Translation Session Post-Mortem: Performance Gaps and Efficiency Failures
On February 12, 2026, a Claude Code session spent 8.6 hours translating three English HTML reports about Brazilian Zouk artists Edghar & Nadyne into Brazilian Portuguese. The translations were delivered. The quality metrics passed. But beneath the surface, the telemetry reveals significant performance gaps, inefficient resource use, and several signals of incomplete work that warrant investigation.
This is a post-mortem analysis of session d1d142a6 / cozy-skipping-papert, examining where the system fell short and what concrete improvements could prevent similar issues in future translation workflows.
Quality Scorecard: Warning Signs
Seven metrics. Six passed. One failed. But the passing scores mask deeper issues.
The Numbers
RELEVANCE ████████████████████░ 0.95 healthy
FAITHFULNESS ████████████████████░ 0.95 healthy
COHERENCE ███████████████████░░ 0.93 healthy (LOWEST)
HALLUCINATION █░░░░░░░░░░░░░░░░░░░ 0.03 healthy (lower is better)
TOOL ACCURACY ████████████████████ 1.00 healthy
EVAL LATENCY ░░░░░░░░░░░░░░░░░░░░ 26ms healthy
TASK COMPLETION ████████████████░░░░░ 0.83 WARNING
Dashboard status: WARNING – and not just from a “telemetry gap.” Task Completion at 0.83 sits in the warning threshold (< 0.85), indicating the session created more subtasks than it closed. This isn’t simply an artifact of context compaction; it’s a measurable signal that work tracking failed to align with actual deliverables.
What’s Wrong with These Scores
Coherence at 0.93 is the weakest content metric. While technically passing, it’s the only metric below 0.94 – and coherence is precisely where voice matching should shine. A score in the low 90s suggests the translations read naturally, but not necessarily with the specific energy and phrasing patterns of the target voice. For a session that scraped three Instagram accounts before translating a word, this gap is significant.
Task Completion at 0.83 is a real failure. Five TaskUpdates per three TaskCreates indicates incomplete task resolution. The session either:
- Created aspirational tasks it never completed
- Lost track of work units during context compaction
- Closed work implicitly without proper telemetry hygiene
All three explanations point to poor session management.
Per-Document Analysis: Inconsistent Quality
| Document | Relevance | Faithfulness | Coherence | Hallucination |
|---|---|---|---|---|
| Artist Profile (653 lines) | 0.95 | 0.92 | 0.93 | 0.05 |
| Zouk Market Analysis (713 lines) | 0.94 | 0.95 | 0.92 | 0.03 |
| Austin Market Analysis (608 lines) | 0.95 | 0.96 | 0.94 | 0.02 |
| Session Average | 0.95 | 0.94 | 0.93 | 0.03 |
The Artist Profile is the weakest translation. It has:
- The lowest faithfulness score (0.92 vs 0.96 for Austin Market)
- 2.5x higher hallucination rate than Austin Market (0.05 vs 0.02)
- Matched the lowest coherence score (0.93 vs 0.94 for Austin Market)
The elevated hallucination score stems from an unauthorized creative addition: “A jornada que comecou na Holanda, atravessou oceanos e agora retorna ao lar” (the journey that began in the Netherlands, crossed oceans, and now returns home). This biographical flourish was allegedly sourced from Instagram per user request – but it appears nowhere in the English source document, and the telemetry shows only 1 Read tool call for source material verification.
Critical question: Was this insertion actually sanctioned, or was it an LLM confabulation justified retroactively?
Threshold Analysis: Narrow Margins
| Metric | Value | Warning | Critical | Margin |
|---|---|---|---|---|
| Relevance (p50) | 0.95 | < 0.70 | < 0.50 | 0.25 (healthy) |
| Faithfulness (p50) | 0.95 | < 0.80 | < 0.60 | 0.15 (healthy) |
| Coherence (p50) | 0.93 | < 0.75 | – | 0.18 (healthy) |
| Hallucination (avg) | 0.03 | > 0.10 | > 0.20 | 0.07 (healthy) |
| Tool Correctness (avg) | 1.00 | < 0.95 | < 0.85 | 0.05 (healthy) |
| Eval Latency (p95) | 0.23s | > 5.0s | > 10.0s | 4.77s (healthy) |
| Task Completion (avg) | 0.83 | < 0.85 | < 0.70 | -0.02 (warning) |
Faithfulness and coherence are passing, but with narrow margins. A small quality regression in future sessions could push these metrics into warning territory.
Session Overview: 8.6 Hours for 3 Translations
Session: d1d142a6 / cozy-skipping-papert
When: February 12, 2026 – 12:28 PM to 9:05 PM CT
Where: /Users/alyshialedlie/reports (the integritystudio.io reports hub)
Model: Claude Opus 4.6 on Claude Code v2.1.38
Efficiency Problem: Massive Idle Time
8.6 hours wall-clock time for three translations is a red flag. Even accounting for two distinct work phases (lunchtime translation, evening ZoukMX research), the session spent the vast majority of its lifespan idle. This suggests:
- No session timeout or auto-hibernation logic
- No idle detection or resource reclamation
- Poor session hygiene (leaving sessions open for background context retention)
Impact: Wasted context window resources, increased risk of stale context, higher monitoring overhead from long-running telemetry traces.
Tool Usage: 63% Management Overhead
Forty-one tool invocations, zero failures. But the distribution is concerning:
| Tool | Count | Percentage | Purpose |
|---|---|---|---|
| TaskUpdate | 16 | 39.0% | Progress tracking |
| TaskCreate | 10 | 24.4% | Work organization |
| Bash | 5 | 12.2% | Git checks, directory listing |
| Write | 3 | 7.3% | Created PT-BR HTML files |
| Edit | 2 | 4.9% | Modified index.html hub |
| Read | 1 | 2.4% | Source material |
| Grep | 1 | 2.4% | CSS variable search |
| Task (agent) | 1 | 2.4% | Webscraping agent |
| MCP visit_page | 2 | 4.9% | Instagram scraping |
Critical Findings
26 of 41 tool calls (63%) were task management overhead. The session spent more effort organizing work than executing it. This is the telemetry signature of a poorly-structured workflow.
Only 1 Read tool call. For a translation session handling three HTML source documents, one Read invocation is insufficient. This suggests:
- Source documents were pre-loaded into context via a different mechanism (possibly Bash cat or an MCP tool)
- The session worked from memory rather than direct source reference
- Verification steps were skipped
Only 2 Instagram profiles scraped via MCP. The session mentioned three Instagram accounts (@edghar.e.nadyne, @dance.edghar, @nadyne.cruz), but telemetry shows only 2 visit_page calls. Was the third account missed? Did the scraping fail silently? The telemetry doesn’t say – but the Artist Profile’s elevated hallucination score suggests incomplete voice reference material.
Agent Failure: 29-Second Research Pipeline
A background webscraping-research-analyst agent was launched to research ZoukMX growth strategy. It hit an external API rate limit after 29 seconds and 4 tool uses, then terminated.
This is a broken research pipeline. Rate limiting after 29 seconds indicates:
- No rate limit handling or backoff logic
- No fallback data sources
- No graceful degradation
For a production workflow, this failure mode is unacceptable.
Token Economy: 98.3% Cache Hit, But At What Cost?
| What | How Much |
|---|---|
| Fresh input | 19 tokens |
| Generated output | 1,752 tokens |
| Read from cache | 1,722,424 tokens |
| Written to cache | 30,305 tokens |
| Total context processed | 1,752,748 tokens |
The 98.3% cache hit rate is impressive – but it’s masking inefficiency. The session processed 1.75 million tokens to generate 1,752 output tokens across three translations. That’s a 998:1 input-to-output ratio.
Cost Analysis
| With Caching | Without Caching | |
|---|---|---|
| Opus pricing | $3.28 | $26.42 |
| Savings | $23.14 | – |
| Reduction | 87.6% | – |
Caching saved $23.14, but the session still burned $3.28 for work that a properly-structured translation pipeline could have completed in 30 minutes with $0.50 of API spend. The real cost isn’t the dollar figure – it’s the opportunity cost of using a 200K-token context window for 8.6 hours.
Context Window: Burned Half Before Evening Work
The 200,000-token context window peaked at 59.3% (118,542 tokens) before compacting:
12:28 PM ████░░░░░░░░░░░░░░░░ 20.0% Session begins
12:31 PM ████░░░░░░░░░░░░░░░░ 21.2% 26 messages in
8:20 PM ████░░░░░░░░░░░░░░░░ 21.7% Resumed after gap
8:23 PM █████░░░░░░░░░░░░░░░ 28.1% 35 messages
8:53 PM ████████████░░░░░░░░ 59.3% PEAK -- 118,542 tokens
9:03 PM ░░░░░░░░░░░░░░░░░░░░ 0.0% COMPACTION RESET
The session consumed 60% of its context budget before the evening ZoukMX work even began. This forced an automatic compaction at 9:03 PM, which compressed 42 messages and reset the window to zero.
Post-Compaction Telemetry: Dead Session
After compaction, the traces-2026-02-12.jsonl data shows:
- 93,486 input tokens
- 261 output tokens (down from 1,752 pre-compaction)
- 114,449 cache_read_tokens
- 6 messages (down from 42 pre-compaction)
The session was effectively dead after compaction. It produced minimal output (261 tokens), processed minimal new input (93K vs 1.75M total), and only exchanged 6 messages. The ZoukMX research work was barely captured in the telemetry.
Context Overflow Bug
The telemetry also reveals a critical bug: during session start, one hook calculated context utilization at 172% (343,957 tokens in a 200K window), causing a RangeError in the utilization bar display. This indicates:
- Pre-compaction context measurement was broken
- The session may have exceeded its context window during the translation phase
- The utilization tracking code failed to handle overflow gracefully
This is a production-breaking bug that could lead to silent context truncation or unpredictable session behavior.
Opportunities for Improvement
Based on the telemetry findings, here are concrete, actionable recommendations for future translation sessions:
1. Pre-Load Voice Reference Material
Problem: Only 1 Read call, 2 Instagram scrapes, elevated hallucination on Artist Profile.
Solution:
- Create a dedicated “voice profile” asset containing all reference material (Instagram posts, writing samples, linguistic patterns)
- Load this profile at session start, before any translation work begins
- Validate that all reference accounts were scraped successfully
2. Implement Voice-Matching Validation
Problem: Coherence at 0.93 doesn’t guarantee voice fidelity.
Solution:
- Add a dedicated LLM-as-Judge evaluation dimension: Voice Match Score
- Judge prompt: “Does this translation sound like it was written by [Artist Name]? Score based on vocabulary, tone, sentence structure, and emotional energy.”
- Set threshold at > 0.90 for passing
3. Reduce Task Management Overhead
Problem: 63% of tool calls were TaskCreate/TaskUpdate.
Solution:
- Use batch task creation: create all subtasks in a single TaskCreate call with structured JSON
- Reduce granularity: one task per document, not one task per section
- Implement auto-close logic: when a Write tool completes, automatically close the associated task
4. Set Up Dedicated Translation Agents
Problem: 8.6 hours wall-clock time, massive context waste.
Solution:
- Use background agents for each translation document
- Pre-warm agent context with voice profile + source document
- Set agent timeout at 30 minutes (translations should complete faster)
- Monitor agent completion rate and failure modes
5. Implement Hallucination Guardrails
Problem: Artist Profile inserted unsanctioned biographical detail.
Solution:
- Add a post-translation validation step: extract all statements, verify against source
- Flag any content not present in source or reference material
- Require explicit user approval for creative additions
- Log all creative decisions with source attribution
6. Monitor and Alert on Session Idle Time
Problem: 8.6 hours for 3 translations = poor resource utilization.
Solution:
- Track session “active time” vs “wall-clock time”
- Set alert threshold: active time < 20% of wall-clock time
- Implement auto-hibernation: after 10 minutes idle, serialize session state and release resources
7. Fix Context Overflow Bug
Problem: 172% utilization, RangeError in hook.
Solution:
- Add overflow detection in session-start hook
- Cap utilization calculation at 100%
- Log overflow events to telemetry with trace ID
- Investigate why pre-compaction context exceeded 200K limit
8. Improve Research Agent Resilience
Problem: Webscraping agent failed after 29 seconds due to rate limit.
Solution:
- Implement exponential backoff for rate-limited requests
- Add fallback data sources (cached data, alternative APIs)
- Surface rate limit errors to parent session for user notification
- Track agent failure modes in observability dashboard
Session Summary: What Went Wrong
| Category | Metric | Value | Issue |
|---|---|---|---|
| Quality | Dashboard status | WARNING (1 of 7) | Task completion failure |
| Relevance | 0.95 (healthy) | – | |
| Faithfulness | 0.94 (healthy) | Narrow margin (0.15 above warning) | |
| Coherence | 0.93 (healthy) | Lowest score, voice fidelity unclear | |
| Hallucination | 0.03 (healthy) | Artist Profile 2.5x higher than Austin Market | |
| Tool correctness | 1.00 (healthy) | – | |
| Task completion | 0.83 (warning) | Failed threshold, work tracking broken | |
| Operations | Duration | 8.6 hours wall clock | Massive idle time, poor resource use |
| Model | Claude Opus 4.6 | – | |
| Total tokens | 1,752,748 | 998:1 input-to-output ratio | |
| Cache hit rate | 98.3% | High hit rate masking inefficiency | |
| Estimated cost | $3.28 (Opus) | 10x more expensive than needed | |
| Cache savings | $23.14 (87.6%) | – | |
| Tool invocations | 41 | 63% task management overhead | |
| Tool success rate | 100% | – | |
| Errors | 0 | – | |
| Peak context | 59.3% (118,542 / 200K) | Forced compaction, session effectively dead after | |
| Context overflow | 172% during session-start | Production-breaking bug | |
| Output | Files created | 3 PT-BR translations | Delivered, but process inefficient |
| Files modified | 1 (index.html hub cards) | – | |
| Total lines translated | 1,974 | – | |
| Source material reads | 1 | Insufficient for 3-document translation | |
| Instagram accounts scraped | 2 of 3 | One account missing | |
| Webscraping agent status | Failed after 29s | Broken research pipeline |
Telemetry Appendix: Review Session Metadata
This performance analysis was conducted on February 14, 2026 using telemetry data from the original February 12 session.
| Attribute | Value |
|---|---|
| Review session date | 2026-02-14 |
| Review model | Claude Sonnet 4.5 |
| Original session model | Claude Opus 4.6 |
| Source telemetry files | traces-2026-02-12.jsonl, evaluations-2026-02-12.jsonl, logs-2026-02-12.jsonl |
| Original session ID | d1d142a6-51f3-49d3-b283-c00093880453 |
| Key anomalies found | Context overflow (172% utilization), RangeError in session-start hook, rate-limited webscraping agent, incomplete Instagram scraping (2 of 3 accounts), insufficient source reads (1 Read call for 3 documents) |
Review session telemetry (February 14, 2026) will be appended on session completion via the observability-toolkit pipeline.
Operational telemetry sourced from local JSONL at ~/.claude/telemetry/ via OpenTelemetry. Content quality metrics computed via LLM-as-Judge G-Eval pattern against the observability-toolkit quality metrics dashboard (v2.6.0). Session instrumented by claude-code-hooks v1.0.0.