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 skills add zhuangguangdahyh-dotcom/content-ops-studio --skill visual-planninggit clone --depth 1 https://github.com/zhuangguangdahyh-dotcom/content-ops-studioWrote 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/zhuangguangdahyh-dotcom/content-ops-studio/visual-planning)<a href="https://agentmods.dev/skills/zhuangguangdahyh-dotcom/content-ops-studio/visual-planning"><img src="https://agentmods.dev/badge/skills/zhuangguangdahyh-dotcom/content-ops-studio/visual-planning/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/skills/zhuangguangdahyh-dotcom/content-ops-studio/visual-planning"><img src="https://agentmods.dev/badge/skills/zhuangguangdahyh-dotcom/content-ops-studio/visual-planning.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 | $0.00036 | $0.01012 |
| Opus 5 | $0.00018 | $0.00506 |
| Sonnet 5 | $0.00007 | $0.00202 |
| Haiku 4.5 | $0.00004 | $0.00101 |
Grade A, and why
visual-planning 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 11d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Convert one exact COPY_APPROVED Content Package and G3-approved Cover Copy Package into a versioned Visual System, page-specific plans, explicit asset requirements, layout/quality evidence and a First-Page Production Handoff.
Modes
Support PLAN, REVISE_PLAN, VALIDATE, and GET_FIRST_PAGE_HANDOFF.
Use this skill when
The Operator asks to plan visuals for G3-approved Content, choose a Visual Mode, define a multi-page layout/background system, check whether approved text fits, revise a visual direction, validate an existing plan, or prepare the Cover production handoff.
Do not use this skill when
Content has not passed G3; the request changes copy or page count; the task is Painpoint research, image generation, first-page approval, finalization or publishing. Route copy/page-count changes to Content Revision. Report image/G4/Renderer/attachment/publishing requests as unavailable in this phase.
Required sequence
- Call
content_ops_doctor, thencontent_ops_get_visual_context. - Require current G3, exact Content/Copy/Cover Copy versions, specific Subject/Audience/Painpoint context, 4–8 contiguous pages and Page 1 Cover.
- Call
content_ops_plan_cover_conversion, thencontent_ops_plan_visual_direction; retain at least three candidates unless the Operator fixed a mode. Each candidate must declare conversion strategy, background semantic role, text prominence, text-to-image ratio, 310×414 and 186×248 thumbnail targets, and a material difference reason. Resolve typography and composition from the current Operator request, Project/Profile/brand, content needs, Industry/Platform rules, and only then the Universal Default. A fallback candidate set must declare materially different composition families, text regions, asset structures and reading paths. - The Host authors the selected Visual System, Page Visual Plans, Reference Manifest, Asset Requirements and planning-only Layout Feasibility/Quality evidence.
- Call
content_ops_submit_visual_plan; this is a local-only write. - Show the exact plan/hash and obtain explicit Feishu-write confirmation.
- Call
content_ops_finalize_visual_plan; it may update only the bounded visual fields and must read-verify protected fields. - Call
content_ops_verify_visual_plan, thencontent_ops_get_first_page_handoff. - Show the direction, score, limitations and handoff readiness. Stop; do not enter image production automatically.
What ships with it
15 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.
- agents/openai.yaml 584 B
- references/asset-source-policy.md 362 B
- references/color-policy.md 297 B
- references/contract.md 291 B
- references/copy-fidelity-policy.md 260 B
- references/examples.md 355 B
- references/failure-handling.md 325 B
- references/layout-policy.md 266 B
- references/quality-policy.md 279 B
- references/README.md 319 B
- references/revision-policy.md 298 B
- references/tool-policy.md 324 B
- references/typography-policy.md 322 B
- references/visual-mode-policy.md 304 B
- references/workflow.md 252 B
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.
- 11d ago First seen · 60 lines · 36 tokens per session scan A a9727344616c
visual-planning is a skill published in the GitHub repository zhuangguangdahyh-dotcom/content-ops-studio (0 stars, last pushed 15d ago), licensed MIT. It adds 36 tokens to every session and 1,012 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 2026-08-31.
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