本文へ移動
cccskills
無料GitHub で公開

tidy

Triggered by "tidy up", "clean up transactions", "categorize uncategorized", "organize my transactions"

インストール方法を見る

含まれるファイル(1)

  • SKILL.md2.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Tidy Up Uncategorized Transactions

Batch-categorize uncategorized transactions by clustering similar ones and applying categories in bulk.

Workflow

  1. 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.

  2. Research unknown transactions. For transactions you can't identify from the description alone:

    • Web search first (if available): Search for the merchant name, any phone numbers or domains in the description, or the raw description itself. This often reveals the business behind cryptic processor names.
    • Search the user's email (if available): Search for the party/merchant name to find order confirmations or receipts. If that doesn't match, search for the exact dollar amount (e.g. "$47.23") to find receipts that way.
  3. Cluster by pattern. Group the results by normalized description or party name. For each cluster, note the count and total amount.

  4. Suggest categorization. For each cluster, propose:

    • A category (pick from the user's existing categories)
    • A party name (the clean merchant/counterparty name)
  5. Present to the user. Show a table or list of clusters with:

    • Pattern / merchant name
    • Count of transactions
    • Total amount
    • Suggested category
    • Whether you recommend creating a rule

    Ask the user to approve, modify, or skip each cluster.

  6. 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.

    • Preview first: admin { "entity": "rule", "action": "preview", ... }
    • Show the preview (how many existing transactions would match)
    • If user confirms, create: admin { "entity": "rule", "action": "create", ... }
  7. 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>" }
    
  8. Summarize. Report how many transactions were categorized, how many rules were created, and how many uncategorized transactions remain.

Tone

Stick to the facts. Present findings and suggestions without judgement — no commentary on spending habits. Just clear, plain-language observations and actionable options.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

davepoon/buildwithclaude3,6192026年10月10日 更新

Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.

日本語の概要は準備中です。原文の説明を表示しています。

davepoon/buildwithclaude3,6192026年10月10日 更新

Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.

日本語の概要は準備中です。原文の説明を表示しています。

davepoon/buildwithclaude3,6192026年10月10日 更新

Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.

日本語の概要は準備中です。原文の説明を表示しています。

davepoon/buildwithclaude3,6192026年10月10日 更新

Audit whether a website can be found, crawled, and cited by AI answer engines such as ChatGPT Search, Perplexity, Google AI Overviews, and Microsoft Copilot. Use when someone asks why their brand is missing from AI answers, whether AI crawlers can read their site, how to get cited by ChatGPT or Perplexity, or asks for a GEO or AEO (generative / answer engine optimization) review. Produces a citation baseline across buyer-intent prompts, a crawler-access check, a citability review of named pages, and a ranked fix list. Not for keyword rank tracking, paid search, or pages behind a login.

日本語の概要は準備中です。原文の説明を表示しています。

davepoon/buildwithclaude3,6192026年10月10日 更新

Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

davepoon/buildwithclaude3,6192026年10月10日 更新

davepoon のスキルをすべて見る

このスキルの問題を報告する