Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add bdfinst/agentic-dev-team/plugin install dev-teamWrote 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/bdfinst/agentic-dev-team/ubiquitous-language)<a href="https://agentmods.dev/skills/bdfinst/agentic-dev-team/ubiquitous-language"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/ubiquitous-language/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/bdfinst/agentic-dev-team/ubiquitous-language"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/ubiquitous-language.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00166 | $0.02062 |
| Opus 5.5 | $0.00066 | $0.00825 |
| Sonnet 5.5 | $0.00033 | $0.00412 |
| Haiku 4.5 | $0.00017 | $0.00206 |
Grade A, and why
ubiquitous-language 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 14d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ubiquitous Language
Produces a queryable per-concept glossary at .plans/domain/. Each concept gets its own file (one-sentence definition, classification, related terms). The glossary feeds domain-review as classification ground truth — run this skill before domain-review for sharper findings.
This skill never modifies source files. It writes only to .plans/domain/ and .plans/domain-language-plan.md.
Announce on start
"Running ubiquitous-language. Gathering domain signals from $ARGUMENTS (or cwd if empty). I'll apply a four-gate filter and write one file per approved business concept to .plans/domain/. Existing files are append-mostly — previous definitions are preserved."
Phase 1: Gather signals (grep-based, no build required)
Run the colocated gather script. It collects raw candidates; it does not decide what is domain language (that is Phase 3's job).
python3 .claude/skills/ubiquitous-language/scripts/collect_domain_signals.py "$ARGUMENTS"
The script writes to .plans/raw/domain-language/:
class-names.txt— PascalCase class/struct/record/interface names from domain and application layersenum-values.txt— enum declarations with their valuesdomain-events.txt— type names ending inCreated,Updated,Submitted,Approved,Cancelled,Rejected,Completed,Failed,Requestedbdd-names.txt— BDD scenario and step names from.featurefiles (Given_,When_,Then_,Should_)validator-rules.txt— validation rule method names and error messagesinterface-names.txt— interface/protocol names from domain and application layers
If the script is unavailable, gather manually using Grep with the patterns documented in the script's comments.
Graph-assisted disambiguation. Prefer CodeGraph/Repowise over more Grep passes to check a candidate term's actual call sites and surrounding context when the raw signal is ambiguous (e.g. a name that could be a technical or a business concept, or two near-duplicate names that might be the same concept under different spellings). This is a supplement to Phase 1's grep-based collection, not a replacement — the collector script output is still the primary candidate source. See knowledge/codegraph-vs-graphify.md for tool selection and the fallback contract.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 14d ago Changed · +8 lines 49a6348f6263
- 1mo ago First seen · 193 lines · 166 tokens per session scan A 455cd2f65995
ubiquitous-language is a skill published in the GitHub repository bdfinst/agentic-dev-team (295 stars, last pushed 4d ago), licensed MIT. It adds 166 tokens to every session and 2,062 once invoked, about $0.0007 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 2026-09-05.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
component-fixtures
Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.