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 three-layer-wisdom-extractiongit 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/three-layer-wisdom-extraction)<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/three-layer-wisdom-extraction"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/three-layer-wisdom-extraction/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/three-layer-wisdom-extraction"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/three-layer-wisdom-extraction.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 | $0.00124 | $0.01930 |
| Opus 5 | $0.00062 | $0.00965 |
| Sonnet 5 | $0.00025 | $0.00386 |
| Haiku 4.5 | $0.00012 | $0.00193 |
Grade A, and why
three-layer-wisdom-extraction 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 7d 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Three-Layer Wisdom Extraction
Most knowledge capture stops at "what worked and what didn't." But the most valuable insights — principles that transfer across entirely different domains — require deliberate abstraction. This skill provides a structured process for that abstraction.
The three layers build on each other: you can't abstract universal principles from thin air, you need the concrete timeline first, then the domain insights, then the philosophical leap.
When This Skill is Worth Using
Not every task warrants three-layer extraction. It's worth the effort when:
- The experience involved a non-obvious breakthrough that required >30 minutes of investigation
- A failed approach revealed something surprising about the problem structure
- The solution involved a counterintuitive choice (e.g., making things worse before better)
- Multiple attempts followed a recognizable pattern (workaround trap, local optimum, etc.)
If the experience was straightforward ("followed docs, it worked"), skip this skill.
The Three Layers
Layer 1: Breakthrough Path
The raw timeline. What was tried, what happened, in what order.
Gather by reviewing conversation history, commit logs, or asking the user. Focus on decision points — moments where a different choice would have led to a different outcome.
Output: A chronological list of attempts with outcomes and key metrics.
Layer 2: Domain Knowledge
Field-specific insights. What patterns, diagnostics, and anti-patterns emerged?
This is what claudeception captures as skills. If claudeception has already run, reuse its
output here. The key additions: boundary conditions (when does the insight NOT apply?) and
diagnostics (what signal would have revealed this earlier?).
Output: Structured domain knowledge (decision trees, comparison tables, or skill files).
Layer 3: Universal Principles
Cross-domain patterns extracted via the five abstraction questions below. These are the highest-value output because they transfer to completely different contexts.
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.
- 7d ago First seen · 191 lines · 124 tokens per session scan A 731da8343ce9
three-layer-wisdom-extraction is a skill published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 13d ago), licensed MIT. It adds 124 tokens to every session and 1,930 once invoked, about $0.0006 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 2026-09-03.
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