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/vynnlee/google-flow-mcp/skillnpx skills add vynnlee/google-flow-mcp --skill skillgit clone --depth 1 https://github.com/vynnlee/google-flow-mcpWrote 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/vynnlee/google-flow-mcp/skill)<a href="https://agentmods.dev/skills/vynnlee/google-flow-mcp/skill"><img src="https://agentmods.dev/badge/skills/vynnlee/google-flow-mcp/skill.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 | $0.00088 | $0.01088 |
| Opus 5 | $0.00044 | $0.00544 |
| Sonnet 5 | $0.00018 | $0.00218 |
| Haiku 4.5 | $0.00009 | $0.00109 |
Grade A, and why
google-flow-generate 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 4d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Flow Generate
Runbook to generate images/video on Google Flow through the google-flow MCP server.
Set FLOW_MCP_DIR below to wherever you cloned this repo.
STEP 0 — Ensure the dedicated Chrome is up (always run first)
powershell -NoProfile -ExecutionPolicy Bypass -File "<FLOW_MCP_DIR>/scripts/ensure-flow-chrome.ps1"
READY/LAUNCHED→ proceed.LAUNCHED: if labs.google shows the marketing landing instead of the app, the user must click "Sign in to Flow" once in that Chrome window, then continue.FAILED→ report; do not proceed.
If the mcp__google-flow__* tools are absent, the MCP server needs a client restart
(it loads into RAM at startup).
STEP 1 — Connect
Call flow_connect:
status: connected→ go on.status: oauth_required→ user signs in to Flow once, then retry.WRONG_GOOGLE_ACCOUNT→ stop; setup mismatch.flow_account_check→method:"assumed"means unverified, not a green light.
STEP 1.5 — Build a faithful prompt (always, before generating)
Flow's agent and the models render far more faithfully from a rich, structured prompt than from a bare phrase. Turn the user's request into a complete prompt without betraying intent — enrich, never invent:
- Keep every element the user named; never drop or swap any.
- Add only supporting detail (subject + action + setting + lighting + composition + style/medium + mood + quality cues; for video also camera movement + pacing). Do NOT add subjects/objects/text the user didn't ask for. Preserve explicit constraints ("red mug, no logo") verbatim.
- Match
ratioto the use (16:9 scene, 9:16 story, 1:1 icon).
The server also wraps the prompt so the agent stays bound to it verbatim.
STEP 2 — Images (free)
flow_generate_image with auto_confirm:true. Models: Nano Banana 2 (default),
Nano Banana Pro (best fidelity — prefer for complex prompts), Imagen 4. Params:
prompt, ratio, project_name, campaign. Result files[] = saved paths. (The
credits_consumed flag is hardcoded; images are free against the Flow pool.)
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.
- 4d ago First seen · 94 lines · 88 tokens per session scan A ca9d3a4db2f7
google-flow-generate is a skill published in the GitHub repository vynnlee/google-flow-mcp (2 stars, last pushed 6d ago), licensed MIT. It adds 88 tokens to every session and 1,088 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 2026-08-31.
Other skills, from other repositories
porting-pine-versions
Migrate Pine Script v4 / v5 source to v6 — analyze, apply migration rules, compile-check, iterate until clean. Use when the user hands over an older script or asks to "upgrade to v6" / "port this Pine code".
learn-from-losses
Post-trade review — pull losing trades from the strategy tester, screenshot each entry in context, and cluster common failure patterns. Use when the user asks "why am I losing?", "review my losses", or "what's wrong with my strategy?".
multi-pane-analysis
Cross-asset reasoning across a multi-pane layout — set a 2x2 grid, assign correlated symbols to each pane, and identify leader/laggard/divergence. Use when the user asks to "compare indices", "watch the complex", or wants correlated-asset reasoning.
rebuild-from-screenshot
Reproduce a chart from a screenshot — read the user's pasted image, diff against the live chart, then set symbol/indicators/drawings to match. Use when the user pastes a chart image and says "recreate this" or "set up my chart like this".
strategy-ab-test
Head-to-head strategy comparison — snapshot state, sweep params on strategy A, restore, sweep strategy B, then compare metrics side-by-side. Use when the user asks "which strategy is better?" or "A/B test these two".
morning-prep
Daily morning scan — load saved morning layout, screenshot watchlist symbols, and summarize overnight pre-market state. Use when the user says "good morning", "morning prep", or asks for a pre-open briefing.