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/nnhhoang/claude-engineering-coachWrote 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/commands/nnhhoang/claude-engineering-coach/flow)<a href="https://agentmods.dev/commands/nnhhoang/claude-engineering-coach/flow"><img src="https://agentmods.dev/badge/commands/nnhhoang/claude-engineering-coach/flow/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/commands/nnhhoang/claude-engineering-coach/flow"><img src="https://agentmods.dev/badge/commands/nnhhoang/claude-engineering-coach/flow.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00016 | $0.00118 |
| Opus 5.5 | $0.00006 | $0.00047 |
| Sonnet 5.5 | $0.00003 | $0.00024 |
| Haiku 4.5 | $0.00002 | $0.00012 |
Grade A, and why
flow 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 2mo 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
Call the coach_flow tool from the AI Engineering Coach MCP server. Pass any ISO dates in $ARGUMENTS as fromDate / toDate; otherwise use the 90-day default.
Present: the deep-work score and trend, the user's most productive hours, follow-up latency, and session continuity. Finish with one concrete suggestion to protect or extend flow, grounded in the numbers.
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.
- 2mo ago First seen · 9 lines · 16 tokens per session scan A a0dc93d59af7
flow is a command published in the GitHub repository nnhhoang/claude-engineering-coach (1 stars, last pushed 4mo ago), licensed MIT. It adds 16 tokens to every session and 118 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
daily-log
Append entry to today's daily log.
ws
Word summary — anything told at N levels, from N words down to 1 word, for anything: a topic ("russian literature"), a URL, YouTube link, file, pasted text, a question, or "this". A leading number sets N (5 by default). Shortcut for the Fabric skill's WordSummary intent.
cancel-ralph
Cancel active Ralph Loop.
engage.actions
Execute Phase 7 - Actions on Objectives and Goal Achievement.
r
Resume the last handoff and carry on with the outstanding work.
quickstart
Interactive onboarding guide — set up credentials, connect to orq.ai, and learn every command and skill.