Add Tampa city council analysis tasks

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Tampa City Council Meeting Analysis Tasks

Six new benchmark tasks analyzing the April 2, 2026 Tampa City Council meeting transcript.

Tasks Added

1. task_meeting_council_votes - List motions and vote outcomes (Closes Task: meeting_council_votes #196)
2. task_meeting_council_public_comment - Summarize public comments (Closes Task: meeting_council_public_comment #197)
3. task_meeting_council_budget - Extract budget discussions (Closes Task: meeting_council_budget #198)
4. task_meeting_council_upcoming - Extract upcoming events/deadlines (Closes Task: meeting_council_upcoming #199)
5. task_meeting_council_contact_info - Extract contact information (Closes Task: meeting_council_contact_info #200)
6. task_meeting_council_neighborhood - Identify neighborhood/district mentions (Closes Task: meeting_council_neighborhood #201)

Asset

• assets/meetings/2026-04-02-tampa-city-council-transcript.md — realtime captioning transcript of a 3.5-hour city council meeting

Task Details

All tasks use the meeting category with hybrid grading (50% automated / 50% LLM judge). Each task asks the agent to produce a specific analytical report from the raw transcript.

The transcript is a real-world realtime captioning output (ALL CAPS, formatting artifacts, no speaker attribution formatting) covering a meeting with 7 council members, dozens of public speakers, multiple presentations, rezoning hearings, and heated debates on topics ranging from affordable housing to police infrastructure.

Closes #196, Closes #197, Closes #198, Closes #199, Closes #200, Closes #201

[kilo-code-bot]

Code Review Summary

Status: No Issues Found | Recommendation: Merge

Six well-structured benchmark tasks with consistent formatting, appropriate hybrid grading weights, and solid regex-based automated checks. The grading functions handle missing files gracefully with fallback path lookups and return zero-scored dicts on failure — good defensive pattern.

Files Reviewed (7 files)

• tasks/manifest.yaml
• tasks/task_meeting_council_votes.md
• tasks/task_meeting_council_public_comment.md
• tasks/task_meeting_council_budget.md
• tasks/task_meeting_council_upcoming.md
• tasks/task_meeting_council_contact_info.md
• tasks/task_meeting_council_neighborhood.md

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_{Reviewed by claude-4.6-sonnet-20260217 · 119,575 tokens}

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🧪 Test Started

Instance: 66.42.90.87 (Vultr vc2-2c-4gb, ATL)
Branch: tasks/meeting-council

Models Being Tested

#	Model
1	openrouter/anthropic/claude-opus-4.6
2	openrouter/openai/gpt-5.4
3	openrouter/google/gemini-3-pro

Tasks Being Tested

#	Task ID	Description
1	task_meeting_council_votes	List motions and vote outcomes
2	task_meeting_council_public_comment	Summarize public comments
3	task_meeting_council_budget	Extract budget discussions
4	task_meeting_council_upcoming	Extract upcoming events/deadlines
5	task_meeting_council_contact_info	Extract contact information
6	task_meeting_council_neighborhood	Identify neighborhood/district mentions

Estimated completion: ~30-45 minutes (3 models × 6 tasks, running in parallel)
Started: 2026-04-15 ~08:00 EDT

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🦀 PinchBench PR Test Results — #326

Instance: 66.42.90.87 (vc2-2c-4gb, ATL)
Branch: tasks/meeting-council
Benchmark Version: 2.0.0-rc1
Duration: ~25 minutes (models in parallel)

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Score Grid (Task × Model)

Task	Claude Opus 4.6	Gemini 2.5 Pro	GPT-5.4
votes	92%	71%	❌ error
public_comment	93%	94%	❌ error
budget	87%	79%	❌ error
upcoming	94%	87%	❌ error
contact_info	100% 🏆	97%	❌ error
neighborhood	100% 🏆	84%	❌ error
Overall	94.4%	85.3%	0%

Detailed Automated Check Breakdown

Check	Opus	Gemini
votes: report_created	✅	✅
votes: minutes_vote	✅	✅
votes: item12_abstain	✅	✅
votes: item14_15_rollcall	✅	✅
votes: item19_unanimous	✅	✅
votes: item22_continued	✅	❌
votes: item23_first_reading	✅	✅
votes: item25_carlson_no	✅	✅
votes: item26_28_reconsider	✅	✅
votes: summary_count	❌	❌
budget: annual_revenue_increase	❌	❌
upcoming: april_20_townhall	✅	❌
upcoming: budget_workshops	✅	❌
neighborhood: macdill	✅	❌
neighborhood: rezoning_addresses	✅ (3/3)	⚠️ (2/3)
public_comment: zion_multiple	❌	❌

Efficiency

Metric	Opus 4.6	Gemini 2.5 Pro
Total tokens	1,318K	1,156K
Total cost	$5.97	$1.67
Cost/task	$0.99	$0.28
Score/dollar	0.95	3.06
Avg exec time	~130s	~122s

GPT-5.4 Failure Analysis

GPT-5.4 failed all 6 tasks with "Could not find agent workspace" errors. The OpenClaw agent was created but sessions produced no transcripts (0 tokens consumed). This appears to be a benchmark harness compatibility issue, not a task design problem. The agent workspace isn't being initialized correctly for this model. All 6 tasks errored in about 22 seconds each (compared to 90-175s for working models).

⚠️ Note: GPT-5.4 was requested as openrouter/openai/gpt-5.4. The model ID is valid on OpenRouter. The failure is in the OpenClaw agent session bootstrapping, not model routing.

Task Quality Assessment

Strengths:

• All 6 tasks produce meaningful score differentiation between models (Opus vs Gemini spread: 3-21 percentage points)
• Hybrid grading (50/50 automated + LLM judge) works well — automated checks catch hard facts while LLM judge evaluates quality
• The 206KB transcript (3.5hr meeting) is a good real-world challenge
• Tasks exercise different analytical skills: votes (procedural parsing), public_comment (speaker identification), budget (numerical extraction), upcoming (temporal reasoning), contact_info (entity extraction), neighborhood (geographic reasoning)

Observations:

• summary_count regex in votes task failed for both models — the pattern (?:total|summary|count).*(?:\d+\s*vote|\d+\s*motion) may be too strict (Opus produced "41 total, 37 unanimous, 4 with dissent" but regex expects format like "41 votes")
• annual_revenue_increase failed for both models — the regex requires "1.5|two" + "million" + "revenue" together; models may phrase this differently
• zion_multiple failed for both — regex requires Zion + speaker name on same line; speakers may be in separate sections
• Gemini missed item22_continued, april_20_townhall, budget_workshops, and macdill — these are factual extraction gaps
• Both models struggled with some automated checks but scored well on LLM judge, suggesting automated regex patterns may need loosening in a few places

Model Notes

Claude Opus 4.6: Dominant performance. 100% on contact_info and neighborhood. Efficient 3-request pattern (read transcript, write report, done). Identified 41 votes (votes task) and 81 people (contact_info). Total cost: $5.97 for 6 tasks.

Gemini 2.5 Pro: Strong performance at 3.5x better cost efficiency. Matched or nearly matched Opus on public_comment (94% vs 93%). Main weakness was votes task (71%) where it missed some dissenting votes and item 22 continuance. Total cost: $1.67 for 6 tasks.

Note on model substitution: Original request was for google/gemini-3-pro which does not exist on OpenRouter. Used google/gemini-2.5-pro instead.

Recommendation

✅ Merge — Tasks are well-designed with appropriate difficulty spread. Score distribution is healthy:

• Not too easy (no model scored 100% overall)
• Not too hard (top model hit 94.4%)
• Good differentiation between models

Minor suggested follow-ups (non-blocking):

1. Loosen summary_count regex to catch natural language summary formats
2. Loosen annual_revenue_increase to allow more phrasing variants
3. Consider if zion_multiple cross-referencing regex is matching as intended
4. Investigate GPT-5.4 agent workspace issue separately (benchmark harness bug, not task bug)

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