table: `project (cwd basename) | sessions | total tokens | cost | % of fleet | last active | models`.
Currency as USD to 4 decimal places; token counts with thousands separators; sort
cost-descending. Add a final TOTAL row summing the columns. Only count tokens that
the session metadata actually carries — if `usage_extras` is absent for a session,
note it as excluded rather than guessing. If the dashboard is unreachable, tell the
user to start it with `npm start` from the repo root.