Borrowing it
Nothing to install: this file belongs to lowtidebuild/ebook-writer. 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/lowtidebuild/ebook-writer/main/.claude/commands/resume.mdgit clone --depth 1 https://github.com/lowtidebuild/ebook-writerWrote 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/lowtidebuild/ebook-writer/resume)<a href="https://agentmods.dev/commands/lowtidebuild/ebook-writer/resume"><img src="https://agentmods.dev/badge/commands/lowtidebuild/ebook-writer/resume.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 | $0.00000 | $0.00317 |
| Opus 5 | $0.00000 | $0.00159 |
| Sonnet 5 | $0.00000 | $0.00063 |
| Haiku 4.5 | $0.00000 | $0.00032 |
Grade A, and why
resume 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 7d 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
Resume Pipeline
Resume an interrupted or paused ebook generation pipeline.
Usage
/resume
Execution
-
Check for existing state:
- If
output/pipeline_state.jsondoes not exist:- "파이프라인 상태 파일이 없습니다.
/generate명령으로 새 파이프라인을 시작해주세요." - Exit
- "파이프라인 상태 파일이 없습니다.
- If
-
Read and validate state:
- Read
output/pipeline_state.json - If
schema_versionis missing or lower than 4, run.venv/bin/python3 scripts/pipeline_state.py migrate output/pipeline_state.json - Run
.venv/bin/python3 scripts/pipeline_state.py validate output/pipeline_state.json - Display current status:
- Topic: {topic}
- Plugin: {plugin or "없음"}
- Last completed step: {last_completed_step or "없음"}
- Started: {started_at}
- Last updated: {updated_at}
- Read
-
Validate artifacts:
- For each completed step, verify the output artifact exists on disk
- If any artifact is missing, reset that step and all subsequent steps to "pending"
- Report any reset steps to the user
-
Resume execution:
- Identify the next pending step
- Follow the Step Execution Protocol in CLAUDE.md from that step onward
- If currently at a gate (Gate 1 or Gate 2), re-present the deliverables for review
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.
- 7d ago First seen · 37 lines · 0 tokens per session scan A b4da5230e373
resume is a command published in the GitHub repository lowtidebuild/ebook-writer (5 stars, last pushed 2mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 317 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 2026-08-31.
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.