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 agentmods add skills/dikamilo/dx-workflow/dx-domainnpx skills add dikamilo/dx-workflow --skill dx-domaingit clone --depth 1 https://github.com/dikamilo/dx-workflowWrote 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/dikamilo/dx-workflow/dx-domain)<a href="https://agentmods.dev/skills/dikamilo/dx-workflow/dx-domain"><img src="https://agentmods.dev/badge/skills/dikamilo/dx-workflow/dx-domain.svg" alt="Measured on agentmods" 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 | $0.00074 | $0.00674 |
| Opus 5 | $0.00037 | $0.00337 |
| Sonnet 5 | $0.00015 | $0.00135 |
| Haiku 4.5 | $0.00007 | $0.00067 |
Grade A, and why
dx-domain 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 6d 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 — 38 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dx-domain
The active glossary discipline: challenge a term, sharpen it, and write it to foundation/glossary.md the moment it resolves. Merely reading the glossary for naming is a one-line habit any skill does — this skill is for changing the model, not consuming it. It fires on three triggers during any work:
- Clash — a term contradicts an existing glossary definition. Call it out at once: "The glossary defines 'cancellation' as X, but you seem to mean Y — which is it?"
- Fuzzy — a vague or overloaded term is in play. Propose one precise canonical term: "You said 'account' — is that the Customer or the User? Those are different things."
- Resolved — a term gets nailed down during framing/design/implementation. Capture it immediately, before the moment passes.
Guard. Write only foundation/glossary.md. If context/foundation/ doesn't exist, the project isn't scaffolded — say so and suggest /dx-init. Create glossary.md lazily on the first resolved term.
Sharpen before you write (invoke dx-references with knowledge-layer)
Load knowledge-layer for the glossary entry shape and the standards/lessons/glossary distinctions. Then force precision:
- Invent a concrete edge-case scenario that probes the boundary between two concepts and makes the user commit. Vague terms survive in the abstract and break on specifics.
- Cross-check the code. If what the user says contradicts what the code does, surface it: "Your code cancels whole Orders, but you just said partial cancellation exists — which is right?"
- Be opinionated. When several words mean one thing, pick the best and list the rest under
_Avoid_.
Write inline, glossary-only
The moment a term resolves, add or edit its entry in foundation/glossary.md using the knowledge-layer entry shape — definitional, domain-only, one or two sentences (what it IS, not what it does). Don't batch. Skip general programming concepts (timeouts, retries, error types) — only terms unique to this project's domain belong.
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.
- 6d ago First seen · 38 lines · 74 tokens per session scan A f12419df5828
dx-domain is a skill published in the GitHub repository dikamilo/dx-workflow (5 stars, last pushed 5d ago), licensed MIT. It adds 74 tokens to every session and 674 once invoked, about $0.0004 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.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
comet-memory
使用于 Comet 需要根据有界语义评审包判断是否值得保存个人记忆、候选、更新、遗忘或跳过时。.
recall-memory
Recall relevant long-term memories on demand. Given a topic or question, judges relevance from pre-loaded metadata, loads only relevant files, and returns a concise summary to the main agent.
agent-expert-creation
Create specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve prompts.
relevance-coarse-filter
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
self-improve
Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past…