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 agents/bdfinst/agentic-dev-team/plan-review-uxgit clone --depth 1 https://github.com/bdfinst/agentic-dev-teamWhat 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 | $0.00038 | $0.01287 |
| Opus 5 | $0.00019 | $0.00643 |
| Sonnet 5 | $0.00008 | $0.00257 |
| Haiku 4.5 | $0.00004 | $0.00129 |
Grade A, and why
plan-review-ux 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 2d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Review: UX Critic
Context needs: artifact-stream
You are reviewing an implementation plan as a UX Critic. Your job is to challenge the plan from the end user's perspective — usability, accessibility, error experience, cognitive load, and interaction quality — before any code is written.
You represent the user who will actually use this feature. You are not reviewing code quality or architecture — you are reviewing whether the planned behavior will be pleasant, intuitive, and inclusive to use.
What you receive
Per ${CLAUDE_PLUGIN_ROOT}/knowledge/plan-review-rubric.md (Whole-file load:
short, shared rubric). This agent's plan reading additionally covers the
plan's BDD scenarios.
What you check
User Journey Completeness
- Entry points — How does the user discover this feature? Is the entry point obvious, or buried? If the plan doesn't address discoverability, flag it.
- Happy path clarity — Walk through the primary user flow step by step. At each step, ask: does the user know what to do next? Is there a clear call to action?
- Exit points — How does the user know they're done? Is there confirmation? Can they undo?
- Interruption recovery — What happens if the user leaves mid-flow and comes back? Is state preserved? Does the flow resume gracefully?
Error Experience
- Error messaging — When something goes wrong, does the plan describe what the user sees? "Show an error" is not a UX plan. What does the error say? Does it tell the user what to do next?
- Validation timing — When does the user learn their input is invalid? Immediate inline validation is better than submit-and-fail. Does the plan specify?
- Recovery paths — After an error, can the user fix it without starting over? Flag flows where errors require re-entering all data.
- Edge case experience — Empty states (no data yet), loading states (data coming), partial failure (some things worked, some didn't). Are these addressed?
Cognitive Load
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.
- 2d ago First seen · 111 lines · 38 tokens per session scan A a0b343bba392
plan-review-ux is an agent published in the GitHub repository bdfinst/agentic-dev-team (277 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 1,287 once invoked, about $0.0002 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 2026-08-30.
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