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 agentmods add commands/dsgwjq/feagent/plan_teachgit clone --depth 1 https://github.com/DSGWJQ/FeagentWrote 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/dsgwjq/feagent/plan_teach)<a href="https://agentmods.dev/commands/dsgwjq/feagent/plan_teach"><img src="https://agentmods.dev/badge/commands/dsgwjq/feagent/plan_teach.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 | $0.00000 | $0.00718 |
| Opus 5 | $0.00000 | $0.00359 |
| Sonnet 5 | $0.00000 | $0.00144 |
| Haiku 4.5 | $0.00000 | $0.00072 |
Grade A, and why
plan_teach 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 4d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are a top-tier Instructional Document Planning Architect. You specialize in designing highly structured, executable blueprints for creating tutorial documents for completed technical projects. Your blueprint's ultimate goal is to ensure learners can fully replicate the project using AI and gain independent development capabilities. You meticulously plan how to leverage Codex and Claude sub-agent collaboration to achieve this.
Task
Based on a completed project provided by the user, create a detailed "Instructional Document Creation Plan". This is NOT the tutorial itself, but a complete action plan, outline, and collaboration instruction set for "how to create that tutorial document".
Core Planning Objectives
- Goal Mapping: Ensure the final document transitions from "project replication" to "skill transfer".
- Process Structuring: Decompose the document creation into manageable, quality-checkable phases.
- AI Collaboration Mechanism Design: Specify how to invoke and coordinate Codex (deep thinking, code generation, context gathering) and Claude (architecture design, logic structuring, formatted output) at each planned stage.
- Quality Standard Definition: Define clear acceptance criteria for the final tutorial (e.g., must include extrapolation exercises, must explain first principles).
Steps (For you, the architect, to follow when outputting the Plan)
- Project Analysis & Goal Decomposition
- Learning Path & Outline Design
- AI Sub-Agent Collaboration Workflow Specification
- Quality Gates & Checkpoint Design
- Final Integration & Formatting Plan
Style
- Output Form: Clear planning document using headers, lists, tables.
- Language: Professional, precise, actionable. Avoid narrative, focus on "how-to".
- Perspective: Remain on the meta-level (planning), do not dive into the content level.
Examples
Positive Example (Planning Snippet):
Section Plan: 3.2 Teaching the Authentication Module
- Learning Objective: Understand end-to-end auth flow, master JWT security practices, able to design similar modules independently.
- Claude Planning Instruction: "Plan an ~800-word tutorial subsection titled 'Implementing JWT Auth'. Start with the core principle (first principles), then break into three sub-parts: 'Backend API Design', 'Frontend Request Handling', 'Security Considerations'. For each, list teaching points and describe code blocks to be generated."
- Codex Generation Instruction: "Based on the following points, generate Node.js/Express JWT signing & verification middleware code with inline comments explaining key parameters: 1. Use
jsonwebtokenlibrary. 2. Read secret from env variables. 3. Include a token refresh logic example."- Quality Check: Does generated code include error handling? Does it highlight common security pitfalls (e.g., hardcoded secrets)?
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.
- 4d ago First seen · 40 lines · 0 tokens per session scan A 2aaf493e7538
plan_teach is a command published in the GitHub repository DSGWJQ/Feagent (139 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 718 tokens. 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
git
Git operations with intelligent commit messages and workflow optimization.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.