cognee: Skill for Claude Code

Ingest data with remember()

remember() is cognee's ingestion API. One call stores the data, builds the knowledge graph, and enriches it. Use it for all ingestion; every option in this skill is a remember() argument unless it says otherwise.

import cognee

result = await cognee.remember("Einstein was born in Ulm.")  # text
result = await cognee.remember(
    ["./notes.md", "./report.pdf"],  # files
    dataset_name="research",
)
print(result.status, result.dataset_id)  # "completed", UUID

All cognee functions are async. Without dataset_name data goes to main_dataset. Needs LLM_API_KEY unless you use the GLiNER extractor (below).

Use it

Inputs

data accepts a string, a list of strings, file paths (absolute, file://, s3://), http(s) URLs, binary streams, or a list mixing them.

• URLs are fetched and scraped (needs ALLOW_HTTP_REQUESTS=true, the default).
• Folders are ingested file by file. A folder that looks like a code project, or a GitHub/GitLab URL, becomes one code repository (needs git on PATH).
• Code files (.py, .ts, .go, …) go down the code-graph route: a deterministic graph, no LLM calls, searchable only with SearchType.CODE. To index a whole repository explicitly, pass content_type="code".
• Databases and dlt sources: a SQL connection string, a dlt DltResource / DltSource, or a CSV. dlt is a core dependency, so no extra is needed (cognee[dlt] is an empty compatibility extra). Options: primary_key (default "id"), write_disposition ("replace" default, or "append"), query, max_rows_per_table.
• Skill playbooks (SKILL.md files): content_type="skills"; ingests into the target dataset (default main_dataset), so pass dataset_name to keep skills in their own dataset.

Where the data goes