Borrowing it
Nothing to install: this file belongs to AsiaOstrich/EngramGraph. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AsiaOstrich/EngramGraph/main/.agents/skills/plan/SKILL.mdgit clone --depth 1 https://github.com/AsiaOstrich/EngramGraphWrote 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/asiaostrich/engramgraph/plan)<a href="https://agentmods.dev/skills/asiaostrich/engramgraph/plan"><img src="https://agentmods.dev/badge/skills/asiaostrich/engramgraph/plan/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/asiaostrich/engramgraph/plan"><img src="https://agentmods.dev/badge/skills/asiaostrich/engramgraph/plan.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 | $0.00074 | $0.02397 |
| Opus 5.5 | $0.00030 | $0.00959 |
| Sonnet 5.5 | $0.00015 | $0.00479 |
| Haiku 4.5 | $0.00007 | $0.00240 |
Grade C, and why
plan scanned grade C with 1 finding 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 14d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
3. **安全掃描**:task.spec 不含危險指令(`rm -rf /`、`DROP DATABASE`、`git push --force`) How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/plan — 從 Spec 自動生成 plan.json
用法
/plan <spec-file.md> # Spec 模式(自有格式 / SpecKit)
/plan <openspec-change-dir/> # OpenSpec 模式
/plan "需求描述文字" # 需求模式
/plan # 互動模式(自動偵測)
執行流程
Step 1: 輸入辨識與格式偵測
根據引數類型判斷使用模式:
引數是 .md 檔案:
- 讀取檔案內容
- 偵測格式:
- 含
## Requirements且## Technical Design→ 自有格式(SPEC-NNN-*.md) - 含
## Summary且## Detailed Design→ SpecKit 格式 - 其他 .md → 嘗試解析為通用 Spec(按自有格式處理,缺少的欄位由 AI 補充)
- 含
引數是目錄:
- 檢查是否含
proposal.md+tasks.md→ OpenSpec 格式 - 否則報錯:「此目錄不符合 OpenSpec 結構」
引數是字串描述(非檔案路徑): → 需求模式
無引數: → 互動模式
- 偵測
openspec/目錄存在? → 列出 changes 讓使用者選擇 - 偵測
specs/*.md存在? → 列出可用 Spec 讓使用者選擇 - 都沒有 → 進入需求模式問答
Step 2: 專案 Context 收集
讀取目標專案的關鍵資訊:
- CLAUDE.md / AGENTS.md — 語言、框架、測試工具、開發規範
- package.json / pyproject.toml — 可用 scripts(build, test, lint)
從 Context 推斷:
- 主要語言(TypeScript / Python / 其他)
- 預設 verify_command(如
pnpm build && pnpm test、pytest -x) - 預設 lint_command(如
pnpm lint、ruff check .)
Step 3: Spec 解析
依偵測到的格式,解析 Spec 內容:
自有格式(SPEC-NNN-*.md)
提取以下區段:
- Summary / Motivation → 理解背景與需求動機
- Requirements (REQ-NNN) → 功能需求列表
- Acceptance Criteria (AC-N) → Given/When/Then 驗收條件
- Technical Design → Phase 分層、每個 Phase 的實作項目
- Test Plan → 測試清單
- Risks → 風險評估
OpenSpec(change 目錄)
讀取以下檔案:
proposal.md→ Why(動機)、What Changes(變更項目)、Impacttasks.md→- [ ]checklist 項目design.md(若存在)→ 技術決策specs/*/spec.md(若存在)→ Requirements(SHALL/MUST 語句)、Scenarios
SpecKit(SPEC-ID.md)
提取以下區段:
- Summary / Motivation → 背景
- Detailed Design → 技術實作步驟
- Acceptance Criteria → checkbox 驗收條件
- Dependencies → 外部依賴
- Risks → 風險
Step 4: Task 切分
依格式套用不同映射規則:
自有格式映射規則
| Spec 區段 | plan.json 欄位 | 映射規則 |
|---|---|---|
| Technical Design > Phase | 一組 tasks | 每個 Phase 的每個實作項目 = 1 個 task |
| Phase 實作項目描述 | task.spec |
合併:項目描述 + 相關 REQ + 技術細節 |
| Acceptance Criteria (AC-N) | task.acceptance_criteria |
分配到最相關的 task |
| Summary / Motivation | task.user_intent |
萃取與 task 最相關的意圖 |
| Test Plan | task.verify_command |
推斷測試指令 |
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.
- 14d ago First seen · 236 lines · 74 tokens per session scan C 14359e142649
plan is a skill published in the GitHub repository AsiaOstrich/EngramGraph (3 stars, last pushed today), licensed MIT. It adds 74 tokens to every session and 2,397 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-16.
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