DeepReport Intelligence Briefing - December 18, 2025

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Agent ecosystem remains active with fresh prompt audits, type checks, and infrastructure reviews. Workflow health dipped: 3 of the last 10 runs failed or were cancelled, while successful runs still emit high error/warning counts. Issue backlog grew slightly, and label skew persists despite minor improvement in unlabeled-open count.

Data covers discussions and workflow runs from the past 7 days plus weekly issues snapshot (213 issues, 19 open). Key focus areas: prompt quality tuning, workflow noise control, and backlog hygiene.

Full Report Details

🔍 Executive Summary

• Prompt scrutiny and code hygiene intensified (prompt-analysis, prompt-clustering, Typist type checks, Go module reviews, doc consolidation/noob tests).
• Workflow reliability slipped: 3/10 recent runs failed/cancelled; successes still show double-digit errors/warnings, with token spend ~1.71M.
• Issues up to 213 (19 open); label skew toward plan/ai-generated unchanged; unlabeled still 27 (6 open).

📊 Pattern Analysis

• Prompt quality cadence (recurring): New prompt-analysis (6845, 6771) and clustering (6814) runs sustain daily prompt tuning attention.
• Code/doc hygiene (recurring): Typist type consistency (6829), Go module reviews (6818, 6706), doc consolidation (6826), and noob testing (6819) continue quality hardening.
• Infra audits (recurring): MCP structural audits (6828, 6724) and firewall daily report (6853) maintain platform checks.
• Workflow noise (concerning): Successes still log many errors/warnings (e.g., §20337316176 with 113 errors, §20337284655 with 19 errors/16 warnings); 3/10 recent runs failed/cancelled.
• Backlog hygiene (emerging regression): Open issues rose from 13→19; unlabeled total 27 (6 open) with plan/ai-generated labels dominating (124 each).

📈 Trend Intelligence

• Workflow runs (last 10): 26.4m total, ~1.71M tokens, 166 errors, 103 warnings, 3 failures/cancellations. Noise and failures higher than prior window (12.8m, 440k tokens, 1 failure).
• Issues: 213 total vs 201 prior; open 19 vs 13 prior. Labels: plan/ai-generated 124 each (up from 113). Unlabeled 27 (6 open) vs 26 (8 open) — slight open improvement, overall growth.
• Discussions: Burst on Dec 18 across prompt analysis, type checks, Go module reviews, doc consolidation, MCP/firewall audits, and daily status.

🚨 Notable Findings

• Reliability risk: Elevated failure/cancel rate (3/10) with noisy “success” logs could mask real regressions.
• Token spend spike: ~4x jump to 1.71M tokens in last 10 runs suggests verbose prompts or larger analyses.
• Backlog growth: Net +12 issues week-over-week; label skew unchanged, indicating triage debt.

🔮 Predictions and Recommendations

• If prompt-analysis cadence continues, prompt quality should improve, but token costs will stay high unless prompts are trimmed.
• Persistent log noise will keep alert fatigue high; centralizing error/warning suppression or fixing noisy steps will restore signal.
• Without targeted triage, plan/ai-generated dominance and unlabeled backlog will keep growing, slowing prioritization.

✅ Actionable Agentic Tasks (Quick Wins)

Task 1: Reduce workflow log noise on successful runs

• Description: Identify top noisy steps from recent successes (e.g., §20337316176, §20337284655) and silence/resolve redundant error/warning emitters.
• Expected Impact: Clearer signal; lower false-positive rate; easier failure detection.
• Suggested Agent: Workflow Auditor.
• Estimated Effort: Medium.

Task 2: Cut prompt/token verbosity in high-cost runs

• Description: Inspect prompts/outputs for the last 10 runs (total ~1.71M tokens) and trim verbosity or enable summarization where possible, targeting the largest consumers.
• Expected Impact: Reduced token cost and faster runs without quality loss.
• Suggested Agent: Prompt Analysis agent.
• Estimated Effort: Medium.

Task 3: Triage unlabeled and plan/ai-generated issues

• Description: Auto-label/close or route the 27 unlabeled issues (6 open) and re-triage the 124 plan/ai-generated items to improve backlog clarity.
• Expected Impact: Faster prioritization; fewer stale items; clearer owner signals.
• Suggested Agent: Issue Arborist.
• Estimated Effort: Quick.

📚 Source Attribution

• Discussions (past 7 days): 6853, 6845, 6771, 6829, 6818, 6828, 6826, 6819, 6814, 6810, 6822, 6719, 6823.
• Workflow runs analyzed (sample): §20338172675, §20337316176, §20337284655.
• Issues data: /tmp/gh-aw/weekly-issues-data/issues.json (weekly snapshot, 213 issues, 19 open).
• Time range: last 7 days; previous memory state: 2025-12-17T15:13:15Z.

AI generated by DeepReport - Intelligence Gathering Agent

[pelikhan]

/plan

[github-actions[bot]]

🎉 Yo ho ho! Plan Command found the treasure and completed successfully! ⚓💰

#6862

[github-actions[bot]]

This discussion was automatically closed because it was created by an agentic workflow more than 3 days ago.