Canonical: https://dotnetpower.github.io/code-context/probes-and-journal/

# 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](https://dotnetpower.github.io/code-context/benchmarks/), [architecture and privacy boundaries](https://dotnetpower.github.io/code-context/architecture/), and [technical FAQ](https://dotnetpower.github.io/code-context/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`)

[Section titled “1. Declarative Probes (pctx / cctx probe)”](https://dotnetpower.github.io/code-context/probes-and-journal/#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

[Section titled “Measured pctx payload reductions”](https://dotnetpower.github.io/code-context/probes-and-journal/#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`)

[Section titled “2. Compaction Continuity Journal (cctx journal)”](https://dotnetpower.github.io/code-context/probes-and-journal/#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`):

 

Terminal window

```
# Record a verified architectural decision or milestonecctx --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 compactioncctx --root /path/to/project journal resume
# Search historical session decisionscctx --root /path/to/project journal search "cgroup memory reserve"
# Print fail-open hook configuration for agent harnessescctx --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`)

[Section titled “3. BPE-Aligned Formula Prompts (cctx prompt)”](https://dotnetpower.github.io/code-context/probes-and-journal/#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:

 

Terminal window

```
# 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 identifierscctx 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](https://dotnetpower.github.io/code-context/benchmarks/) for the full experimental methodology and confidence intervals.

## Sources and limitations

Measurements are from the repository's published benchmark fixtures, not a performance or billing guarantee. Hardware, workload, cache state and model pricing change results. This documentation site cannot search or read a visitor's local source code. Indexing is local; explicitly requested HTTP probes contact their supplied URLs.