Creator · inkboard
Last updated · Sep 5, 2026
Build and maintain an explorable, progressively-disclosed isometric "atlas" of a system's architecture — an interactive page (hover to read, click to pin, go inside for steps, moving data packets you can inspect, chapters that reveal the system a few structures at a time) plus a
Sandbox only
Install targets
Codex install prompt
Install the "system-atlas" agent skill from https://github.com/inkboard/system-atlas/tree/main/skills/system-atlas. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Build and maintain an explorable, progressively-disclosed isometric "atlas" of a system's architecture — an interactive page (hover to read, click to pin, go inside for steps, moving data packets you can inspect, chapters that reveal the system a few structures at a time) plus a generated text twin (SYSTEM.md) and question tracking by ID, all from one data file in the repo. Use this whenever someone wants to discuss, design, review, or explain an architecture visually — "make an atlas", "map the system", "make the architecture explorable", "visualize the codebase/agent/pipeline so we can talk about it", "a diagram I can click around", "walk me through how it fits together", or when an architecture discussion is producing a pile of open questions that need tracking across feedback rounds. Also use it to update an existing atlas after decisions change. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"inkboard-system-atlas","task":"Install system-atlas","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + Cursor + Browser agents
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add inkboard/system-atlas --skill system-atlas
Maintenance
fresh
6d since push
Risk
Safe to try
Quality score needs review
GitHub quality
392
73/100 Quality · 79/100 Trust
Coverage tags
Review notes
Quality score needs review · Stars/forks activity: 392 stars, 15 forks; issue activity unavailable in current metadata
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
392 GitHub stars
Repo activity
392 stars, 15 forks
Maintenance
6d since push
License
MIT
Install
npx skills add inkboard/system-atlas --skill system-atlas
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add inkboard/system-atlas --skill system-atlasDo not use when
Alternative
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Alternative
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Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20system-atlas%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20system-atlas%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/inkboard-system-atlas/install
Agent should check
Copy prompt
Task: Use system-atlas in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20system-atlas%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/inkboard-system-atlas/install
Install command: npx skills add inkboard/system-atlas --skill system-atlas
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/inkboard-system-atlas/install
LLM text format
/api/skills/inkboard-system-atlas/install?format=text
Find alternatives
/api/skills/search?q=system-atlas&limit=3
Agent prompt
Use system-atlas for this task. Review https://www.openagentskill.com/api/skills/inkboard-system-atlas/install, then install with: npx skills add inkboard/system-atlas --skill system-atlasRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/inkboard-system-atlas
LLM text
/api/registry/manifest/inkboard-system-atlas?format=text
Install alias
/api/registry/install/inkboard-system-atlas
Recommend
/api/registry/recommend?task=Use%20system-atlas%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code, Cursor, Browser agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Research agents
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO392 GitHub stars
Stars/forks activity
CHECK392 stars, 15 forks; issue activity unavailable in current metadata
Recent maintenance
PASS6d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
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--- name: system-atlas description: Build and maintain an explorable, progressively-disclosed isometric "atlas" of a system's architecture — an interactive page (hover to read, click to pin, go inside for steps, moving data packets you can inspect, chapters that reveal the system a few structures at a time) plus a generated text twin (SYSTEM.md) and question tracking by ID, all from one data file in the repo. Use this whenever someone wants to discuss, design, review, or explain an architecture visually — "make an atlas", "map the system", "make the architecture explorable", "visualize the codebase/agent/pipeline so we can talk about it", "a diagram I can click around", "walk me through how it fits together", or when an architecture discussion is producing a pile of open questions that need tracking across feedback rounds. Also use it to update an existing atlas after decisions change. ---
# System Atlas
An atlas is one data file that renders two views: an **interactive isometric map** (a single self-contained HTML file), and a **generated text twin** (`SYSTEM.md`) with the decisions table, every structure, the flows, and the open questions by ID. The data file is the only thing anyone edits; both views rebuild from it. It sits beside a hand-written glossary (`CONTEXT.md`) and ADRs.
The reason for this shape: an architecture discussion produces decisions, questions, and vocabulary faster than any one document can hold, and the person you are discussing with wants to *see* the system, not read it. The map is for them; the text twin is for the repo and for you next session; the single source is what keeps the two honest.
This skill was distilled from building a real agent-architecture atlas across one long design session and several rounds of feedback. The user's corrections from that session are the rules below; `references/process-and-lessons.md` has the story.
## When to reach for it — and when not
Use it when the system is new enough that vocabulary, decisions, and questions are still moving, and there will be more than one feedback round. Don't use it for a finished system that only needs a README, or for one diagram in a PR.
## Process
Follow the order — each step was earned by a correction the first time round.
1. **Read the inputs before drawing.** The vision doc, the repo's existing surfaces, and whatever prior art the user allows (ask — they may forbid a branch or a source). If you will build on a framework, read its docs first; hand long docs to a subagent with your specific design questions and have it return a primer with gotchas and a "what it does not give us" list. Drawing before this produces boxes that don't map to anything real. 2. **Discuss before drawing.** Propose the structure in chat, mapped to the runtime's real primitives, and ask only the questions you cannot derive from the repo. Take defaults for the rest and say which. Ask as plain chat text. 3. **First atlas — the whole system.** Copy `assets/` into the atlas home (`template.html`, `build.mjs`, and `data.example.mjs` renamed to `data.mjs`), fill the data, build, publish. **Where the atlas home is depends on the repo's docs policy.** Some repos commit design docs freely — then `docs/<system>/atlas/` in-tree is right. Other repos deliberately commit only ADRs and `CONTEXT.md`, with specs and evidence going to the issue tracker instead; in that case put the atlas, `SYSTEM.md` and `research/` in a git-ignored scratch directory and attach `SYSTEM.md` plus the research to the spec issue as comments when the spec is published, keeping only `docs/<system>/adr/` and `docs/<system>/CONTEXT.md` in-tree. Ask which policy applies before committing anything. Learned the hard way: committing the whole set produced a 3,900-line docs PR and four review rounds reconciling three restatements of one design — with ADRs plus a glossary only, there is one place to be consistent. If your agent has a design-guidance skill for HTML artifacts, load it before touching the template; read `references/design-language.md` for the visual rules either way. 4. **Progressive disclosure.** A whole system at once reads as noise ("hard to parse" was the first correction). Ten-ish chapters; each adds at most three structures and runs one small flow that only touches revealed structures; the last chapter shows everything with a flow picker. Unrevealed structures stay in the index, dimmed, with their chapter number. Panels are summary-first: one sentence, then *Read more* and *Steps* folded. 5. **Shapes and labels.** Letters on boxes are not enough ("better box shapes/labelling" was the second correction). Give each role a shape and put a readable name label on the canvas under every structure — see design-language. 6. **Text twin.** `CONTEXT.md` is a glossary and nothing else (domain-model format: the nouns, one line each); ADRs only for decisions that are hard to reverse, surprising without context, and the result of a real trade-off — these two are the in-tree pieces. `SYSTEM.md` is generated and `research/` holds evidence; both live with the atlas (scratch dir or `docs/`, per step 3). Don't open issues unless asked. 7. **Feedback by question ID.** Every question is `Q-<code><n>` with a state: open (a string), resolved `{q, r}` (answer + date), or routed `{q, to}` (handed to a named next step such as a deep dive). Record the user's words. If they call something "not a question", drop it; if they say "I don't get this", explain with a concrete example *before* resolving. After each round: rebuild, republish, update memory. 8. **Deep dives feed back.** Research with subagents against one shared brief (the interface we own, the requirements that separate candidates, a usage model for cost, a fixed deliverable shape). Write a synthesis with a normalized cost/fit grid. Fold resolutions into the data as `{q, r: '… (from the deep dive, date)'}`. If the user rejects a proposal, sweep *every* file and rewrite — a banner on top of a stale section is not enough; they will find it. 9. **Keep it current.** "The atlas is great for me — but not if it's not up to date." One source, rebuild and republish after every change, never hand-edit generated files, and leave a `README.md` in the docs folder explaining the set (table in process-and-lessons).
## Publishing the map
`atlas.html` is one self-contained file — no build step, no external assets beyond a Google Fonts stylesheet. Publish it whichever way the person can actually open:
- If your agent can publish a hosted HTML artifact, publish it there and keep the URL stable across rebuilds; put it in `META.artifactUrl` so the generated `SYSTEM.md` links to it. - Otherwise serve the folder with any static server (`npx serve`, `python3 -m http.server`) and hand over the local URL, or commit the file and let the repo's pages host serve it.
Either way the rule is the same: one URL, republished after every data change, never a second copy.
## What done looks like
- `<atlas home>/data.mjs` exists and is the only edited source; `bun <atlas home>/build.mjs` writes `SYSTEM.md` and `atlas.html` without error. - The atlas is published at a stable URL and republished there after every data change. - Every structure has `one`, `what`, `how`, a `short` label, a role `kind`, and its questions; ghosts are marked; chapters exist with per-chapter flows; the last chapter is the whole system. - `SYSTEM.md` carries the decisions table, the question index with IDs and states, and the "how this file is maintained" footer. - Project memory records the atlas URL, docs paths, locked decisions with dates, what the user rejected and why, and the next step.
## Verify before publishing
Syntax-check the built script (`new Function(js)`), then look at it: serve the folder with a static server and open it in a real browser — `file://` renders as a static snapshot in some in-app browsers and the fonts may not load. Resize to ~1280×800 and screenshot a first chapter, a middle chapter, the last chapter, an inside view, and the light theme. Keep `<!doctype html>` first and `<meta charset="utf-8">` immediately after it: without the doctype the page renders in quirks mode, and without an early charset the arrows render as mojibake. Click a structure and confirm the panel says **pinned** and offers *Go inside*, and click a packet dot: this renderer rebuilds its whole scene on every draw, so a stray `render()` in a hover handler detaches the element under the cursor and the browser stops synthesising clicks — the map still looks perfect in a screenshot while nothing responds to a click. After every decision, grep the outputs for the stale words (`pending`, the old model name, the rejected design) — the person reads everything.
## Files in this skill
- `assets/template.html` — the atlas renderer (title and top-strip stats injected at build) - `assets/build.mjs` — `data.mjs` → `atlas.html` + `SYSTEM.md` - `assets/data.example.mjs` — a minimal starter with every field documented; copy to `data.mjs` - `references/design-language.md` — layout, palette, isometric grammar, shapes by role, labels, copy rules, the chapter recipe - `references/process-and-lessons.md` — the first session step by step, the README table, the subagent deep-dive pattern, cost-model habits, things that bit
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for system-atlas, ready for a manual X post.
system-atlas: Build and maintain an explorable, progressively-disclosed isometric "atlas" of a system's arc... 392 stars https://www.openagentskill.com/skills/inkboard-system-atlas?ref=x
Listing + install path for system-atlas: https://www.openagentskill.com/skills/inkboard-system-atlas?ref=x Install: npx skills add inkboard/system-atlas --skill system-atlas
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filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness