Autowiki chunk
Use when splitting a document into processable chunks. Trigger: 'chunk this file', 'split document for ingestion', 'what chunking strategy for this file'.From its SKILL.md
npx -y skills add knefenk/autowiki --skill autowiki-chunkAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its file declares
Copied from the file, not written here
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
2.7 KB, 585 tokens by cl100k_base, as published. Nobody here has run it
AutoWiki Chunk
Split a document into chunks the agent can process without context overflow. Pick strategy by file type, then read one chunk at a time.
Strategy selection
| Extension | Strategy | How to split |
|---|---|---|
.md, .txt | prose | Split at ## or # headers. Fallback: paragraph boundaries (double newline). Target 1K-3K tokens per chunk. |
.log, .jsonl | log | Three sections: head (first 50 lines for format), middle (100 lines from center for patterns), tail (last 100 lines for recency). |
.csv, .tsv | data | Schema (header row) + sample (20 evenly-spaced rows). |
.py, .js, .go | code | Split at def , class , async def . Keep imports and file-level config with first chunk. |
Prose chunking
Read the file. Find section headers (## Title or # Title). Split so each
chunk is 1K-3K tokens. If no headers, split at paragraph boundaries (blank
lines). Avoid splitting mid-paragraph.
To read a specific chunk without loading the full file:
read_file <file> offset=<N> limit=<M>
Skip boilerplate: license headers, table of contents, navigation blocks.
Log chunking
Read first 50 lines for format identification (head). Sample 100 lines from the middle third of the file (patterns). Read last 100 lines (recent events). Skip the rest.
For JSONL: the head section surfaces the schema. Middle sections find error signatures and outliers. Tail shows most recent events.
Data chunking
Read the header row for schema. Sample 20 rows evenly distributed through
the file. step = max(1, total_rows // 20). Skip the rest.
Schema + 20 rows is ~500 tokens. Full 10K-row CSV is ~100K tokens. Same extraction quality for structural understanding.
Code chunking
Split at top-level function and class boundaries. import statements,
file-level constants, and docstrings stay with the first chunk.
Implementation-only functions (getters, I/O helpers) that contain no novel logic or concepts can be summarized in one line rather than processed in full. Focus extraction on: novel algorithms, data structures, configuration that defines system behavior.
Never
- Load the full file into context — use
offsetandlimit - Process the same chunk twice without changing strategy
- Count implementation-only code blocks as "stale" — they're expected
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.