A deterministic selector that reduces the token cost of structured files before they enter an AI agent's context window.

The selector supports local JSON, JSONL, CSV, and TSV files. For each file, it generates reversible candidate representations, counts tokens for the active model tokenizer, verifies that the winning candidate decodes back to the parsed source value, and returns the lowest-token safe read path.

It is built for agent runtimes such as Codex, Claude Code, Pi, Hermes Agent, OpenClaw, MCP tools, and generic local agents that often read large structured files as context.

• Lossless structured-context selection: not semantic summarization, not fuzzy prompt compression.
• Source-file preserving: adapters write optimized sidecars under .codex/context-cache/ and never rewrite the referenced data files.
• Tokenizer-aware: token counts use tiktoken for OpenAI models or a configured Hugging Face tokenizer.json; fallback counts are labelled as estimated.
• Deterministic: fixed input, model profile, tokenizer, and candidate tier produce the same selection.
• Conservative at runtime: semantic shell operations such as jq, grep, sed, head, pipes, cat -n, unsupported files, and low-savings cases stay raw.

The core flow is:

agent reads a structured file
-> adapter asks selector.py for a verified decision
-> selector writes a lower-token sidecar when it is safe
-> adapter substitutes the verified read_path
-> model sees optimized content, source file remains unchanged

Latest checked-in downloaded-corpus run:

• Model: gpt-5.4-mini via tiktoken
• Corpus: 28 JSON/JSONL/CSV/TSV files from public tabular, QA, conversation, review, code/documentation, log, and GitHub metadata datasets
• Candidate tier: benchmark path includes advanced candidates; runtime hooks default to the safer tier

Best source-family reductions in that run:

See reports/benchmark-report.md and EVIDENCE.md for the full corpus, commands, per-file results, baseline handling, and limits of the claim.

Important boundary: the repo currently proves deterministic token savings and round-trip safety. Full answer-parity evidence across model families is still an optional eval path, not a completed production claim.

python3 -m venv .venv
.venv/bin/python -m pip install -r requirements.txt
chmod +x run-hook.sh

Run the unit suite:

.venv/bin/python -m unittest discover -s tests

Try the selector directly:

.venv/bin/python selector.py \
  --cwd "$PWD" \
  --model gpt-5.4-mini \
  --adapter manual \
  --include-candidates \
  --verify-report \
  sample-repetitive.json

The output is a context-selector/v1 JSON report. Each result contains:

• source: original file path
• read_path: the verified path an adapter may read
• selected: whether a sidecar won
• selected_format, raw_tokens, selected_tokens, saved_tokens
• source and sidecar hashes for auditability

Adapters should trust only the verified read_path, not a raw output_path.

Add the Bash hook to ~/.codex/config.toml:

[features]
hooks = true

[[hooks.PreToolUse]]
matcher = "Bash"

[[hooks.PreToolUse.hooks]]
type = "command"
command = "/absolute/path/to/context-compression/run-hook.sh"
timeout = 30
statusMessage = "Optimizing data file reads"

Manual smoke test:

printf '%s\n' '{"hook_event_name":"PreToolUse","cwd":"'"$PWD"'","model":"gpt-5.4-mini","tool_name":"Bash","tool_input":{"command":"cat sample-repetitive.json"}}' \
  | ./run-hook.sh

Expected result: hookSpecificOutput.updatedInput.command points at a sidecar under .codex/context-cache/. The hook does not add optimizer narration to the conversation.

Current Codex boundary: PreToolUse can rewrite whole-file Bash reads. UserPromptSubmit no-ops by default because current Codex hooks cannot invisibly replace pasted prompt text or app-injected file attachments.

All adapters are thin wrappers over the same selector/verifier contract. They must not implement independent compression logic.

See adapters/CONTRACT.md for the required adapter invariants.

Supported source formats are JSON, JSONL, CSV, and TSV.

The default runtime candidate tier is safe:

• raw/no conversion
• compact JSON
• columnar JSON as [columns, rows]
• CSV/TSV with JSON cells

Advanced candidates are available for offline evaluation:

• codebook JSON as [columns, dictionaries, rows]
• typed CSV/TSV

Enable the larger candidate set only when you are intentionally evaluating it:

CONTEXT_OPTIMIZER_CANDIDATE_TIER=advanced

Keep advanced out of invisible runtime hooks until paired quality evals prove answer parity for the target model family.

Selection rule:

best = argmin token_count(decoder_instructions(candidate) + candidate, model)
       subject to round_trip(candidate) == parsed_source_value

select only if best != raw
       and savings >= CONTEXT_OPTIMIZER_MIN_SAVINGS_RATIO
       and saved_tokens >= CONTEXT_OPTIMIZER_MIN_SAVED_TOKENS

The default absolute floor is 128 saved tokens. This keeps tiny token wins out of invisible runtime rewrites, where local preprocessing may cost more than the provider-side savings.

Optional latency gate:

CONTEXT_OPTIMIZER_PROVIDER_INPUT_TOKENS_PER_SECOND=1500
CONTEXT_OPTIMIZER_MIN_NET_LATENCY_SAVED_MS=0
CONTEXT_OPTIMIZER_MAX_HOOK_LATENCY_MS=500

When provider throughput is configured, PreToolUse rewrites only when the projected provider-side input latency saved by fewer tokens is greater than local preprocessing time plus any configured margin.

Use the unit suite after changing hook behavior, candidate encoding/decoding, model-profile resolution, selector reports, or Bash rewrite logic:

.venv/bin/python -m unittest discover -s tests

Run local harness smokes:

.venv/bin/python scripts/run_harness_smokes.py

When changing adapter glue or install instructions, verify host contract assumptions too:

.venv/bin/python scripts/verify_harness_contracts.py

Run the lean evidence gate before using benchmark or product claims:

.venv/bin/python scripts/verify_evidence.py --full-tests

Verify a clean install from an isolated temporary checkout:

python3 scripts/verify_clean_install.py

Build or refresh the local, git-ignored benchmark corpus:

.venv/bin/python benchmark.py all \
  --rows 1000 \
  --out data/benchmark-corpus \
  --corpus data/benchmark-corpus \
  --candidate-tier advanced \
  --input-price-per-1m 5 \
  --monthly-calls 100000 \
  --provider-input-tokens-per-second 1500 \
  --require-publication-corpus

--input-price-per-1m is a scenario value. API pricing changes, so rerun with current pricing before using dollar projections externally.

Check the corpus without running token counts:

.venv/bin/python benchmark.py verify-corpus --corpus data/benchmark-corpus

To compare external codecs without adding runtime dependencies, use --baseline-dir or reproducible --baseline-command inputs:

.venv/bin/python benchmark.py run \
  --corpus data/benchmark-corpus \
  --baseline-command 'toon=/opt/homebrew/bin/npm exec --yes --package @toon-format/toon@2.3.0 -- node scripts/toon_baseline.mjs --fallback-raw-on-fail {input} {output}' \
  --baseline-command 'onto=onto encode --input {input} --output {output}'

Generated baseline outputs include provenance and hashes so baseline claims are auditable. Baseline tooling stays benchmark-only until it wins on token count, round-trip safety, deterministic behavior, latency break-even, and answer parity for the relevant data family.

Adapters pass the active model slug as model when the host exposes it. The selector resolves model metadata from:

• the adapter or hook payload
• ~/.codex/config.toml
• project .codex/config.toml
• model_catalog_json from Codex config
• CONTEXT_OPTIMIZER_MODEL_CATALOG_JSON
• bundled model-catalog.snapshot.json

OpenAI model slugs use tiktoken when available. Modern GPT/Codex slugs not yet mapped by tiktoken default to o200k_base; override with CONTEXT_OPTIMIZER_TIKTOKEN_ENCODING when needed.

For non-OpenAI models, set:

CONTEXT_OPTIMIZER_TOKENIZER_JSON=/path/to/tokenizer.json

When this is set and tokenizers is installed, the selector uses that exact Hugging Face tokenizer instead of the fallback estimate.

The optional Inspect AI path builds paired raw/optimized tasks from benchmark data:

.venv/bin/python evals/build_context_quality_dataset.py \
  --corpus data/benchmark-corpus \
  --out evals/context-quality.generated.jsonl \
  --model gpt-5.4-mini

.venv/bin/python evals/verify_context_quality_dataset.py \
  evals/context-quality.generated.jsonl

That verifies local pair integrity only. Do not promote it to answer-parity evidence until an Inspect run has been executed against real models and summarized. See evals/README.md.

This project is a deterministic format selector, not a universal new notation. Related work includes LLMLingua/LongLLMLingua-style prompt compression, TOON, ONTO, table-structure evals, tokenizer studies, and dictionary-encoding approaches.

Those systems should be treated as matched benchmark baselines rather than background references only. The selector should win on the target axis for the input family being claimed: token savings, exact round-trip fidelity, determinism, latency break-even, or model utility at a fixed budget.

See RESEARCH.md and PAPER_SWEEP_2025_2026.md for the full notes.

• selector.py: reusable selector CLI and context-selector/v1 report surface
• hook.py: conservative Codex hook runtime
• verify_selector_report.py: report verifier
• benchmark.py: corpus build, token benchmark, and baseline comparison path
• adapters/: Codex-adjacent and agent-runtime integrations
• evals/: optional Inspect AI quality-check path
• reports/, EVIDENCE.md: checked-in evidence surfaces

Generated local artifacts under .codex/context-cache/, data/benchmark-corpus/, logs/, .venv/, and Python cache directories should stay untracked.

MIT