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tidy
// Triggered by "tidy up", "clean up transactions", "categorize uncategorized", "organize my transactions"
// Triggered by "tidy up", "clean up transactions", "categorize uncategorized", "organize my transactions"
Social media scheduling and publishing for AI agents. Use when the user wants to schedule posts, connect social accounts, upload media, or publish campaigns to X, LinkedIn, Instagram, Facebook Pages, TikTok, Discord, Telegram, YouTube, Reddit, WordPress, or Pinterest through SocialClaw.
127 production skills across 10 categories (Security, Deployment, Dev, Business, Content, SEO, Marketing, Product, Automation, Core). Skills auto-load based on your task. Includes localhost dashboard with Agent Runner, Burn Report, and session diary.
Session-start briefing from Origin. Reads the project status file (the /handoff-maintained ledger of Active/Backlog work), then loads identity, preferences, and topic-relevant memories so the agent walks in with context. Surfaces any memories the daemon has flagged for human revision before the session uses them. Invoked as `/brief [topic]`. Call FIRST at session start, before any other Origin verb.
Save a memory to Origin in flow. Active capture verb — use proactively when the user states a preference, makes a decision, corrects you, or shares a durable fact. Invoked as `/capture <content>`.
Alias for `/origin:handoff` — symmetric brief/debrief naming. Same behavior: end-of-session ritual that writes session log + project status + granular MCP captures. Invoked as `/debrief`. Use when the user prefers the brief/debrief pair over brief/handoff.
Synthesize wiki pages from related memories. One endpoint, one flow: daemon clusters and synthesizes what it can; agent finishes whatever the daemon couldn't (no LLM or cluster too big). Invoked as `/distill [target]`.
| name | tidy |
| description | Triggered by "tidy up", "clean up transactions", "categorize uncategorized", "organize my transactions" |
Batch-categorize uncategorized transactions by clustering similar ones and applying categories in bulk.
Fetch uncategorized transactions. Call the query MCP tool:
{ "detail": true, "is_uncategorized": true, "period": "last_90d", "limit": 200, "sort": "-amount" }
If $ARGUMENTS contains a time period (e.g. "this month", "last 30 days"), use that instead of last_90d.
Research unknown transactions. For transactions you can't identify from the description alone:
Cluster by pattern. Group the results by normalized description or party name. For each cluster, note the count and total amount.
Suggest categorization. For each cluster, propose:
Present to the user. Show a table or list of clusters with:
Ask the user to approve, modify, or skip each cluster.
Prefer rules over one-off annotations. If a cluster has more than one transaction, or the merchant is likely to appear again (subscriptions, regular stores, utilities, etc.), create a rule rather than annotating individual transactions. Rules automatically categorize future transactions too.
admin { "entity": "rule", "action": "preview", ... }admin { "entity": "rule", "action": "create", ... }Annotate the rest. For truly one-off transactions where a rule wouldn't help, apply directly:
{ "action": "categorize", "filter": { "search": "<pattern>" }, "category_name": "<approved_category>" }
Also set the party if one was approved:
{ "action": "set_party", "filter": { "search": "<pattern>" }, "party_name": "<approved_party>" }
Summarize. Report how many transactions were categorized, how many rules were created, and how many uncategorized transactions remain.
Stick to the facts. Present findings and suggestions without judgement — no commentary on spending habits. Just clear, plain-language observations and actionable options.