convergence-monitoring
convergence-monitoring is a Session review & learning skill for AI agents, published by Kanevry in the session-orchestrator repository on GitHub. Skillspace rates convergence-monitoring D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Monitor iterative improvement loops for convergence. Three signals — shrinking diff, pass-rate plateau, velocity — drive a Stop/Continue/Investigate decision at each inter-wave checkpoint. Distinct from /evolve (retrospective) and session-reviewer (wave output review): convergence-monitoring answers \"are we making progress?\" not \"was the last wave correct?\". Primary consumer: /autoresearch loops and wave-executor inter-wave checkpoints.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 50
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 99 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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