* feat(PBI-67/ST-1297): datamodel-velden voor job-model-selectie
Voegt 8 nieuwe optionele velden toe verspreid over Product, Task en
ClaudeJob ten dienste van de override-cascade:
task.requires_opus → job.requested_* → product.preferred_* → kind-default
Bestaande rijen krijgen NULL (Product/ClaudeJob) of false (Task) en
vallen daarmee terug op de kind-defaults uit de resolver (ST-1298).
Migration is additief: alleen ALTER TABLE ADD COLUMN, geen RENAME of
DROP. Bestaande factories en seed-script blijven werken zonder
aanpassing omdat alle nieuwe velden default-waardes hebben.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(PBI-67/ST-1299): job-config snapshot bij enqueue + worker-flag-runbook
T-789: Snapshot van resolved JobConfig in ClaudeJob.requested_*
bij elke job-creatie. Helper in lib/job-config-snapshot.ts laadt
product (preferred_*) en task (requires_opus) en draait de resolver
uit lib/job-config.ts (mirror van scrum4me-mcp/src/lib/job-config.ts —
zelfde matrix, sync-comment in bestand). Toegepast op alle 5
enqueue-locaties:
- actions/user-questions.ts (PLAN_CHAT)
- actions/sprint-runs.ts × 3 (SPRINT_IMPLEMENTATION x2,
TASK_IMPLEMENTATION loop)
- actions/ideas.ts (IDEA_GRILL / IDEA_MAKE_PLAN)
Test-mocks uitgebreid met product.findUnique en task.findUnique zodat
de helper bij unit tests veilig terugvalt op kind-defaults (alle 563
tests groen).
T-790: Sectie 'Config doorgeven aan Claude Code' toegevoegd aan
docs/runbooks/worker-idempotency.md met CLI-flag-mapping en de
verwachte aanroep per kind. Forward-link naar
docs/runbooks/job-model-selection.md (volgt in T-794).
Plus: docs/plans/job-model-selection.md (de approved plan-doc).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(PBI-67/ST-1300): cost-attribution voor thinking-tokens + admin UI
T-792: token-stats + token-history rekenen actual_thinking_tokens nu
mee in de totale kosten (tegen input-rate, conform Anthropic billing).
COALESCE-veilig zodat oude rijen 0 bijdragen i.p.v. NaN. Nieuwe export
`getTokenStatsByKind` aggregeert tokens en kosten per ClaudeJob.kind
zodat we relatieve uitgaven van IDEA_GRILL/IDEA_MAKE_PLAN/PLAN_CHAT/
TASK_IMPLEMENTATION/SPRINT_IMPLEMENTATION kunnen zien.
T-793: admin/jobs Kosten-tabel toont:
- Nieuwe kolom 'Thinking' (aantal verbruikte thinking-tokens)
- Mismatch-marker (rood) als requested_model afwijkt van actuele
model_id — duidt op een worker die de CLI-flag niet doorgaf.
Tooltip toont aangevraagd model. Geen Sentry/log-noise.
Page-level cost-berekening volgt dezelfde formule (input_price ×
thinking_tokens). 563 tests groen.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs(PBI-67/ST-1301): runbook + CLAUDE.md updates voor model/mode-selectie
T-794: Nieuwe runbook docs/runbooks/job-model-selection.md met
override-cascade, kind-default-matrix, override-voorbeelden,
auditspoor en cost-attribution-formule. 107 regels.
T-795: CLAUDE.md hardstop-bullet voor 'Model/mode per ClaudeJob'
(verwijst naar nieuwe runbook) + patterns-quickref-rij voor
job-config resolver. CLAUDE.md blijft 139 regels (≤ 150).
T-796: docs:check-links groen — 108 files, geen broken links. Twee
externe-repo verwijzingen (scrum4me-mcp/...) ge-de-linked tot plain
text omdat de check-links script de zustertree niet traverseert; de
referenties blijven leesbaar.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
185 lines
6.4 KiB
TypeScript
185 lines
6.4 KiB
TypeScript
import { prisma } from '@/lib/prisma'
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export interface SprintTokenRow {
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sprintId: string
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sprintCode: string
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sprintGoal: string
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totalTokens: number
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totalCostUsd: number
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jobCount: number
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}
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export interface DayTokenRow {
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day: string
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totalTokens: number
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totalCostUsd: number
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}
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export interface PbiTokenRow {
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pbiId: string
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pbiCode: string
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pbiTitle: string
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totalTokens: number
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totalCostUsd: number
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}
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type RawSprintRow = {
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sprint_id: string
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sprint_code: string
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sprint_goal: string
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total_tokens: bigint
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total_cost: number | null
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job_count: bigint
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}
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type RawDayRow = {
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day: Date
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total_tokens: bigint
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total_cost: number | null
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}
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type RawPbiRow = {
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pbi_id: string
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pbi_code: string
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pbi_title: string
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total_tokens: bigint
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total_cost: number | null
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}
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export async function getSprintTokenHistory(
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userId: string,
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productId?: string,
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limit = 8,
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): Promise<SprintTokenRow[]> {
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const rows = productId
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? await prisma.$queryRaw<RawSprintRow[]>`
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SELECT
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sp.id AS sprint_id,
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sp.code AS sprint_code,
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sp.sprint_goal,
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COALESCE(SUM(cj.input_tokens + cj.output_tokens + cj.cache_read_tokens + cj.cache_write_tokens + COALESCE(cj.actual_thinking_tokens, 0)), 0) AS total_tokens,
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SUM(
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cj.input_tokens * mp.input_price_per_1m / 1000000.0
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+ cj.output_tokens * mp.output_price_per_1m / 1000000.0
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+ cj.cache_read_tokens * mp.cache_read_price_per_1m / 1000000.0
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+ cj.cache_write_tokens * mp.cache_write_price_per_1m / 1000000.0
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+ COALESCE(cj.actual_thinking_tokens, 0) * mp.input_price_per_1m / 1000000.0
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) FILTER (WHERE cj.input_tokens IS NOT NULL) AS total_cost,
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COUNT(*) FILTER (WHERE cj.input_tokens IS NOT NULL) AS job_count
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FROM claude_jobs cj
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JOIN tasks t ON cj.task_id = t.id
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JOIN stories s ON t.story_id = s.id
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JOIN sprints sp ON s.sprint_id = sp.id
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LEFT JOIN model_prices mp ON mp.model_id = cj.model_id
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WHERE cj.user_id = ${userId}
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AND cj.status = 'DONE'
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AND cj.product_id = ${productId}
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GROUP BY sp.id, sp.code, sp.sprint_goal
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ORDER BY sp.created_at DESC
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LIMIT ${limit}
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`
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: await prisma.$queryRaw<RawSprintRow[]>`
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SELECT
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sp.id AS sprint_id,
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sp.code AS sprint_code,
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sp.sprint_goal,
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COALESCE(SUM(cj.input_tokens + cj.output_tokens + cj.cache_read_tokens + cj.cache_write_tokens + COALESCE(cj.actual_thinking_tokens, 0)), 0) AS total_tokens,
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SUM(
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cj.input_tokens * mp.input_price_per_1m / 1000000.0
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+ cj.output_tokens * mp.output_price_per_1m / 1000000.0
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+ cj.cache_read_tokens * mp.cache_read_price_per_1m / 1000000.0
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+ cj.cache_write_tokens * mp.cache_write_price_per_1m / 1000000.0
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+ COALESCE(cj.actual_thinking_tokens, 0) * mp.input_price_per_1m / 1000000.0
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) FILTER (WHERE cj.input_tokens IS NOT NULL) AS total_cost,
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COUNT(*) FILTER (WHERE cj.input_tokens IS NOT NULL) AS job_count
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FROM claude_jobs cj
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JOIN tasks t ON cj.task_id = t.id
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JOIN stories s ON t.story_id = s.id
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JOIN sprints sp ON s.sprint_id = sp.id
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LEFT JOIN model_prices mp ON mp.model_id = cj.model_id
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WHERE cj.user_id = ${userId}
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AND cj.status = 'DONE'
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GROUP BY sp.id, sp.code, sp.sprint_goal
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ORDER BY sp.created_at DESC
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LIMIT ${limit}
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`
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return rows.map(r => ({
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sprintId: r.sprint_id,
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sprintCode: r.sprint_code,
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sprintGoal: r.sprint_goal,
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totalTokens: Number(r.total_tokens),
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totalCostUsd: Number(r.total_cost ?? 0),
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jobCount: Number(r.job_count),
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}))
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}
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export async function getDayTokenData(userId: string, sprintId: string): Promise<DayTokenRow[]> {
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if (!sprintId) return []
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const rows = await prisma.$queryRaw<RawDayRow[]>`
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SELECT
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DATE(cj.finished_at) AS day,
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COALESCE(SUM(cj.input_tokens + cj.output_tokens + cj.cache_read_tokens + cj.cache_write_tokens + COALESCE(cj.actual_thinking_tokens, 0)), 0) AS total_tokens,
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SUM(
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cj.input_tokens * mp.input_price_per_1m / 1000000.0
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+ cj.output_tokens * mp.output_price_per_1m / 1000000.0
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+ cj.cache_read_tokens * mp.cache_read_price_per_1m / 1000000.0
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+ cj.cache_write_tokens * mp.cache_write_price_per_1m / 1000000.0
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+ COALESCE(cj.actual_thinking_tokens, 0) * mp.input_price_per_1m / 1000000.0
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) FILTER (WHERE cj.input_tokens IS NOT NULL) AS total_cost
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FROM claude_jobs cj
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JOIN tasks t ON cj.task_id = t.id
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JOIN stories s ON t.story_id = s.id
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LEFT JOIN model_prices mp ON mp.model_id = cj.model_id
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WHERE cj.user_id = ${userId}
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AND s.sprint_id = ${sprintId}
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AND cj.status = 'DONE'
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AND cj.finished_at IS NOT NULL
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GROUP BY DATE(cj.finished_at)
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ORDER BY day ASC
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`
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return rows.map(r => ({
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day: r.day.toISOString().slice(0, 10),
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totalTokens: Number(r.total_tokens),
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totalCostUsd: Number(r.total_cost ?? 0),
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}))
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}
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export async function getPbiTokenAggregates(userId: string, sprintId: string): Promise<PbiTokenRow[]> {
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if (!sprintId) return []
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const rows = await prisma.$queryRaw<RawPbiRow[]>`
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SELECT
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p.id AS pbi_id,
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p.code AS pbi_code,
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p.title AS pbi_title,
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COALESCE(SUM(cj.input_tokens + cj.output_tokens + cj.cache_read_tokens + cj.cache_write_tokens + COALESCE(cj.actual_thinking_tokens, 0)), 0) AS total_tokens,
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SUM(
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cj.input_tokens * mp.input_price_per_1m / 1000000.0
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+ cj.output_tokens * mp.output_price_per_1m / 1000000.0
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+ cj.cache_read_tokens * mp.cache_read_price_per_1m / 1000000.0
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+ cj.cache_write_tokens * mp.cache_write_price_per_1m / 1000000.0
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+ COALESCE(cj.actual_thinking_tokens, 0) * mp.input_price_per_1m / 1000000.0
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) FILTER (WHERE cj.input_tokens IS NOT NULL) AS total_cost
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FROM claude_jobs cj
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JOIN tasks t ON cj.task_id = t.id
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JOIN stories s ON t.story_id = s.id
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JOIN pbis p ON s.pbi_id = p.id
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LEFT JOIN model_prices mp ON mp.model_id = cj.model_id
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WHERE cj.user_id = ${userId}
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AND s.sprint_id = ${sprintId}
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AND cj.status = 'DONE'
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GROUP BY p.id, p.code, p.title
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ORDER BY total_cost DESC
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`
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return rows.map(r => ({
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pbiId: r.pbi_id,
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pbiCode: r.pbi_code,
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pbiTitle: r.pbi_title,
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totalTokens: Number(r.total_tokens),
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totalCostUsd: Number(r.total_cost ?? 0),
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}))
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}
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