domain-plan

A planning tool for designing a complete set of coding-agent skills for a specific field, including how the skills and their outputs connect.

In plain words
What is it for?
Use it to describe a target field, derive the needed skills, map their workflow and outputs, and save planning documents for the build stage.
Why use it?
It gives a new domain a structured skill map and workflow before implementation begins, instead of adding isolated skills without an overall plan.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/fagemx/prismstack/domain-plan
Any agent
npx skills add fagemx/prismstack --skill domain-plan
Clone the repo
git clone --depth 1 https://github.com/fagemx/prismstack

Made for: Claude Code, Codex.

Per session 120 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,473 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00120 $0.05473
Opus 5 $0.00060 $0.02736
Sonnet 5 $0.00024 $0.01095
Haiku 4.5 $0.00012 $0.00547

Measured 2d ago against content hash 5a308a98491d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

domain-plan 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 2d 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.

skills/domain-plan/SKILL.md · 500 lines

How it starts

The opening of the file, as written. The whole thing — 500 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Domain Stack Architect

你是 domain stack 架構師。你規劃整套 skill 系統,不是寫單一 skill。 你的目標:從一個領域名稱,推導出完整的 skill map + workflow + artifact flow。


Auto Mode

如果被自動模式調用(orchestrator 傳入 --auto flag):

  • 跳過所有 STOP gates — 不問用戶確認
  • 跳過 AskUserQuestion — 自動做最佳決策
  • 仍然遵循所有方法論(skill-map-methodology)
  • 仍然存 artifact(skill-map-*.md + domain-config.json)
  • Phase 4(用戶確認)直接選 A(開始搭建)

互動模式的所有 Phase 不變。Auto mode 只是跳過停頓點。


中斷恢復

如果 skill 執行中斷(用戶取消、context 超限、錯誤):

  1. 偵測狀態: 搜尋 $_PROJECTS_DIR/skill-map-*.md — 如果存在,表示 Phase 5 已完成或先前有執行紀錄
  2. 恢復點:
    • 如果 skill-map-*.md 已存在 → 問用戶要修改現有還是重新開始
    • 如果對話中已有生命週期確認(Phase 1-2 完成)→ 跳到 Phase 3
    • 如果對話中已有用戶領域回答 → 跳到 Phase 1,不重問 Phase 0
  3. 不重做: 不重問用戶已回答的領域描述、不重新推導已確認的生命週期
  4. 通知用戶: 告知恢復狀態,確認繼續或重新開始

Phase 0: Domain Discovery

方法論(先讀再做)

  • Read {PRISM_DIR}/shared/methodology/skill-map-methodology.md — skill map 推導的完整方法(生命週期、缺口法、獨立性測試、分類、數量校準、brownfield mode)

{PRISM_DIR} = 找到的 Prismstack 安裝路徑(~/.claude/skills/prismstack 或 .claude/skills/prismstack)

0a. 先前執行偵測

# Search for prior skill maps from any domain
for f in ~/.prismstack/projects/*/skill-map-*.md; do
  [ -f "$f" ] && echo "FOUND: $f"
done

如果找到先前的 skill map → 告知用戶:

我找到先前的 skill map:{path}。你要: A. 基於這份 map 修改 B. 從零開始規劃新領域

如果用戶選 A → 讀取該 skill map,跳到 Phase 3(修改模式)。

0b. Brownfield 偵測

# 如果用戶指定了目標目錄,掃描現有 skill
_TARGET_DIR="${1:-.}"  # 用戶指定的目錄或當前目錄
_EXISTING_SKILLS=0
if [ -d "$_TARGET_DIR/skills" ]; then
  _EXISTING_SKILLS=$(find "$_TARGET_DIR/skills" -name "SKILL.md" -maxdepth 2 2>/dev/null | wc -l | tr -d ' ')
fi
# 也掃描自動化腳本
_HAS_SRC=0
[ -d "$_TARGET_DIR/src" ] && _HAS_SRC=1
_HAS_SCRIPTS=0
[ -d "$_TARGET_DIR/scripts" ] && _HAS_SCRIPTS=1

echo "EXISTING_SKILLS: $_EXISTING_SKILLS"
echo "HAS_SRC: $_HAS_SRC"
echo "HAS_SCRIPTS: $_HAS_SCRIPTS"

Brownfield 偵測信號(任一成立):

  • _EXISTING_SKILLS > 0
  • 用戶說「我有現有的 skill」「整合成 stack」「stack 化」「已經有一些 skill」
  • 用戶指向一個已有 skill 的 repo/目錄

如果偵測到 brownfield → 進入 Brownfield Path(見下方),跳過 0c 的領域問題。

Read the full file on GitHub · 500 lines

Files

What ships with it

4 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. 2d ago First seen · 500 lines · 120 tokens per session scan A 5a308a98491d

Subscribe to this mod's changes

domain-plan is a skill published in the GitHub repository fagemx/prismstack (2 stars, last pushed 4mo ago), licensed MIT. It adds 120 tokens to every session and 5,473 once invoked, about $0.0006 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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