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 kambleakash0/agent-skills --skill domain-glossarygit clone --depth 1 https://github.com/kambleakash0/agent-skillsWrote 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/kambleakash0/agent-skills/domain-glossary)<a href="https://agentmods.dev/skills/kambleakash0/agent-skills/domain-glossary"><img src="https://agentmods.dev/badge/skills/kambleakash0/agent-skills/domain-glossary/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/kambleakash0/agent-skills/domain-glossary"><img src="https://agentmods.dev/badge/skills/kambleakash0/agent-skills/domain-glossary.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 | $0.00018 | $0.01167 |
| Opus 5 | $0.00009 | $0.00583 |
| Sonnet 5 | $0.00004 | $0.00233 |
| Haiku 4.5 | $0.00002 | $0.00117 |
Grade A, and why
domain-glossary 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 11d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ubiquitous Language Glossary
This skill turns an ongoing conversation into a DDD-style ubiquitous language document. It scans for domain terms, resolves ambiguities, proposes canonical names, and writes a living glossary to DOMAIN_GLOSSARY.md in the working directory.
When to Use
Use this skill when:
- You’re discussing a domain, feature, product, or system and want shared language.
- You mention “ubiquitous language”, “domain model”, or “DDD”.
- You keep tripping over overloaded terms (“account”, “user”, “customer”) and want them nailed down.
You can re-run this skill as the conversation evolves to keep the glossary up to date.
Process
-
Scan the conversation and context
- Read the current conversation, plus any attached docs or PRDs, for domain-relevant nouns, verbs, and concepts.
- Ignore generic technical terms (array, function, endpoint) unless they carry domain meaning here.
-
Detect language problems
Identify and note where:
- The same word is used for different concepts (ambiguity).
- Different words are used for the same concept (synonyms).
- Terms are vague, overloaded, or inconsistently applied.
-
Propose a canonical glossary
- Choose one canonical term per concept and be opinionated about names.
- For each term, write a tight, one‑sentence definition that says what it is, not how it’s implemented.
- List “aliases to avoid” so everyone sees which terms should be retired in this context.
-
Group terms into sections
- Group related terms under headings (e.g. “Order lifecycle”, “People”, “Billing”, “Content model”).
- Each group gets its own Markdown table with columns:
Term,Definition,Aliases to avoid. - If everything belongs to a single cohesive cluster, one table is fine—don’t force fake groupings.
-
Describe relationships
- Add a “Relationships” section that describes how the key terms relate to each other in plain language.
- Use bold term names and, where obvious, simple cardinalities (e.g. “An Order has many Line items”).
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.
- 11d ago First seen · 126 lines · 18 tokens per session scan A 1a688fe8298e
domain-glossary is a skill published in the GitHub repository kambleakash0/agent-skills (9 stars, last pushed 2d ago), licensed MIT. It adds 18 tokens to every session and 1,167 once invoked, about $0.0001 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 2026-08-31.
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