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  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.py against the 3 PT-BR translation file paths
Telemetry files scanned: traces-2026-02-12.jsonl through traces-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_score for 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 than session.id keys. 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