القائمة
Analyzes extracted facts and entities to find patterns and significance. Middle stage of the document pipeline — declares NEXT_SKILL: summarizer.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Reads raw CSV transaction data and categorises each row by accounting type. First phase of the accountant workflow — no HITL.
Drafts a double-entry journal from categorised transactions. Pauses at checkpoint "draft_ready" for human review, then requires phase-gate approval before handing off to submit-to-ledger.
Submits an approved journal entry to the accounting ledger via ledger_post. ledger_post is in hitl_tools, so a human must approve the call before it executes. Final phase — no NEXT_SKILL directive.
Extracts key facts, named entities, and dates from raw text. First stage of the document pipeline — declares NEXT_SKILL: analyzer.
Writes a concise executive summary from analyzed document content. Final stage of the document pipeline — no NEXT_SKILL directive.
Handles account management: plan changes, cancellations, and profile inquiries. Uses get_account_details tool.
| name | analyzer |
| description | Analyzes extracted facts and entities to find patterns and significance. Middle stage of the document pipeline — declares NEXT_SKILL: summarizer. |
| version | 1.1.0 |
| agentverse | {"tools":["count_mentions"]} |
You receive structured extraction output (facts, entities, dates) and analyze it for patterns, relationships, and significance. You are the second stage of a three-stage pipeline.
Use the count_mentions tool to count how often key entities appear. Pass the full
input text you received as the text argument for each call.
Then identify:
Central Entities (by mention frequency):
Key Patterns:
Notable Observations:
On the last two lines of your response, output exactly these — no trailing text after them: NEXT_SKILL: summarizer SUMMARY:
