ai-scaling-laws SKILL.md skillA
ai-scaling-laws from vasilyu1983/ai-agents-public is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add vasilyu1983/ai-agents-public/ai-scaling-laws@v1.2Pins v1.2 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- vasilyu1983/ai-agents-public
- Path
skills/universal/ai-scaling-laws/SKILL.md- Versions recorded
- 1
- Latest version
- v1.2, committed Oct 5, 2026
- Size
- 15,496 bytes
- Repository stars
- 101
- License
- MIT
Sizes models and token budgets using Kaplan/Chinchilla scaling laws. Use when reasoning about compute-optimal N and D, tokens-per-parameter ratios, or over-training tradeoffs.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# AI Scaling Laws — Compute-Optimal Sizing Skill Use this skill to allocate training compute between model parameters and tokens, then adjust for data and inference constraints. ## Quick Reference | Concept | Formula / Heuristic | Notes | |---------|---------------------|-------| | Compute budget | C ≈ 6 N D | D = training tokens. **Say which N you use**: Kaplan counts non-embedding params; Chinchilla counts total params (embeddings included). ≈6 = forward + backward matmuls only; the attention term is extra (see compute-budget reference) | | Chinchilla-optimal ratio | D ≈ 20 × N | From Hoffmann et al. 2022; holds compute constant | | Kaplan (2020) allocation | N ∝ C^0.73, D ∝ C^0.27 | No fixed D/N ratio: the compute-efficient path grows D ∝ N^0.37, so D/N falls as C grows. Difference from Chinchilla is *methodological* (FLOP counting, warmup, optimizer tuning), not simply "wrong" — see post-Chinchilla ref | | Optimal N given C | N* ≈ (C / 120)^0.5 | **Derived**, not a paper table: it is C = 6ND with D = 20N | | Optimal D given C | D* ≈ (C / 0.3)^0.5 | Same algebra (D* = 20 N*) | …
Read the whole file at its exact version.
How to install
mdr add vasilyu1983/ai-agents-public/ai-scaling-laws@sha256:2c2977ea76b80bc0The line at the top of this page pins the label v1.2, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_rn255oasurq2jahi)
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Versions
The registry has recorded 1 version of this file, committed on Oct 5, 2026.
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Source
vasilyu1983/ai-agents-public · 101 stars · license MIT · pushed 2026-10-16 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_rn255oasurq2jahi GET https://markdownregistry.com/api/v1/resolve?ref=vasilyu1983/ai-agents-public/ai-scaling-laws GET https://markdownregistry.com/api/v1/blob/2c2977ea76b80bc02e600bf076525c6e5ebb8886524fe0e36556fa5c301a210a
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Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.