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dotnetpower/code-context

Fast local code search, source reads, and smart block-clamped context for AI coding agents

Before you install: no copy-paste command · license MIT · link reachable

First step: nothing runnable here yet - start from the repository: https://github.com/dotnetpower/code-context

View on GitHub ↗Link checked 2026-11-14 · HTTP 200 · opens the vendor's own site
Star count
0 ★ · dotnetpower/code-context · read from the GitHub search API · (checked 2026-11-13 20:00:50)
Source
read directly from its GitHub page · type: open-source project
Harvest history
first seen 2026-11-14 · last seen 2026-11-14 · seen 1× · listed by 1 sources
Score
score 103 · 1 sources · repo
FAQ (answers come from our records)
Who is it for?
We filed it under these tags: 开源项目. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository dotnetpower/code-context;first catalogued 2026-11-14;last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.

# Code Context

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

Fast local file discovery, source search, smart block-clamped context, zero-schema data/log probes (pctx), and compaction continuity (cctx journal) for coding agents on Linux, native Windows (cctx.exe), WSL, and SSH.
Runs as 7 native VS Code Language Model Tools (#cctxNativeContext, #cctxNativeGrep, #cctxNativeRead, #cctxNativeFiles) and an MCP server backed by one shared Rust cache, 💰 Real-bill LLM cost reduction — not raw byte vanity: Controlled Copilot A/B trials cut net session billing by 25.4% (4.76B → 3.55B nanoAIU) and wall time by 24.7% (100% accuracy). Across 159 live sessions / 7,578 turns (24h), cctx modeled $48.88 (-26.6%) lower spend (~$183.78 → $134.90 @ $0.001/AIC; ~$488.82 @ $0.01 API list ref.) by avoiding ~1.58B tokens — 54.0% from avoided LLM turns (851.8M) and 44.5% from avoided prompt-cache re-reads (701.8M, 98.7×), not single-output byte trimming (7.1M, 0.5%).
## 💰 Net LLM cost analysis & live cctx usage
sh
$ cctx usage --hours 24 # or: cctx usage --hours 24 --details | --json
⚡ CCTX USAGE · Last 24 hours
💰 $48.88 saved · 26.6% less (estimated: Without cctx ~$183.78 → With cctx $134.90 @ $0.001/AIC; not a bill)
EFFICIENCY: Tokens avoided ~1.58B (851.8M turns · 701.8M cache 98.7x · 16.1M BPE) · Warm hits 95.5% · Adoption 90.3%
| Cost driver (full cost anatomy) | Built-in (rg + view) | Post-hoc hook / MCP sandbox plugins | Code Context (cctx + pctx + journal) |
| Standing prompt / schema tax | 0 tok/turn | +2,000 to +6,300 tok/turn (9–11 MCP schemas) | -1,292 to -2,123 tok/turn (BPE formula; 0 extra MCP schemas) |
| Execution flow & VS Code support | 2–4 turns (rg → slice view) | PreToolUse deny → LLM writes JS/shell → re-run (PostToolUse replace is no-op in VS Code) | 1 turn (#cctxNativeContext block-clamped; 0 PreToolUse denials) |
| Controlled A/B coding session bill | Baseline (100% pass) | +2.2% to +34.6% MORE expensive (+15.5% on Try-It) | -25.4% net AIU cost (-24.7% wall time, 100% accuracy) |
| 24h multi-session spend (159 sess) | ~$183.78 (183.8K AIC est.) | Extra turns + output tokens offset byte cuts | $134.90 (-$48.88 USD / -26.6% less · ~$488.82 @ $0.01 API list ref.) |
## Measured speed, token & zero-schema probe benchmarks
On AKS Store Demo, the fixed mixed retrieval suite averaged 17.22× faster with a warm CCTX cache (95% CI 17.04–17.40×, up from 6.83× in v0.14): 3.996 → 0.232 ms per operation, with a 19.735 ms cold sync median (5.01× faster than v0.14).
| Retrieval task (AKS Store Demo) | CCTX off | CCTX on, warm | Off / on |
| Filename discovery | 4.466 ms | 0.254 ms | 17.57× |
| Literal search | 5.234 ms | 0.230 ms | 22.77× |
| Search + context | 5.343 ms | 0.250 ms | 21.39× |
| Known file read + hash | 0.081 ms | 0.190 ms | 0.43× — slower |
| Fixed mixed suite | 3.996 ms | 0.232 ms | 17.22× |
| Token, probe & agent benchmark | Baseline / raw input | With cctx / pctx / journal | Reduction / speedup |
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Source
github · https://github.com/dotnetpower/code-context
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