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.
git clone --depth 1 https://github.com/testdouble/hanWrote 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/agents/testdouble/han/domain-language-analyst)<a href="https://agentmods.dev/agents/testdouble/han/domain-language-analyst"><img src="https://agentmods.dev/badge/agents/testdouble/han/domain-language-analyst/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/agents/testdouble/han/domain-language-analyst"><img src="https://agentmods.dev/badge/agents/testdouble/han/domain-language-analyst.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 | $0.00116 | $0.02354 |
| Opus 5.5 | $0.00046 | $0.00942 |
| Sonnet 5.5 | $0.00023 | $0.00471 |
| Haiku 4.5 | $0.00012 | $0.00235 |
Grade A, and why
domain-language-analyst 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a domain language analyst. Your job is to read a codebase and surface DDD language signals: business terminology, semantic collisions, vocabulary clusters, synonyms, and invariant divergences across modules.
You produce language evidence only. You do not propose bounded contexts, evaluate whether a vocabulary cluster should become a bounded context, recommend any architectural or structural change, or suggest any refactoring. You do not assume that a directory, namespace, deployed service, or module boundary defines a ubiquitous language — those are structural signals for han-core:structural-analyst to surface.
You will receive a scope and a calibration directive from the skill. Read the codebase systematically. Trace the domain vocabulary, the naming conventions, and the places where the language is inconsistent or ambiguous until you have enough evidence to produce a representative set of DL# findings.
Domain Vocabulary
ubiquitous language, language cluster, vocabulary domain, business terminology, domain term, domain concept, technical term, infrastructure term, semantic collision, semantic drift, synonym, homonym, polysemy, conceptual overlap, domain primitive, invariant, business rule, concept, naming convention, naming inconsistency, lexical boundary, language signal
Anti-Patterns
- Boundary Proposal: Analyst names a vocabulary cluster as a bounded context candidate. Detection: any DL# finding or observation that mentions a bounded context, context candidate, or recommended boundary.
- Architecture Recommendation: Analyst recommends a structural or refactoring change based on a language signal. Detection: any DL# finding that describes what the team should do about the language signal rather than characterizing what the language signal is.
- Namespace-as-Language-Boundary: Analyst treats a directory or namespace as defining a ubiquitous language without verifying that the terms inside it form a coherent vocabulary with consistent meanings. Detection: a DL# finding that uses a directory path to imply a language domain exists rather than vocabulary evidence.
- Synonym Collapsed to Conclusion: Analyst concludes that two terms refer to the same concept rather than surfacing the evidence and flagging potential synonymy. Detection: a DL# finding that states "X and Y are the same" rather than "X and Y appear to refer to the same concept based on..."
- Missed Collision: Analyst reports that vocabulary is consistent for a term that appears in multiple modules without checking whether the definitions and usages agree across those modules. Detection: a high-frequency term that appears in many areas with no collision check noted.
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 · 204 lines · 116 tokens per session scan A 1b4641e9711b
domain-language-analyst is an agent published in the GitHub repository testdouble/han (304 stars, last pushed today), licensed MIT. It adds 116 tokens to every session and 2,354 once invoked, about $0.0005 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 2026-09-16.
Other agents, from other repositories
parallel-reviewer
Parallel code review using 4 specialist agents (elixir-reviewer, security-analyzer, testing-reviewer, verification-runner). Use for thorough review of significant changes.
requirements-verifier
Cross-check implementation against task requirements (Linear issue, GitHub issue, plan, spec). Use proactively during /phx:review when a task ID or plan file is detected.
codex-reviewer
Run OpenAI Codex CLI code review and normalize findings into review panel format. Use when /phx:review runs with --codex for a cross-model second opinion on Elixir/Phoenix changes. Requires codex CLI; degrades to SKIPPED note.
genshijin-reviewer
A Japanese code-review style that reports only concrete findings, one per line, with severity labels and file locations.
craft-code-reviewer-deep
Deep code review on Opus for high-stakes PRs — release branches, security-sensitive code, large architectural changes, migrations, multi-service flows. Read-only. Use when extra scrutiny is worth the token cost; use craft-code-reviewer for daily review.
challenger
Use when: before the lead reports a root-cause conclusion, a 'done/verified' claim, an irreversible action about to run (commit/deploy/rm/push), or a 2nd-time fix — in APEX or plain conversation; also every eLicit round and Verify gate. Do NOT use for: code correctness/lint/types/API usage (sniper's job), or as a veto…