Zero-Schema Probes (pctx), Journal & Formula Prompts

When should I use pctx probes and the continuity journal?

Use pctx to inspect JSON, APIs, tables, images, and logs; preserve compact continuity with cctx journal and compile formula prompts.

Scope and evidence

Local developer tooling, not a hosted source-search service. Benchmark results are workload-specific, not guarantees.

Where can I verify the results and limitations?

Read the benchmark methodology, architecture and privacy boundaries, and technical FAQ.

Coding agents lose tens of thousands of tokens per session on tasks outside source code: inspecting 1 MB API payloads, writing throwaway python3 -c or jq scripts, re-running noisy test suites, and losing architectural state after context compaction.

Code Context solves all three in native Rust CLI subcommands (pctx / cctx probe, cctx journal, and cctx prompt) with 0 standing MCP tool-schema tokens and 0 LLM script-authoring turns.

---

1. Declarative Probes (pctx / cctx probe)

pctx (installed alongside cctx as a direct alias for cctx probe) gives agents six deterministic inspection primitives with automatic secret/token scrubbing and ANSI stripping:

Command	Replaces	Example Usage
pctx json	jq, python3 -c "import json..."	pctx json response.json --where status=500 --count-by service
pctx http	Multi-curl loops & OpenAPI/STAC scripts	pctx http "https://api.example.com/{svc}/health" --var svc=auth,billing
pctx table	Ad-hoc sqlite3 / Parquet / CSV Python scripts	pctx table metrics.db --sql "SELECT model, AVG(ms) FROM runs GROUP BY 1"
pctx image	Blindly attaching multi-megabyte PNGs	pctx image ui-after.png --expect 1440x900 (dimensions, channels, exposure check)
pctx batch	Raw multi-command log dumps	pctx batch --cmd "cargo test --locked" --query "FAILED|panicked"
pctx log	Re-running slow builds to grep a different error	pctx log @latest "deadlock|timeout" (queries saved @sN spool in ~11 ms)

Measured pctx payload reductions

• pctx json on 5,000 API records (1,021,281 B): emits a 662 B structured schema + filtered count digest in 7.82 ms (-99.94% payload bytes).
• pctx batch + pctx log @latest on a 2,400-line cluster log (226,918 B): spools output once as @s1, preserves code fences and stack traces intact, and answers follow-up Okapi BM25 + Trigram Reciprocal Rank Fusion (RRF) queries in 11.00 ms (2,273 B, -99.00%) with zero command re-execution.

---

2. Compaction Continuity Journal (cctx journal)

Long coding sessions inevitably hit context-window compaction, causing agents to forget subtle invariants, rejected approaches, or verified root paths. cctx journal maintains a project-scoped continuity log that resumes into strictly <= 2,048 B (2 KiB):

# Record a verified architectural decision or milestone
cctx --root /path/to/project journal record \
  --summary "Switched planner literal extraction to ASCII case-folded trigrams"

# Emit <= 2 KiB <cctx_journal_state> block + top BM25/RRF recall after compaction
cctx --root /path/to/project journal resume

# Search historical session decisions
cctx --root /path/to/project journal search "cgroup memory reserve"

# Print fail-open hook configuration for agent harnesses
cctx --root /path/to/project journal hook-config

• Measured compression: Compresses a 40-step (434,271 B) engineering session into 1,829 B (-99.58%) in 5.51 ms.
• Fail-open safety: Hook integration never blocks tool execution (0 PreToolUse denials).

---

3. BPE-Aligned Formula Prompts (cctx prompt)

Verbose prose instructions in AGENTS.md, CLAUDE.md, MCP descriptions, and subagent definitions get re-tokenized on every single turn. cctx prompt compiles agent instructions and user task prompts into compact, BPE-aligned symbolic specs while preserving domain nouns and safety invariants:

# Render BPE-aligned formula instructions (-63.1% tokens: 2,048 -> 756 tok)
cctx --root /path/to/project prompt --style formula --target instructions

# Compress an English or Korean task prompt while preserving technical identifiers
cctx prompt --compress "Analyze planner.rs and fix case-insensitive regex fallback" --json

Supported --target outputs include instructions, mcp, subagent-yaml, subagent-md, and claude-md.

Continue to Benchmarks & Token Suites for the full experimental methodology and confidence intervals.