atlas

atlas is a skill for Claude Code, Codex from MingyiSecLab/Mingyi-Atlas. It costs 34 tokens per session (1,540 once invoked), scanned A, original, Apache-2.0.

An orchestrated security-engagement workflow that collects planning documents, builds an operation plan, delegates tasks to specialist agents, and combines their findings. It is designed for authorized offensive-security work within defined scope and restrictions.

In plain words
What is it for?
Starting an engagement, reading rules and threat plans, creating or loading an operation plan, assigning specialist tasks, tracking objectives, and compiling results.
Why use it?
It gives a security assessment a documented plan, clear objectives, and delegated execution steps. It also helps preserve scope boundaries and produce a final report.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Starting an engagement, reading rules and threat plans, creating or loading an operation plan, assigning specialist tasks, tracking objectives, and compiling results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mingyiseclab/mingyi-atlas/atlas
Install

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.

Any agent
npx skills add MingyiSecLab/Mingyi-Atlas --skill atlas
Clone the repo
git clone --depth 1 https://github.com/MingyiSecLab/Mingyi-Atlas

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for atlas

README.md
[![agentmods](https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/atlas/github.svg)](https://agentmods.dev/skills/mingyiseclab/mingyi-atlas/atlas)
Your own site
<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.

agentmods 80×15 button for atlas

Your own site · 80×15
<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>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,540 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash 4e6bd275b441, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

src/skills/standard/atlas/SKILL.md · 101 lines

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

  1. On session start, ALWAYS run the engagement-startup skill (load_skill("/skills/standard/atlas/engagement-startup/SKILL.md")).
  2. Read engagement docs from the active engagement workspace's plan/ directory:
    • roe.json — scope boundaries, restrictions, contacts
    • conops.json — kill chain phases, threat profile, success criteria
    • deconfliction.json — deconfliction identifiers
  3. If any of those are missing, delegate to soundwave (task("soundwave", ...)) to regenerate before continuing.
  4. If plan/opplan.json already exists, load_opplan(workspace_path) and skip Phase 2.

Phase 2 — Execute (build OPPLAN)

  1. add_objective for each top-level goal extracted from the kill chain. Set engagement_name and threat_profile on the first call. One objective per sub-agent context window, respecting kill-chain dependency order via blocked_by.
  2. list_objectives — review the complete plan (tree view if hierarchy is present).
  3. Present the OPPLAN to the user for approval. WAIT for user confirmation. Do NOT proceed without approval.
  4. save_opplan(workspace_path) — persist to plan/opplan.json.
  5. Enter the execution loop:
    1. list_objectives — review current statuses.
    2. Pick the next pending objective (highest priority with blocked_by resolved).
    3. get_objective(id) — read full details.
    4. update_objective(id, status="in-progress", owner="<agent>").
    5. task("<agent>", ...) — delegate with the full context-handoff template (workspace path, scope summary, objective acceptance criteria, prior findings, OPSEC notes).
    6. Evaluate the result; update_objective(id, status="passed/blocked", notes="...").
    7. Record findings to findings/FIND-{NNN}.md and lessons_learned.md.
    8. If BLOCKED, document WHY in notes; consider re-planning (add_objective/objective_expand/objective_collapse) before moving on.
  6. 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.

Read the full file on GitHub · 101 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 101 lines · 34 tokens per session scan A 4e6bd275b441

Subscribe to this mod's changes

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.