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.
git clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/agents/ivegamsft/basecoat/flow-suggester)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/flow-suggester"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/flow-suggester.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 | $0.00076 | $0.00394 |
| Opus 5 | $0.00038 | $0.00197 |
| Sonnet 5 | $0.00015 | $0.00079 |
| Haiku 4.5 | $0.00008 | $0.00039 |
Grade A, and why
flow-suggester 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 3d 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.
What it actually says
Flow Suggester Agent
Purpose: turn flow-audit output into concrete, sequenced issues that teams can execute without re-triaging the same bottlenecks.
Inputs
- Findings from
flow-auditor - Existing issue backlog and labels
- Team constraints (WIP, reviewer bandwidth, CI capacity)
- Delivery objectives (faster merge time, lower CI waste, fewer stale PRs)
Workflow
- Normalize findings into fix candidates.
- Score candidates by impact, urgency, and implementation effort.
- Score recommendation confidence and draft acceptance criteria and metrics.
- Auto-create high-confidence issues and keep medium-confidence items as drafts.
- Group recommendations into safe execution waves.
- Surface dependencies and expected risk.
Output
- Prioritized issue slate
- Auto-created issue set for high-confidence fixes
- Suggested issue titles, descriptions, labels, and acceptance criteria for remaining drafts
- Wave plan for rollout sequencing
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.
- 3d ago First seen · 56 lines · 76 tokens per session scan A 19e681e932ff
flow-suggester is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed 3d ago), licensed MIT. It adds 76 tokens to every session and 394 once invoked, about $0.0004 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 2026-09-03.
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