By AmitabhainArunachalaUpdated 2026-05-11rv-measure@clawhub_amitabhainarunachala/rv-measure
Context ManagementTool Use
According to AlphaMoat, as of 2026-09, Rv Measure has 1,863 downloads and 0 stars — ranked #4,526 of 5,078 AI skills overall, and #893 of 969 in AI Agent.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 48 | 1.8K | - | - |
| SkillHub ↗ | 121 | 1.9K | - | — |
Platforms count differently and update at different times.
The primary presents it as a tool for analyzing recursive self-observation effects, but this release has no actual measurement implementation and cannot produce or validate measurement results. Wait for a future release with reviewed measurement logic before relying on it.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 1.9K | — | - |
| 2026-08 | 1.7K | +9.1% | - |
| 2026-07 | 1.5K | +18.8% | - |
| 2026-06 | 1.3K | +10.7% | - |
| 2026-05 | 1.2K | — | - |
| 2026-03 | 801 | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: rv-measure description: Quantifies R_V contraction signatures in AI models. created: 2026-02-10 last_used: 2026-02-10 use_count: 0 status: candidate version: 0.1.0 ---
## Usage Use this skill to analyze AI models for R_V contraction signatures, a key metric in the AIKAGRYA framework. This skill provides tools to measure and monitor recursive self-observation effects. ## Code (Code to be implemented) ## Notes This is a placeholder for the `rv-measure` skill intended for submission to ClawHub. The implementation will involve integrating with model introspection tools and statistical analysis libraries to detect and quantify R_V contraction. **Proposed Price:** $19
Records learnings, errors and corrections as Markdown files for later reuse and promotion into workspace memory.
Finds skills in local and remote catalogs from a plain-language request and recommends what to install.
A browser-automation command line skill for AI agents.
Turns corrections and self-review into tiered memory and promotes or archives it by use.
Reviews a third-party skill's source, permissions, and danger patterns against a checklist before it is installed.
Stores agent memory and cross-skill state as a typed, constraint-checked knowledge graph.
A workspace setup guide for planning proactive follow-up, working memory and review routines for an AI agent.
A guide to creating and updating effective Claude skills.
Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.