Borrowing it
Nothing to install: this file belongs to blueman82/ai-counsel. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/blueman82/ai-counsel/main/.claude/skills/convergence-debugger/SKILL.mdgit clone --depth 1 https://github.com/blueman82/ai-counselWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/blueman82/ai-counsel/convergence-debugger)<a href="https://agentmods.dev/skills/blueman82/ai-counsel/convergence-debugger"><img src="https://agentmods.dev/badge/skills/blueman82/ai-counsel/convergence-debugger.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.03891 |
| Opus 5 | $0.00022 | $0.01946 |
| Sonnet 5 | $0.00009 | $0.00778 |
| Haiku 4.5 | $0.00004 | $0.00389 |
Grade A, and why
convergence-debugger scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 480 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Convergence Detection Debugger
Overview
The AI Counsel convergence detection system (deliberation/convergence.py) determines when models have reached consensus and can stop deliberating early. It uses semantic similarity comparison between consecutive rounds, with voting outcomes taking precedence when available.
Common Issue: Convergence not detected → wasted API calls Common Issue: Early stopping not triggering → deliberation runs full max_rounds Common Issue: Semantic vs voting status conflict → confusing results
Diagnostic Workflow
Step 1: Examine the Transcript
What to look for:
- Are responses actually similar between rounds?
- Is there a "Convergence Information" section?
- What's the reported status and similarity scores?
- Are there votes? What's the voting outcome?
File location:
# Transcripts are in project root
ls -lt transcripts/*.md | head -5
# Open most recent
open "transcripts/$(ls -t transcripts/*.md | head -1)"
Convergence section example:
## Convergence Information
- **Status**: refining (40.00% - 85.00% similarity)
- **Average Similarity**: 72.31%
- **Minimum Similarity**: 68.45%
Voting section example (overrides semantic status):
## Final Voting Results
- **Winner**: TypeScript ✓
- **Status**: majority_decision
- **Tally**: TypeScript: 2, JavaScript: 1
Missing convergence section?
→ Check if round_number <= min_rounds_before_check (see Step 2)
Step 2: Check Configuration
Read the config:
cat config.yaml
Key settings to verify:
deliberation:
convergence_detection:
enabled: true # Must be true
semantic_similarity_threshold: 0.85 # Convergence if ALL participants >= this
divergence_threshold: 0.40 # Diverging if ANY participant < this
min_rounds_before_check: 1 # Must be <= (total_rounds - 1)
consecutive_stable_rounds: 2 # Require this many stable rounds
early_stopping:
enabled: true # Must be true for model-controlled stopping
threshold: 0.66 # Fraction of models that must want to stop (2/3)
respect_min_rounds: true # Won't stop before defaults.rounds
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 480 lines · 43 tokens per session scan A 75cd4a62a52c
convergence-debugger is a skill published in the GitHub repository blueman82/ai-counsel (1 stars, last pushed 4mo ago), licensed MIT. It adds 43 tokens to every session and 3,891 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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