knowledge-summarize

Research

Generate a TL;DR summary of a specific document or learning unit in the Knowledge base. Pulls chunks from pgvector and synthesizes via Claude Haiku. Use when the user wants a quick overview ('summary of lesson 5', 'TL;DR of this PDF', 'explain document X in one paragraph').

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Source SKILL.md: https://github.com/evolution-foundation/evo-nexus/blob/HEAD/.claude/skills/knowledge-summarize/SKILL.md

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knowledge-summarize

Group: Consumption. Generate TL;DR of a document or unit using indexed chunks.

When to trigger

"Summary of lesson 5"
"TL;DR of this PDF"
"Explain document X"
"Summary of module Y"

Arguments

Name	Type	Required	Description
`document_id`	str	one of two	Document UUID
`unit_id`	str	one of two	Unit UUID (aggregates all docs)
`connection`	str	no	Defaults to first ready
`max_tokens`	int	no	Limit (default 500)

Workflow

Step 1 — Fetch chunks

from dashboard.backend.sdk_client import evo

if document_id:
    doc = evo.get(f"/api/knowledge/v1/documents/{document_id}",
                  headers={"X-Knowledge-Connection": connection})
    chunks = doc["chunks"]
    title = doc["title"]
elif unit_id:
    docs = evo.get(f"/api/knowledge/v1/documents?unit_id={unit_id}",
                   headers={"X-Knowledge-Connection": connection})
    chunks = []
    for d in docs:
        full = evo.get(f"/api/knowledge/v1/documents/{d['id']}",
                       headers={"X-Knowledge-Connection": connection})
        chunks.extend(full["chunks"])
    title = f"Unit {unit_id} ({len(docs)} documents)"

Step 2 — Concatenate + truncate

Concatenate chunk.content separated by \n\n. If total > 40k chars: sample first/middle/last third.

Step 3 — LLM call

Model: claude-haiku-4-5-20251001.

Prompt:

Summarize the document in structured markdown. Max {max_tokens} tokens.

## {title}

**TL;DR (1 paragraph):** ...

**Key points:**
- ...
- ...

**Target audience / when to use:** (optional)

### Document
{concatenated_chunks}

Step 4 — Render

Return summary + footer Based on {N} chunks from {M} documents.

Actionable failures

Neither document_id nor unit_id passed → "Pass one of the two (mutually exclusive)."
Not found → "Not found. Use knowledge-browse to list."
ANTHROPIC_API_KEY missing → "Set ANTHROPIC_API_KEY in .env."
Doc status != ready → "Not indexed (status={status}). Re-upload the document or wait for ingestion to complete."