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 topprismdata/cultivating-ml-agent --skill agent-nurture-frameworkgit clone --depth 1 https://github.com/topprismdata/cultivating-ml-agentWrote 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/topprismdata/cultivating-ml-agent/agent-nurture-framework)<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/agent-nurture-framework"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/agent-nurture-framework/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/agent-nurture-framework"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/agent-nurture-framework.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00107 | $0.07906 |
| Opus 5 | $0.00053 | $0.03953 |
| Sonnet 5 | $0.00021 | $0.01581 |
| Haiku 4.5 | $0.00011 | $0.00791 |
Grade C, and why
agent-nurture-framework scanned grade C with 2 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 9d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
find ~/.claude/memory/ -name "*.md" -mtime +14 | wc -l Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
ls ~/.claude/skills/*/SKILL.md | wc -l How it starts
The opening of the file, as written. The whole thing — 815 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Nurture Framework: From Novice to Expert Through Knowledge Crystallization
A practical framework for systematically developing AI agent capabilities, derived from 2 months of real-world experimentation. This document covers the complete lifecycle: knowledge acquisition, skill extraction, fragmentation management, knowledge consolidation, and progress measurement.
Theoretical Foundation
This framework is grounded in Nurture-First Development (NFD) (Zhang, 2026, arXiv:2603.10808), which proposes that domain-expert agents should be grown through structured conversational interaction rather than pre-programmed. The core mechanism is the Knowledge Crystallization Cycle: fragmented knowledge embedded in operational dialogue is periodically consolidated into structured, reusable assets.
Key insight from our experience: An agent went from needing 2 weeks to achieve top 10% in a Kaggle competition (S6E2, February) to achieving the same in 24 hours (S6E4, April) -- a 14x speedup attributable entirely to accumulated and crystallized knowledge.
Part 1: The Learning Pipeline
1.1 Five-Stage Learning Loop
┌──────────────────────────────────────────────────────────┐
│ │
│ ┌─────────┐ ┌──────────┐ ┌──────────┐ │
│ │ 1. STUDY │───▶│ 2. VERIFY│───▶│ 3. APPLY │ │
│ │ (Theory) │ │(Notebook)│ │(Practice)│ │
│ └─────────┘ └──────────┘ └────┬─────┘ │
│ ▲ │ │
│ │ ▼ │
│ ┌─────────┐ ┌──────────┐ ┌──────────┐ │
│ │ 5. PLAN │◀──│ 4. EXTRACT│◀──│ Encounter│ │
│ │ (Next) │ │(Crystallize)│ │ Problems │ │
│ └─────────┘ └──────────┘ └──────────┘ │
│ │
└──────────────────────────────────────────────────────────┘
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.
- 9d ago First seen · 815 lines · 107 tokens per session scan C 912257656883
agent-nurture-framework is a skill published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 12d ago), licensed MIT. It adds 107 tokens to every session and 7,906 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 2 findings (reads agent configuration directories, enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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