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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.
Passed 21 of 23 test cases with the skill, and 3 of 23 without it.
and 22 more in the full report
18 fixed3 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 18 test cases the skill fixed. Same model, same prompt, with and without the skill.
Summarize document doc-777-report when ANTHROPIC_API_KEY environment variable is not set.
| Checked | Without skill | With skill |
|---|---|---|
| States that ANTHROPIC_API_KEY must be set in .env. | Missed | Passed |
The instructions your model reads when the skill loads.
Group: Consumption. Generate TL;DR of a document or unit using indexed chunks.
| 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) |
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"])
test cases passed, vs 3 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.