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 dikamilo/dx-workflow --skill dx-domain-discovergit 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-discover)<a href="https://agentmods.dev/skills/dikamilo/dx-workflow/dx-domain-discover"><img src="https://agentmods.dev/badge/skills/dikamilo/dx-workflow/dx-domain-discover/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/dikamilo/dx-workflow/dx-domain-discover"><img src="https://agentmods.dev/badge/skills/dikamilo/dx-workflow/dx-domain-discover.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 | $0.00021 | $0.00627 |
| Opus 5 | $0.00010 | $0.00313 |
| Sonnet 5 | $0.00004 | $0.00125 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
dx-domain-discover 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 10d 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dx-domain-discover
Bootstrap foundation/glossary.md from an existing codebase. Mine the code for the ubiquitous language already living in it — the terms the domain uses — and write them down so every later skill names things consistently. This is the one-time (per-module) extraction pass; /dx-domain keeps the glossary sharp during ongoing work. Re-runnable: pass [module or path] to scope the sweep to one area and grow the glossary incrementally.
Guard. foundation/glossary.md must exist (seeded by /dx-init) — if it is missing, tell the user to run /dx-init first, then stop. Read the current glossary before mining so you extend it, never clobber it.
Invoke dx-references with knowledge-layer for the glossary entry shape and the standards/lessons/glossary distinction — this skill writes the glossary and nothing else.
1 — Mine the vocabulary
Spawn built-in Explore subagents (fan-out, read-only), scoped to [module or path] when given, else the whole repo. Harvest candidate domain terms from:
- Identifiers — type/class/entity names, enums, key function and method names.
- Module and package names — the boundaries the code already draws.
- Comments and existing docs — READMEs, ADRs, doc-comments where terms get defined in prose.
Keep only terms specific to this domain. Drop general programming concepts (cache, retry, handler, DTO) — they are not ubiquitous language.
2 — Resolve and write
For each surviving term, write a glossary entry in the shape from knowledge-layer (**Term**: definition — domain-only, no implementation. _Avoid_: confusable term). Be opinionated: when several names map to one concept, pick one and list the rest under _Avoid_.
Surface, don't average. When you find a term clash (two definitions for one word) or a fuzzy/overloaded term, do not guess — list it and ask the user to resolve it, then record their decision. Keep the file glossary-ONLY: no implementation detail, no rationale, no decisions (those are lessons).
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.
- 10d ago First seen · 42 lines · 21 tokens per session scan A 23c2e83f8a1c
dx-domain-discover is a skill published in the GitHub repository dikamilo/dx-workflow (5 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 627 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.
Other skills, from other repositories
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comet-memory
A review step for deciding whether information should become durable personal memory. It can keep, update, forget, or skip memory candidates based on bounded evidence.
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…