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 axiomantic/spellbook --skill analyzing-domainsgit clone --depth 1 https://github.com/axiomantic/spellbookWrote 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/axiomantic/spellbook/analyzing-domains)<a href="https://agentmods.dev/skills/axiomantic/spellbook/analyzing-domains"><img src="https://agentmods.dev/badge/skills/axiomantic/spellbook/analyzing-domains/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/axiomantic/spellbook/analyzing-domains"><img src="https://agentmods.dev/badge/skills/axiomantic/spellbook/analyzing-domains.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 | $0.00067 | $0.01454 |
| Opus 5.5 | $0.00027 | $0.00582 |
| Sonnet 5.5 | $0.00013 | $0.00291 |
| Haiku 4.5 | $0.00007 | $0.00145 |
Grade A, and why
analyzing-domains 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 1mo 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Analysis
Reasoning Schema
Before analysis: domain being explored, stakeholder terminology, existing system context, integration boundaries.
After analysis: ubiquitous language captured, entity boundaries defined, aggregate roots identified, context map complete, agent recommendations justified.
Invariant Principles
- Language Is the Model: Ubiquitous language IS the domain model. Misaligned terminology → misaligned code.
- Boundaries Reveal Architecture: Bounded context boundaries become service boundaries.
- Aggregates Protect Invariants: An aggregate exists to enforce business rules atomically.
- Events Reveal Causality: Domain events capture what the business cares about.
- Context Maps Are Politics: Upstream/downstream relationships reflect power dynamics.
- Recommendations Follow Characteristics: Agent/skill recommendations emerge from domain properties.
Inputs / Outputs
| Input | Required | Description |
|---|---|---|
problem_description |
Yes | Natural language description of the problem space |
stakeholder_vocabulary |
No | Terms already used by domain experts |
| Output | Type | Description |
|---|---|---|
domain_glossary |
Inline | Ubiquitous language definitions |
context_map |
Mermaid | Bounded contexts and relationships |
entity_sketch |
Mermaid | Entities, value objects, aggregates |
agent_recommendations |
Table | Recommended skills with justification |
Domain Analysis Framework
Phase 1: Language Mining
Extract from: user request, codebase (class/method names), docs, stakeholder conversations. If problem description is minimal, note gaps and request clarification before proceeding.
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.
- 1mo ago First seen · 158 lines · 67 tokens per session scan A c4b3c57fc0bb
analyzing-domains is a skill published in the GitHub repository axiomantic/spellbook (11 stars, last pushed 3d ago), licensed MIT. It adds 67 tokens to every session and 1,454 once invoked, about $0.0003 per session on Opus 5.5. 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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