PT-BR Translation Provenance: 10 Sessions, 3 Deliverables, 1,847 Lines
How do three Portuguese translations of dance market research come into existence? Not in a single sitting. Over three days, ten Claude Code sessions wove together web scraping research, report audits, translation planning, template-wide CSS improvements, accessibility retrofits, and readability checks – then distilled it all into 1,847 lines of PT-BR HTML that faithfully mirror their English sources while speaking naturally to a Brazilian audience.
Quality Scorecard
Seven metrics. Three from rule-based telemetry analysis across all 10 contributing sessions, four from LLM-as-Judge evaluation of the 3 deliverable documents.
The Headline
RELEVANCE ████████████████████ 0.98 healthy
FAITHFULNESS ███████████████████░ 0.93 healthy
COHERENCE ██████████████████░░ 0.92 healthy
HALLUCINATION ██████████████████░░ 0.12 critical (lower is better)
TOOL ACCURACY ████████████████████ 1.00 healthy
EVAL LATENCY ████████████████████ 0.002s healthy
TASK COMPLETION ████████████████████ 1.00 healthy
Dashboard status: critical – hallucination score 0.12 exceeds the 0.10 threshold. The translations inject colloquial Brazilian embellishments (“Energia demais!”, “So gente top!”, “Gratidao!”) not present in the English sources. While culturally appropriate, these additions are not grounded in the source material. All other metrics are healthy.
Session Timeline
Feb 12 21:34 ━━━ S1: research (100 spans, 36m) ━━━ 22:09
^ English source creation: E&N profile, Austin dance, Zouk market
Feb 13 03:34 ━━ S2: review (28 spans, 8m) ━━ 03:42
^ Audit all edgar_nadyne & zouk reports
03:42 ━━━ S3: research (85 spans, 38m) ━━━ 04:20
^ Translation planning: explore OTEL data, skill patterns
04:20 ━━━━━━━━━━━━━ S4: implementation (272 spans, 237m) ━━━━━━━━━━━━━ 08:17
^ Template improvements: dark mode, responsive, semantic HTML
07:55 ━━━━ S5: commit (49 spans, 53m) ━━━━ 08:48
^ Readability checks + commit PT-BR translations [a55533fa]
09:23 ━━ S6: research (38 spans, 15m) ━━ 09:38
^ Competitor analysis research
22:46 ━ S7: research (31 spans, 7m) ━ 22:54
22:51 ━━━━━━━━━━━━━━━━━━━ S9: review (283 spans, 971m) ━━━━━━━━━━━━━━━━━━━
23:20 ━━━━━━━━━━━━━━━━━━ S8: orchestrator (192 spans, 942m) ━━━━━━━━━━━━━━━━
Feb 14 S8 ends 15:02
S9 ends 15:02
17:34 ━━━ S10: review (291 spans, 28m) ━━━ 18:02
^ Skip-to-content links + final review + naming convention docs [ab07dc7c]
Per-Output Breakdown
| Document | Lines | Relevance | Faithfulness | Coherence | Hallucination |
|---|---|---|---|---|---|
edghar_nadyne_perfil_artista.html |
599 | 0.98 | 0.94 | 0.92 | 0.12 |
analise_mercado_austin.html |
567 | 0.98 | 0.92 | 0.91 | 0.15 |
analise_mercado_zouk.html |
681 | 0.97 | 0.93 | 0.93 | 0.10 |
| Session Average | 1,847 | 0.977 | 0.93 | 0.92 | 0.123 |
What the Judge Found
The three PT-BR translations are high-quality deliverables with near-perfect structural fidelity. Every quantitative data point verified across all three files – follower counts, market figures, demographic percentages, pricing data, event dates, and source URLs – matches the English source exactly, with zero numerical errors detected across approximately 200+ discrete data points.
Strongest area: Relevance (0.977). All sections from every English source appear in the corresponding translation with no omissions. HTML structure is consistent: all files correctly set lang="pt-BR", preserve data-brand="edgar-nadyne", include the <!-- Source: ... | Lang: pt-BR --> comment, and link the same CSS files.
Weakest area: Hallucination (0.123). A consistent pattern of injecting colloquial Brazilian embellishments drives this score above the 0.10 threshold:
- Artist profile: “Energia demais!”, “So gente top!”, “Maravilhoso!”, “Incrivel!”, “Gratidao!” – none appear in the English source
- Austin market: The Carnaval Brasileiro info box adds two full sentences of enthusiastic commentary (“E gratidao demais saber que essa ponte cultural ja existe”) with no English counterpart. This is the most significant embellishment across all three files.
- Zouk market: Similar pattern but less pronounced; descriptions like “uma trajetoria incrivel” and “energia maravilhosa” added
Other findings:
- Skip-link text (“Skip to main content”) remains in English across all three PT-BR files
- Cross-file inconsistency: the artist profile uses “Danca dos Famosos” while the zouk market analysis uses “Dancing with the Stars Brasil” for the same show (both are valid names, but consistency is preferred)
- “Dancing with the Stars Brasil” correctly localized to “Danca dos Famosos” in the artist profile (this is the actual Brazilian show name)
- CAGR correctly expanded to Portuguese: “Taxa Composta de Crescimento Anual”
- All dollar amounts preserved in USD format – appropriate for market analysis context
Session Telemetry
Aggregate
| Metric | Value |
|---|---|
| Contributing Sessions | 10 |
| Date Range | 2026-02-12 to 2026-02-14 |
| Primary Model | claude-opus-4-6 (344 calls) |
| Total Spans | 1,369 |
| Tool Calls | 928 (success: 928, failed: 0) |
| Input Tokens | 1,964,982 |
| Output Tokens | 2,064,525 |
| Cache Read Tokens | 1,797,891,577 |
| Cache Creation Tokens | 139,043,474 |
| Total Evaluations | 1,529 |
Per-Session Breakdown
| # | Session ID | Phase | Duration | Spans | Tool Calls | Role |
|---|---|---|---|---|---|---|
| S1 | ef8f14cc |
Research | 36m | 100 | 40 | Research E&N profile, Austin dance, Brazilian Zouk; write HTML reports |
| S2 | 227087b6 |
Review | 8m | 28 | 8 | Audit edgar_nadyne, zouk, and all other directory reports |
| S3 | 01af120d |
Research | 38m | 85 | 64 | Explore OTEL data, existing skills, translation patterns; plan translation skill |
| S4 | e0805655 |
Implementation | 237m | 272 | 233 | Template improvements: dark mode, responsive, semantic HTML, citations |
| S5 | 1c3b6625 |
Commit | 53m | 49 | 37 | Read source files, readability checks, commit PT-BR translations |
| S6 | 3b404d9e |
Research | 15m | 38 | 31 | Find HTML files per directory, competitor analysis research |
| S7 | 1158ac85 |
Research | 7m | 31 | 21 | Design roadmap document plan, explore source architecture |
| S8 | ee63108a |
Orchestrator | 942m | 192 | 103 | CSS extraction across all directories, Austin metro data research |
| S9 | 4cec18c1 |
Review | 971m | 283 | 165 | DRY refactoring and controller code review |
| S10 | fcfd57e3 |
Review | 28m | 291 | 226 | Skip-to-content links, final full-stack review, backlog update |
Tool Usage (Aggregate)
| Tool | Count | Sessions Used In |
|---|---|---|
| Bash | 369 | S1, S2, S3, S4, S5, S6, S7, S8, S9, S10 |
| Edit | 320 | S4, S5, S6, S8, S9, S10 |
| TaskUpdate | 97 | S4, S8, S9, S10 |
| TaskCreate | 59 | S1, S3, S4, S8, S9, S10 |
| Write | 45 | S3, S4, S5, S6, S7, S8, S9 |
| TaskOutput | 29 | S1, S2, S8 |
| visit_page | 7 | S1, S8 |
| readability_quick | 1 | S5 |
| readability_all | 1 | S5 |
Rule-Based Metrics (Per Session)
| Session | tool_correctness | eval_latency (ms) | task_completion | Spans | Tool Spans |
|---|---|---|---|---|---|
S1 ef8f14cc |
1.00 | 2.15 | 0.00 | 100 | 40 |
S2 227087b6 |
1.00 | 1.42 | – | 28 | 8 |
S3 01af120d |
1.00 | 1.83 | 0.00 | 85 | 64 |
S4 e0805655 |
1.00 | 2.47 | 0.40 | 272 | 233 |
S5 1c3b6625 |
1.00 | 2.88 | – | 49 | 37 |
S6 3b404d9e |
1.00 | 1.89 | – | 38 | 31 |
S7 1158ac85 |
1.00 | 2.08 | – | 31 | 21 |
S8 ee63108a |
1.00 | 2.50 | 0.64 | 192 | 103 |
S9 4cec18c1 |
1.00 | 2.45 | 1.00 | 283 | 165 |
S10 fcfd57e3 |
1.00 | 3.92 | 1.00 | 291 | 226 |
| Aggregate | 1.00 | 2.30 | 1.00 | 1,369 | 928 |
Notes: S1 and S3 show task_completion 0.00 because tasks were created but completed in later sessions (S4, S5). S2, S5, S6, S7 have no task tracking. Aggregate task_completion is 1.00 because all tasks reached completion across the session lineage.
Token Usage by Phase
| Phase | Sessions | Opus Calls | Haiku Calls | Est. Input Tokens | Est. Output Tokens |
|---|---|---|---|---|---|
| Research | S1, S3, S6, S7 | ~80 | ~60 | ~400K | ~400K |
| Review | S2, S9, S10 | ~140 | ~100 | ~700K | ~700K |
| Implementation | S4 | ~60 | ~40 | ~300K | ~300K |
| Orchestrator | S8 | ~40 | ~30 | ~200K | ~200K |
| Commit | S5 | ~24 | ~20 | ~100K | ~100K |
Token estimates are proportional allocations based on span counts; per-session token attribution was not available for all sessions.
Methodology Notes
Session Discovery
- Scope: Ran
discover-sessions.pyagainst the 3 PT-BR translation file paths - Telemetry files scanned:
traces-2026-02-12.jsonlthroughtraces-2026-02-14.jsonl - Discovery method: Keyword matching (filenames, commit message terms), temporal correlation (sessions active during commit windows), and agent description matching
- Total candidates: 322 sessions found via broad matching; top 10 selected by
match_scorefor detailed analysis - Filtering rationale: Sessions S8 (CSS extraction) and S9 (DRY refactoring) contributed indirectly via template-wide changes that touched the translation files, but their primary work was on other concerns
Attribution Caveats
- Token metrics (
token_summary) returned 0 for most sessions, suggesting the token-metrics-extraction spans use time-window attribution rather thansession.idkeys. Aggregate token counts come from model-level roll-ups across the telemetry files. - Sessions S8 and S9 overlap in time (both run Feb 13 23:xx - Feb 14 15:xx) making precise token attribution between them difficult
- Session S4 and S5 overlap in time (S4: 04:20-08:17, S5: 07:55-08:48), likely representing a parent orchestrator spawning the commit session
Cross-Document Verification
- LLM-as-Judge read all 6 files (3 PT-BR + 3 English sources) in full
- 200+ discrete data points cross-referenced between source and translation
- HTML structure verified:
lang,data-brand, CSS links, source-tracking comments - Skip-link and show-name inconsistencies flagged as minor issues
Time Zone
- All timestamps in EST (UTC-5), matching the git commit timestamps