donotreadagain 0.1.0

Read once, never again — self-describing files so AI agents stop re-parsing.

donotreadagain (dnr)

Read once, never again. Embed a faithful, signed AI transcript into each expensive-to-parse file's own metadata, so AI agents stop re-OCR/re-parsing the same PDF, image, scan, or audio every time.

· status: v0.1, pre-release

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The problem

AI agents re-parse the same file every time they touch it — re-OCR a scan, re-run vision on a screenshot, re-transcribe an audio clip, re-extract a PDF. It's slow, it burns tokens and model calls, and it's non-deterministic. In repeat-access corpora (legal, research, compliance) the same documents get read dozens of times; with multi-agent setups every agent re-parses independently. The waste compounds exactly where it hurts.

The idea

dnr reads a file once, then writes a verbatim transcript + structured metadata into the file's own native metadata slot as a signed JSON record — the file becomes self-describing. Any agent that opens it later reads the cached transcript instead of re-parsing. A per-folder SQLite + FTS5 index makes a whole folder searchable without opening anything.

The second view is the win:

	first view (re-parse)	second view (cached)
born-digital PDF	~1.4 s (pypdf)	~60 ms — ~22× faster
image / scan / audio	a vision / Whisper model call	a few ms of text — no model at all

…and the cache is trustworthy: a record is used only if it's signed by a trusted key and its content_hash still matches the file, so "fast" never means "stale or forged."

Demo

$ dnr ingest contract.pdf            # transcribe once → sign → embed in the file
ingested contract.pdf  [in-file]
  method=text-extract transcriber=pypdf
  signed key_id=ce6d170a497238f7

$ dnr read contract.pdf              # later (or from any agent): verified cache hit — no re-parsing
LOAN AGREEMENT
Lender: Acme Capital LLC
Borrower: Jordan Smith
Principal: USD 1,200,000
...

$ dnr index ./contracts
$ dnr query ./contracts --match damages --context 40    # search a whole folder, no files opened
contract.pdf
    … Principal: USD 1,200,000  Maturity: 2026-12-31  Damages clause: section 7.

The transcript lives inside contract.pdf — move it, email it, hand it to another agent, and the cached transcript travels with it.

Quickstart

Requires Python 3.10+ (its stdlib includes the sqlite3 used to read the index — one dependency covers both).

# run with no persistent install:
uvx --from donotreadagain dnr <cmd>
# or install:
pipx install donotreadagain        # or: pip install donotreadagain

dnr ingest report.pdf              # transcribe once (local) → sign → embed in the file
dnr read   report.pdf              # print the cached transcript (verified), or fall back
dnr index  ./case-folder           # build .dnr.db
dnr query  ./case-folder --match "손해배상" --tag 가압류 --since 2025-01-01

For a scan / image / anything you must look at, the agent transcribes it and records the result:

dnr record scan.png --transcript-file t.md --method vision --transcriber <your-model>

How it fits together

File = canonical truth                    Index .dnr.db = derived, regenerable
┌────────────────────────────┐  harvest  ┌────────────────────────────┐
│  signed dnr record          │ ───────▶  │  fixed table + FTS5 search  │
│  content_hash · transcript  │           │  path · tags · transcript … │
│  provenance · fields · sig  │           └────────────────────────────┘
└────────────────────────────┘                 ▲ query via sqlite3 — no dnr install needed
   ▲ transcribe · sign · embed once (expensive)

Where the record lives (no sidecar files):

• In-file for formats with a metadata slot — PDF→XMP, MP3→ID3, PNG→iTXt, JPEG→APP segment. Pixels/bytes-of-content untouched (content_hash invariant), so the transcript travels with the file (move it, email it — it's still there).
• db-only in the folder's .dnr.db for formats with no slot yet (docx, …), or via --no-embed for evidentiary originals you must not modify (file left byte-identical).
• Nothing for already-readable text (.txt/.md/.csv) — an agent just reads it.

Using it

• Read (consumer): dnr read <file> returns the cached transcript only if it's present, trusted, and still matches (self-validating — a changed file silently misses). No dnr tool? An agent can read .dnr.db directly with ambient sqlite3 (the db's _dnr_readme table self-describes).
• Transcribe (producer): dnr ingest (local: pypdf / Whisper / python-docx) or dnr record (agent supplies a vision transcript). dnr owns no model — the transcript is an input from whoever's best placed.
• Query a folder: dnr query <folder> combines --match (FTS, Korean/CJK ok) ∩ --tag a,b ∩ --since/--until ∩ --where; plus --any (OR sweep), --dedup, --context (KWIC), --format json. Save composed queries with --save/--use; accumulate labels with dnr tag.
• Agents onboard once: point an agent at a dnr folder and it fetches SKILL.md once — then it knows dnr everywhere. dnr init just ensures a signing key; nothing is written into your folders.

Design principles

• dnr is the deterministic substrate; the agent is the intelligence. dnr does verifiable primitives (hash, sign, full-text/structured query); it never infers metadata (dates, parties, topics) or does fuzzy semantic search — that's the agent's job. Set metadata explicitly with dnr tag / dnr date.
• File = truth, index = regenerable cache. Delete .dnr.db and rebuild it from the files anytime.
• Transcriber-agnostic. dnr ships a contract (the verbatim guide) + a trust layer, not a model. Fidelity is the transcriber's; provenance is recorded so a consumer can apply its own quality policy (trusted ≠ faithful).

Status & honest limits

v0.1, pre-release. Works today for repeat-access corpora; validated by real-corpus dogfooding. Known limits we're explicit about:

• Adoption is the real lever. The value compounds when agents know dnr (a skill, eventually native support) — not from the tool alone.
• trusted ≠ faithful. A signature proves who made it + that it matches the file, not that the transcription is accurate. Low-quality/garbled transcripts are flagged (dnr status), not silently trusted.
• Not yet published to PyPI; a standalone binary for Python-less environments is future work.

See vision.md (design) · spec/dnr-0.1.md (spec) · SECURITY.md (threat model) · qna.md (settled design decisions) · MILESTONES.md (roadmap).

Development

git clone https://github.com/melodysdreamj/donotreadagain
cd donotreadagain
python -m venv .venv && . .venv/bin/activate
pip install -e ".[dev]"
pytest                       # the suite is green and fast

Contributions welcome — see CONTRIBUTING.md.

License

MIT © 2026 june lee