The whole hunk
from line 29, old and new numbered
/
lines
from line 29
2929
3030 - `"1m"`
3131
32- `claude_tag_categories: optional array of "dm" or "engaged" or "monitoring" or 2 more`
33
34 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.
35
36 maxItems: 100
37
38 - `"dm"`
39
40 - `"engaged"`
41
42 - `"monitoring"`
43
44 - `"proactive"`
45
46 - `"scheduled"`
47
48- `claude_tag_user_ids: optional array of string`
49
50 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.
51
52 maxItems: 100
53
3254- `context_windows: optional array of "0-200k" or "200k-1M"`
3355
3456 Filter to specific context-window pricing tiers. Use `group_by[]=context_window` to break out per-tier values.
from line 67
4567
4668 format: date-time
4769
48- `group_by: optional array of "context_window" or "cost_type" or "inference_geo" or 6 more`
70- `group_by: optional array of "claude_tag_category" or "claude_tag_user_id" or "context_window" or 8 more`
4971
5072 Dimensions to break each time bucket out by. Defaults to no grouping (one total per bucket). Each bucket reports at most its top 100 groups; a group beyond that cap has no row in that bucket (there is no remainder row), so grouped buckets are not exhaustive when a dimension has more than 100 distinct values.
5173
5274 maxItems: 100
5375
76 - `"claude_tag_category"`
77
78 - `"claude_tag_user_id"`
79
5480 - `"context_window"`
5581
5682 - `"cost_type"`
from line 193
167193
168194 Amount (post-discount, pre-credit) in fractional cents.
169195
196 - `claude_tag_category: "dm" or "engaged" or "monitoring" or 2 more or null`
197
198 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.
199
200 - `"dm"`
201
202 - `"engaged"`
203
204 - `"monitoring"`
205
206 - `"proactive"`
207
208 - `"scheduled"`
209
210 - `claude_tag_user_id: string or null`
211
212 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.
213
170214 - `context_window: "0-200k" or "200k-1M" or null`
171215
172216 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 331
287331 "results": [
288332 {
289333 "amount": "amount",
334 "claude_tag_category": "dm",
335 "claude_tag_user_id": "U0123ABCDEF",
290336 "context_window": "0-200k",
291337 "cost_type": "code_execution",
292338 "currency": "USD",