Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Check invoices and receipts line by line against your offers and list what matches, what differs and what has no offer. Nothing is paid or sent. Use for "check my invoices", "match invoices to offers", "does this invoice match the quote".
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
What you get: invoices and receipts sorted and checked line by line against the matching offers.
offer.md files from pb-offer./pb-invoice-check-<date>/run-log.md; list the PDFs. pdftotext is not installed, so you read each PDF with the harness PDF read; a PDF it cannot read becomes a row with status unread and the run continues — you may paste its text laterinvoices.csv, columns file, vendor, date, number, net, VAT, total, status — a field that is not on the invoice is ?; every PDF in the folder has exactly one rowinvoices.csv; no offer found → status no offercheck.md: per invoice match / mismatch (which line, how much it differs) / no offer / unread — every total recomputed from its lines; the recomputed value and the printed value are both showninvoices.csv and check.md in full and apply your edits until you approve — approval loggedNothing is paid or sent, so there is no GO step. The run only reads the two folders and writes into the save folder.
Run log: run-log.md in the save folder. One line per step as it completes (N. done|skipped|failed — file — check result). If a check fails, stop, write the failure into the log and tell the user.
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Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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