Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add momentmaker/kaijutsu --skill convergence-detectgit clone --depth 1 https://github.com/momentmaker/kaijutsuWrote 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/momentmaker/kaijutsu/convergence-detect)<a href="https://agentmods.dev/skills/momentmaker/kaijutsu/convergence-detect"><img src="https://agentmods.dev/badge/skills/momentmaker/kaijutsu/convergence-detect.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 | $0.00063 | $0.01230 |
| Opus 5 | $0.00032 | $0.00615 |
| Sonnet 5 | $0.00013 | $0.00246 |
| Haiku 4.5 | $0.00006 | $0.00123 |
Grade A, and why
convergence-detect 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 8d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
convergence-detect
Iterative loops (review-fix loops, plan-polish loops, retrospective extraction) have a natural endpoint: when each new pass mostly restates the previous one, you're done. This skill is a primitive for detecting that endpoint quantitatively, so loops don't stop too early (missed signal) or too late (wasted tokens).
When to invoke
- Another skill says "use convergence-detect to decide when to stop"
- You're running a multi-round refinement and want a principled stop
- The user asks "are we done iterating?"
The three signals
Track these across the last 3 rounds (minimum). All three must point the same way before declaring convergence.
1. Output size shrinking
Token count of the output decreases round-over-round. New rounds add fewer findings, suggestions, or words than the previous.
| Round | Output tokens |
|---|---|
| N-2 | 1500 |
| N-1 | 800 |
| N | 350 |
Decreasing → signal to stop.
2. Change rate slowing
Of the items in round N, how many are new vs. restated from a previous round?
| Round | Items | New | Restated |
|---|---|---|---|
| N-2 | 12 | 12 | 0 |
| N-1 | 8 | 5 | 3 |
| N | 6 | 1 | 5 |
New / total approaching 0 → signal to stop.
3. Content similarity rising
Compare round N output to round N-1 output. If they're saying mostly the same things in different words, you've converged.
Practical heuristic without an embedding model:
- Take each finding/item from round N
- Check if a substantively-equivalent item appears in round N-1
- If 80%+ of round N items have a match in round N-1, they're saying the same thing
Quantitative scoring
Don't eyeball — measure.
- Output tokens: count tokens (or characters as a proxy) per round. Track the ratio
tokens(N) / tokens(N-1). Below 0.6 = strongly shrinking; below 0.8 = mildly shrinking. - New-item ratio: of the items in round N, count how many are new vs. restated. Items whose normalized text matches an item from N-1 (case-insensitive, whitespace-collapsed, identifier-equivalent) count as restated. Below 0.2 = strongly slowing.
- Jaccard similarity to previous round: |intersect(N, N-1)| / |union(N, N-1)|. Above 0.7 = strongly similar.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 122 lines · 63 tokens per session scan A cd5887beba64
convergence-detect is a skill published in the GitHub repository momentmaker/kaijutsu (3 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 1,230 once invoked, about $0.0003 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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