The whole hunk
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lines
from line 30
3030
3131 - `"1m"`
3232
33- `claude_tag_categories: optional array of "dm" or "engaged" or "monitoring" or 2 more`
34
35 Filter to Claude Tag (Claude in Slack) usage in specific spend categories. Usage with no category never matches. `dm` usage is reported under the user's product rather than `claude-tag`, so combining this filter with `products[]=claude-tag` excludes it. Use `group_by[]=claude_tag_category` to break out per-category values.
36
37 maxItems: 100
38
39 - `"dm"`
40
41 - `"engaged"`
42
43 - `"monitoring"`
44
45 - `"proactive"`
46
47 - `"scheduled"`
48
49- `claude_tag_user_ids: optional array of string`
50
51 Filter to Claude Tag (Claude in Slack) usage attributed to specific Slack users, by Slack user ID (for example `U0123ABCDEF`), not claude.ai user ID. Usage that is not Claude Tag, and Claude Tag usage not attributed to a single user, never matches. Use `group_by[]=claude_tag_user_id` to break out per-user values.
52
53 maxItems: 100
54
3355- `context_windows: optional array of "0-200k" or "200k-1M"`
3456
3557 Filter to specific context-window pricing tiers. Use `group_by[]=context_window` to break out per-tier values.
from line 74
5274
5375 default: false
5476
55- `group_by: optional array of "context_window" or "inference_geo" or "model" or 4 more`
77- `group_by: optional array of "claude_tag_category" or "claude_tag_user_id" or "context_window" or 6 more`
5678
5779 Break each actor's row out by the given dimensions. Accepts the same values as the bucketed `/usage_report` endpoint. `limit` bounds (actor × time bucket × dimension) rows — with dimensions or `bucket_width` present, one actor may span several rows.
5880
5981 maxItems: 100
6082
83 - `"claude_tag_category"`
84
85 - `"claude_tag_user_id"`
86
6187 - `"context_window"`
6288
6389 - `"inference_geo"`
from line 246
220246
221247 The number of input tokens read from the cache.
222248
249 - `claude_tag_category: "dm" or "engaged" or "monitoring" or 2 more or null`
250
251 Claude Tag (Claude in Slack) spend category: `engaged` (a person addressed Claude in a channel or thread), `proactive` (Claude responded without being addressed), `scheduled` (a scheduled routine ran), `monitoring` (Claude watching a channel it was asked to monitor), or `dm` (direct messages with Claude). Populated only when `claude_tag_category` is in `group_by[]`; null for usage that is not Claude Tag. Direct-message usage is billed to the individual user and is reported under that user's product, not under `claude-tag`. New categories may be added over time.
252
253 - `"dm"`
254
255 - `"engaged"`
256
257 - `"monitoring"`
258
259 - `"proactive"`
260
261 - `"scheduled"`
262
263 - `claude_tag_user_id: string or null`
264
265 Slack user ID (for example `U0123ABCDEF`) of the member the Claude Tag (Claude in Slack) usage is attributed to, not a claude.ai user ID. Populated only when `claude_tag_user_id` is in `group_by[]`; null for usage that is not Claude Tag and for Claude Tag usage that is not attributed to a single user (for example `monitoring`, and `proactive` usage Claude initiated), so per-user rows can sum to less than the Claude Tag total. Cannot be combined with `group_by[]=rbac_group_id` or the `rbac_group_ids[]` filter.
266
223267 - `context_window: "0-200k" or "200k-1M" or null`
224268
225269 Context-window pricing tier of the usage or cost. Null unless `context_window` is in `group_by[]`; it can also be null on grouped rows with no context-window tier, such as code execution.
from line 384
340384 "ephemeral_5m_input_tokens": 500
341385 },
342386 "cache_read_input_tokens": 3200000,
387 "claude_tag_category": "dm",
388 "claude_tag_user_id": "U0123ABCDEF",
343389 "context_window": "0-200k",
344390 "ending_at": "2019-12-27T18:11:19.117Z",
345391 "inference_geo": "global",