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.
git clone --depth 1 https://github.com/LigphiDonk/Oh-my--paperWrote 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/commands/ligphidonk/oh-my--paper/idea-forge)<a href="https://agentmods.dev/commands/ligphidonk/oh-my--paper/idea-forge"><img src="https://agentmods.dev/badge/commands/ligphidonk/oh-my--paper/idea-forge.svg" alt="Measured on agentmods" 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 | $0.00017 | $0.00609 |
| Opus 5 | $0.00009 | $0.00304 |
| Sonnet 5 | $0.00003 | $0.00122 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
idea-forge 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 8d 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.
What it actually says
必须使用 AskUserQuestion 工具进行所有确认步骤，不得用纯文字替代。
你是 Oh My Paper Orchestrator。创新点的生成和最终选择都需要用户参与。
第一步：确认前置条件
cat .pipeline/docs/gap_matrix.md
cat .pipeline/memory/literature_bank.md | head -30
用 AskUserQuestion 展示当前文献基础：
已有 X 篇文献，发现以下研究空白：
- [空白 A]
- [空白 B]
- [空白 C]
准备基于这些空白生成 5 个创新方向。
选项：
确认，开始生成先看完整的 gap_matrix 再决定指定一个研究空白重点发展
第二步：生成创新点
/codex:rescue 阅读 .pipeline/docs/gap_matrix.md 和 .pipeline/memory/literature_bank.md，使用 .claude/skills/inno-idea-generation/SKILL.md 生成 5 个候选创新方向，写入 .pipeline/docs/idea_board.json
第三步：展示 5 个 idea，等用户筛选
读取 idea_board.json，用 AskUserQuestion 展示：
生成了以下 5 个创新方向：
- [Idea A]：...
- [Idea B]：... ...
接下来对这些方向做新颖性和可行性评估。
选项：
全部评估只评估我感兴趣的（告诉我哪几个）这些方向不对，重新生成
第四步：评估打分
/codex:rescue --resume 对 idea_board.json 中的 [选定的] idea 用 .claude/skills/inno-idea-eval/SKILL.md 打分（novelty/feasibility/impact 各 1-5 分），更新 scores 字段
第五步：你（Orchestrator）主导最终决策
展示评分结果，用 AskUserQuestion 询问：
评估结果：
- [Idea A]：新颖 4 / 可行 3 / 影响 5
- [Idea B]：新颖 5 / 可行 2 / 影响 4
- ...
你倾向于选哪个方向？
选项列出各 idea 名称，加一个「我来描述自己的想法」。
用户选定后，更新 project_truth.md，将其余方向记录到 decision_log.md。
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.
- 8d ago First seen · 72 lines · 17 tokens per session scan A c4d6d9092bf0
idea-forge is a command published in the GitHub repository LigphiDonk/Oh-my--paper (721 stars, last pushed 4mo ago), licensed MIT. It adds 17 tokens to every session and 609 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.