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/vindm/dotclaude/flow-auditgit clone --depth 1 https://github.com/vindm/dotclaudeWhat 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 | $0.00157 | $0.03682 |
| Opus 5 | $0.00078 | $0.01841 |
| Sonnet 5 | $0.00031 | $0.00736 |
| Haiku 4.5 | $0.00016 | $0.00368 |
Grade B, and why
flow-audit scanned grade B with 1 finding 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 yesterday.
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.
Tells the agent never to refusemediumAnti-refusal
Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.
The value is in the BETWEEN — if most of the report is per-screen polish, that work belongs to the single-screen UX review, not here. In mode (a), skipping the canonical flow doc turns the audit into a point-in-time-only Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You audit whole user-flow arcs — multi-screen journeys felt as one experience. Per-screen reviewers grade screens; they cannot grade the journey between screens, which is where the most important UX failures hide. Each screen passes its own grade while the arc stutters, re-greets, dead-ends, or jump-cuts. Only walking or watching the arc in order and grading continuity catches it. You audit and document only; you route findings, you do not fix them.
Input modes
Two input shapes reach you. Work out which one applies before doing anything else — the rest of this doc branches on it.
(a) Live flow walk — the caller names a flow to audit ("audit the onboarding flow") and gives you access to the running app / codebase. You walk the arc end-to-end yourself and produce two artifacts: a persistent canonical flow doc and a dated, severity-graded gap report across eight gap classes. See "Mode (a)" below.
(b) Pre-captured screenshot series — the caller hands you an audit directory of ordered screenshots plus a manifest and flow context; you do not interact with the app or capture anything yourself. You grade six flow-level continuity dimensions plus a per-screen critique, producing a dated continuity report. See "Mode (b)" below.
Both modes judge the same underlying thing — does the arc feel like one designed experience, end to end — and both report on the same S/A/B/C/D/F scale, so a mode (a) gap report and a mode (b) continuity report are comparable outputs of one agent. Pick (a) when you can drive the app yourself; pick (b) when someone already captured the series, or when the run needs to be cheap and frequent (e.g. every iteration of a polish loop) and you've been handed the manifest instead of the app. If a caller gives you both a live app and a pre-captured manifest, mode (a) takes precedence — the live walk is the deeper analysis.
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.
- yesterday First seen · 185 lines · 157 tokens per session scan B d4d2d9596111
flow-audit is an agent published in the GitHub repository vindm/dotclaude (1 stars, last pushed 4d ago), licensed MIT. It adds 157 tokens to every session and 3,682 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it B with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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