Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
The English summary is being prepared. Showing the original description.
Experimental workflow for auditing recurring feedback, telemetry, errors, and brittle delivery paths to find systemic fixes. Use for periodic or requested cross-source lookbacks, not routine single-item intake.
The English summary is being prepared. Showing the original description.
Review the instructions the agent will receive before you install.
Read .agent-factory/config.yaml and apply the optional
skill_prompts.factory-lookback entry as additional project guidance. Use this
workflow when asked to look back across a bounded period or when an enabled
workflows.lookback automation runs. Its purpose is to find patterns that
normal item-by-item flows keep missing or fixing only temporarily.
For each recurring cluster, trace representative reports to their original source and inspect prior dispositions, commits, tests, and release evidence. Check whether the symptom recurred after a claimed fix, appeared through a sibling caller, or escaped because the normal workflow lacked a signal, owner, verification step, or recovery path.
For a report previously held for more information, inspect the full source thread for new replies. Re-triage the original report with the new details and check whether the normal fix flow now has enough evidence to proceed. Keep the item open when the answer is still incomplete; do not treat a reply as a fix or as permission for another action.
Reproduce a representative current case when possible. Trace related callers and surfaces to the shared boundary that can explain the evidence. Prefer one verified correction at that boundary over a pile of caller-specific patches. Separate confirmed causes from hypotheses, and state what evidence would disprove each proposed explanation.
Recommend only by default. Prepare a fix locally only when workflows.lookback.implement allows it and the user asked for it in this conversation; publishing needs the user's GO. A recurring pattern is evidence for
investigation, not automatic permission to edit code or take an external
action. Keep reply, issue closure, PR approval, merge, deployment, and
notification under their own configured policies.
When implementation is allowed, make the smallest systemic fix that addresses the confirmed cause. Add or update a regression check at the boundary, exercise the representative failure and relevant sibling paths, and inspect the same signals again after the fix. A test, merge, or “fixed” label alone does not prove that the live recurrence stopped.
For each pattern, report:
Do not treat unavailable telemetry or an incomplete history as evidence that a pattern does not exist.
.agent-factory/config.yaml. If it is missing, stop and
ask the user; never create it or guess sources.git push, gh pr merge,
gh release create and every external write (reply, comment, approval, close,
status change, notification) need the user's literal GO for that exact action.
Prepare the change or draft text, show it, and stop.No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
The English summary is being prepared. Showing the original description.
Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.