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/midnightdarling/collate/ocrgit clone --depth 1 https://github.com/MidnightDarling/collateWhat 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 | $0.00015 | $0.00412 |
| Opus 5 | $0.00008 | $0.00206 |
| Sonnet 5 | $0.00003 | $0.00082 |
| Haiku 4.5 | $0.00002 | $0.00041 |
Grade A, and why
ocr 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.
What it actually says
This is the public user path.
Treat python3 scripts/run_full_pipeline.py --pdf <pdf> as an internal /
debug path unless it has been proven equivalent by the same fresh-agent
real-PDF gate.
The user handed over a scanned PDF and wants the full pipeline to produce publishable output:
$ARGUMENTS
Use the Task tool to dispatch the ocr-pipeline-operator subagent. Pass the PDF path as-is with a prompt that instructs:
- Input PDF is the path above.
- Follow the Canonical Workflow in
agents/ocr-pipeline-operator.md:- Run
python3 scripts/run_full_pipeline.py --pdf "<pdf>"for the mechanical stages (prep → OCR). - Read
<workspace>/_internal/_pipeline_status.json. Ifstatus=awaiting_agent_review, buildreview/page_review_packets.json, classify the document type (classics/republican/modern), invokehistorical-proofreaderwithprep/pages/*.pngplus the packet file, and verify that<workspace>/review/raw.review.mdis mechanically page-grounded before continuing. - Re-enter
python3 scripts/run_full_pipeline.py --workspace "<workspace>"to chain apply-review, diff-review, to-docx, mp-format. - Verify
final.md,previews/diff-review.html,review/diff-summary.md,output/*_final.docx,output/*_wechat.htmlall exist.
- Run
- On success, report using the Human-Facing Delivery Message template; on failure, return the structured Failure Contract.
Relay the agent's delivery message verbatim to the human — no second layer of summary. The three things they care about: deliverable paths, audit summary, residual risks.
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.
- 2d ago First seen · 27 lines · 15 tokens per session scan A 46ecdbccb583
ocr is a command published in the GitHub repository MidnightDarling/collate (6 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 15 tokens to every session and 412 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 2026-08-31.
Other commands, from other repositories
t800-start
Единая команда. Создание rules, skills, commands, subagents, hooks — для плагина, текущего проекта или глобально.
t800-fix
Зачем: узкая правка существующих agents / skills / commands / rules / hooks по pack — без полного DEEP /t800-start.
t800-onboard
Для чата с новичками. Показывает, что настроено в Cursor (global + local), и что умеет отдел T-800.
t800-bootstrap
Запускайте один раз при установке плагина или для новичка в чате.
t-800-factory
Обязательно вызови субагента через Task(t-800-factory).
t-800-factory-validate
Запусти проверки и при необходимости вызови аудитора.