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 bdfinst/agentic-dev-team --skill domain-analysisgit clone --depth 1 https://github.com/bdfinst/agentic-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/domain-analysis)<a href="https://agentmods.dev/skills/bdfinst/agentic-dev-team/domain-analysis"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/domain-analysis/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/domain-analysis"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/domain-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 156 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
- medium Agent Snooping · line 17 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00115 | $0.02273 |
| Opus 5.5 | $0.00046 | $0.00909 |
| Sonnet 5.5 | $0.00023 | $0.00455 |
| Haiku 4.5 | $0.00012 | $0.00227 |
Grade A, and why
domain-analysis 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Analysis
Role: worker. This command produces a domain-health report — it is analytical, not prescriptive; it does not redesign code or write implementation.
Overview
Assess the domain health of an existing multi-component system. Produce a structured report covering bounded context boundaries, context map relationships, domain event flows, value stream throughput, and a friction report identifying where the architecture inhibits continuous delivery.
This skill is analytical, not prescriptive. It describes what is, not what should be built. For greenfield design, use skills/domain-driven-design/SKILL.md. For code-level violations (anemic model, missing DTOs, wrong layer), use agents/domain-review.md.
Scope
Before starting, determine scope from the user's request:
- Single repo: analyze folder structure, package boundaries, shared models
- Multi-repo / microservices: analyze service boundaries, shared libraries, event contracts, API dependencies
Evidence requirement
Every finding must be grounded in something you actually read. When reporting a God Object, a missing ACL, a coupling trap, or a friction item, cite the specific file:line or file where the evidence was observed. Do not assert that a pattern exists based on naming conventions alone — open the file and confirm it.
Classify each finding by confidence:
- observed — directly visible in a file you read (an import statement, a shared type, a method call)
- inferred — visible at folder or module level without reading implementation (directory name, package dependency in package.json)
- suspected — requires runtime knowledge or cannot be confirmed from static analysis alone
Graph-assisted evidence gathering. Prefer CodeGraph/Repowise over raw Grep
for tracing imports, call relationships, and cross-context dependencies — they're
cheaper and more accurate than re-deriving the call graph from text search. See
knowledge/codegraph-vs-graphify.md
for tool selection and the fallback contract.
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 · 184 lines · 115 tokens per session scan A 83a067ef0ba5
domain-analysis is a skill published in the GitHub repository bdfinst/agentic-dev-team (295 stars, last pushed 4d ago), licensed MIT. It adds 115 tokens to every session and 2,273 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 2026-09-03.
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