Distilly Context Distiller & Prompt Pruning Optimizer

@distilly/mindset · v1.1.8

A context compression and token economizer engine for DeepSeek Harness. Leverages semantic relevance scoring to prune redundant prompt tokens, deduplicate chat logs, and reduce latency by up to 70%.

Token-OptimizationContext-PruningPrompt-EngineeringCost-Saving
GitHub Stars
42 K+
+12.4% this month
Monthly Downloads
184 K+
Monthly registry pulls
Reach Score
98.8/ 100
Top Tier Ecosystem
Runtime
Cordis v3+
Node 18+ / Bun / Deno

Installation & Integration

CLI one-click launch, package managers, and Cordis integration

bash
manager:
$ pnpm add @distilly/mindset

Architecture

Distilly protects developer budgets and eliminates context drift in extended autonomous tasks. As multi-step agent trajectories unfold, prompt histories swell with repetitive tool schemas, verbose terminal logs, and conversational noise. This plugin implements an intelligent token distillation middleware on the Cordis egress pipeline. By scoring token semantic density, it compresses verbose command outputs into factual summaries, collapses repetitive error traces, and preserves code interfaces—achieving up to 70% token reductions without semantic loss.

Architectural Principles & Constraints

01
Strict Type Isolation

Guaranteed by TypeScript compile-time contracts, inter-plugin event bus calls enjoy zero-drift safety.

02
Sub-Millisecond Hot Reload

Supports dynamic runtime mounting and graceful unloading without restarting the primary host process.

03
Deterministic State Machine

Embeds multi-phase execution lifecycle guards, preventing context loss during long-horizon reasoning.

04
Zero Native Build Dependencies

Designed for lightweight cross-platform environments, booting instantly across Node.js, Bun, and Deno.

Core Features

01
Lossless Semantic Compression: Retains critical variables and constraints without degrading success
02
Substantial Cost Reduction: Lowers input token expenditure by 40% to 70% on long-horizon tasks
03
Slashed First-Token Latency (TTFT): Smaller prompt payloads dramatically shorten prefill latency
04
Configurable Pruning Tiers: Offers Safe, Balanced, and Aggressive compression profiles

Core Workflow

01

Token Density Audit

Scans conversation histories, scoring semantic density and recency decay.

02

Terminal Log Compaction

Identifies massive terminal logs, compacting progress bars into factual summaries.

03

Trajectory Distillation

Distills early trial-and-error reasoning turns into high-density fact checklists.

04

Optimized Payload Egress

Delivers compressed prompt payloads to the DeepSeek inference gateway.

Configuration Parameters Reference (YAML / JSON)

ParameterTypeDefaultDescription
compressionLevelstringbalancedCompression intensity profile
maxLogLinesnumber30Maximum uncollapsed lines retained for tool outputs
preserveCodeBlocksbooleantruePrevent compression from altering code block syntax

Use Cases

Enterprise Production Agent

Relies on microkernel lifecycle guards and fault-tolerant state machines for continuous reliability.

SWE-bench Benchmark Evaluation

Native integration with SWE-bench workflows, automatically capturing diffs and verification metrics.

Autonomous Code Refactoring

Separates reasoning from tool actions to independently locate and refactor multi-file codebases.

Cross-Tool Workflow Automation

Safely orchestrates events across sandboxes to seamlessly link enterprise developer tooling.

Best Practices

01
Sandbox Permission Guard

Strictly isolate sub-process calls and network scope; deploy within Docker containers in production.

02
Exponential Backoff Retries

Configure adaptive exponential retries with strict timeouts to mitigate upstream model rate limits.

03
Session State Checkpointing

Persist state machine snapshots to survive hardware interruptions and resume instantly without loss.

04
Full Trajectory Audit Logs

Enable full trace logging, aggregating reasoning thought streams and tool I/O into your observability hub.

FAQ

Q1:How to handle timeouts in long-running autonomous tasks?

Increase the timeout parameter inside your YAML configuration and dispatch periodic heartbeat signals across the Cordis event bus. For long-running tool execution and model reasoning, configure persistent session snapshotting so suspended tasks can safely resume their exact context after interruptions, preventing the kernel from recycling active agent sessions prematurely.

Q2:How to capture and stream the model reasoning thought process?

The harness runtime natively provides end-to-end streaming hooks while its state machine automatically intercepts and strips <think> reasoning tags from model responses. Subscribe directly to the onThink event listener to consume live reasoning token streams in real-time, delivering typewriter animation to the user interface while persisting full trajectories for audit compliance.

Q3:How to resolve dependency conflicts across multiple plugins?

Cordis microkernel uses directed acyclic graph topological sorting to dynamically resolve plugin dependencies. When shared services or runtime versions conflict, assign distinct isolated namespaces at the application entrypoint. Leveraging context injection alongside lazy activation ensures dependencies load strictly on-demand when tools are triggered, maintaining system stability and preventing memory bloat.

Q4:How to enforce permissions and sandbox isolation in production?

Combine isolated container sandboxing with fine-grained capability checks to prevent plugins from accessing sensitive files or unauthorized external networks. Enforce explicit runtime system call whitelisting through the microkernel, executing all third-party tool scripts inside isolated ephemeral containers to block arbitrary code execution and eliminate security privilege escalation risks entirely.

Recommended Ecosystem Plugins

Explore related Cordis extensions designed to work synergistically