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 skills/gmickel/flow-next/flow-next-auditnpx skills add gmickel/flow-next --skill flow-next-auditgit clone --depth 1 https://github.com/gmickel/flow-nextWrote 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/gmickel/flow-next/flow-next-audit)<a href="https://agentmods.dev/skills/gmickel/flow-next/flow-next-audit"><img src="https://agentmods.dev/badge/skills/gmickel/flow-next/flow-next-audit.svg" alt="Measured on agentmods" 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 | $0.00046 | $0.03779 |
| Opus 5 | $0.00023 | $0.01889 |
| Sonnet 5 | $0.00009 | $0.00756 |
| Haiku 4.5 | $0.00005 | $0.00378 |
Grade A, and why
flow-next-audit 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/flow-next:audit — agent-native memory staleness review
Memory entries decay. A .flow/memory/bug/runtime-errors/ entry logged six months ago might reference a renamed file, a deleted function, or a codepath that no longer exists. Without periodic review, the store accumulates zombie entries and memory-scout surfaces outdated advice.
This skill IS the audit. The host agent (Claude Code / Codex / Droid) walks .flow/memory/, reads each entry, uses Read/Grep/Glob/git to verify references against the current codebase, applies engineering judgment, and decides per entry whether to Keep / Update / Consolidate / Replace / Delete / Harden. Optional autofix mode applies unambiguous actions and marks ambiguous as stale.
Harden is the graduation path: a lesson that keeps getting re-learned and states a mechanically checkable rule should stop riding the context window and become a gate. The audit proposes an artifact in a surface the repo already has — a lint rule, a CI step, or a rule in the substantive CLAUDE.md / AGENTS.md — verifies the gate actually fires, and only then demotes the entry to a pointer at it (file stays on disk, provenance intact). Propose-and-confirm by design: gate surfaces are shared repo infrastructure, so Harden never applies unattended.
Decision entries (.flow/memory/knowledge/decisions/) and glossary terms (GLOSSARY.md files at the repo root and on the ancestor chain) are walked alongside the rest of memory. Decisions get a calibrated judging question — "does the constraint that motivated this choice still hold?" — and Replace becomes a two-step supersession (write successor, mark old decision_status: superseded, never git rm). Glossary terms are scanned for code usage; zero-hit terms get a <!-- stale: ... --> HTML comment via Edit tool (no flowctl glossary mark-stale exists), _Avoid_ aliases appearing in code surface as alias-creep findings.
There is no Python audit-engine, no codex/copilot subprocess dispatch, no deterministic scorer. The host agent is already an LLM and does the work directly. flowctl provides only thin persistence plumbing (memory mark-stale, memory mark-fresh, memory mark-hardened, memory search --status). All judgment — is this recurring, is it mechanizable, which gate surface, what should the rule say — stays in this skill; there is no flowctl gate subcommand and never will be.
Read workflow.md for the full phase-by-phase execution. Read phases.md for the 6-outcomes lookup with memory-schema-specific calibration.
What ships with it
7 files 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.
- 2d ago Changed 15e765dc9648
- 6d ago First seen · 147 lines · 46 tokens per session scan A 29571d45a25e
flow-next-audit is a skill published in the GitHub repository gmickel/flow-next (691 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 3,779 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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