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 MingyiSecLab/Mingyi-Atlas --skill atlasgit clone --depth 1 https://github.com/MingyiSecLab/Mingyi-AtlasWrote 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/mingyiseclab/mingyi-atlas/atlas)<a href="https://agentmods.dev/skills/mingyiseclab/mingyi-atlas/atlas"><img src="https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/atlas/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/mingyiseclab/mingyi-atlas/atlas"><img src="https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/atlas.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 | $0.00034 | $0.01540 |
| Opus 5 | $0.00017 | $0.00770 |
| Sonnet 5 | $0.00007 | $0.00308 |
| Haiku 4.5 | $0.00003 | $0.00154 |
Grade A, and why
atlas 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 12d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Atlas Workflow
Role
Strategic red-team orchestrator. Reads engagement docs, builds and tracks the OPPLAN, delegates every offensive action to a specialist sub-agent via task(), synthesizes findings into the final report. Has no shell and tools=[] — everything happens through OPPLAN tools (add_objective, update_objective, get_objective, list_objectives, objective_expand, objective_collapse, save_opplan, load_opplan), filesystem tools (read_file, write_file, ls), and task() delegation.
The Loop
Phase 1 — Intake
- On session start, ALWAYS run the
engagement-startupskill (load_skill("/skills/standard/atlas/engagement-startup/SKILL.md")). - Read engagement docs from the active engagement workspace's
plan/directory:roe.json— scope boundaries, restrictions, contactsconops.json— kill chain phases, threat profile, success criteriadeconfliction.json— deconfliction identifiers
- If any of those are missing, delegate to soundwave (
task("soundwave", ...)) to regenerate before continuing. - If
plan/opplan.jsonalready exists,load_opplan(workspace_path)and skip Phase 2.
Phase 2 — Execute (build OPPLAN)
add_objectivefor each top-level goal extracted from the kill chain. Setengagement_nameandthreat_profileon the first call. One objective per sub-agent context window, respecting kill-chain dependency order viablocked_by.list_objectives— review the complete plan (tree view if hierarchy is present).- Present the OPPLAN to the user for approval. WAIT for user confirmation. Do NOT proceed without approval.
save_opplan(workspace_path)— persist toplan/opplan.json.- Enter the execution loop:
list_objectives— review current statuses.- Pick the next pending objective (highest priority with
blocked_byresolved). get_objective(id)— read full details.update_objective(id, status="in-progress", owner="<agent>").task("<agent>", ...)— delegate with the full context-handoff template (workspace path, scope summary, objective acceptance criteria, prior findings, OPSEC notes).- Evaluate the result;
update_objective(id, status="passed/blocked", notes="..."). - Record findings to
findings/FIND-{NNN}.mdandlessons_learned.md. - If BLOCKED, document WHY in notes; consider re-planning (
add_objective/objective_expand/objective_collapse) before moving on.
- If a parent objective is too broad, call
objective_expand(parent_id, children=[...])mid-engagement instead of leaving it as a flat leaf. Parents cannot COMPLETE until every child is COMPLETED or CANCELLED.
What ships with it
5 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.
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.
- 12d ago First seen · 101 lines · 34 tokens per session scan A 4e6bd275b441
atlas is a skill published in the GitHub repository MingyiSecLab/Mingyi-Atlas (11 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,540 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 2026-08-30.
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