Reactive Resume Semantic Parser & Career Engine

@reactive-resume/dsh-plugin · v4.5.1

A structured resume parsing and tailored compilation pipeline for DeepSeek Harness. Extracts granular career graphs from PDFs and compiles ATS-optimized resumes targeted against job specs.

Resume-ParserJSON-ResumeATS-OptimizationPDF-Export
GitHub Stars
44 K+
+12.4% this month
Monthly Downloads
198 K+
Monthly registry pulls
Reach Score
98.9/ 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 @reactive-resume/dsh-plugin

Architecture

Reactive Resume transforms unstructured career documents into typed JSON Resume schemas. Traditional document parsers struggle with dynamic layouts, misattributing technical seniority and accomplishments. This plugin establishes standard JSON Resume as an intermediate representation. Agents parse heterogeneous PDF, DOCX, and Markdown resumes, cross-referencing candidate profiles against target job descriptions for ATS semantic ranking, quantified impact polishing, and pixel-perfect PDF rendering.

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
JSON Resume Compliant: Adheres to open-source schema specifications for cross-platform portability
02
ATS Simulation Scoring: Replicates mainstream enterprise ATS parsing filters to maximize pass rates
03
STAR Impact Polishing: Reframes generic task bullet points into measurable metric-driven achievements
04
Cross-Border Localization: Automatically localizes domestic resumes into US/EU compliant formats

Core Workflow

01

Multi-Format Ingestion

Parses PDF and DOCX binaries, extracting visual bounding blocks and chronology.

02

Schema Normalization

Converts raw text into typed JSON Resume objects with standardized skill tags.

03

JD Matching & Gap Audit

Scores match index against target job descriptions, surfacing missing skills.

04

ATS Compilation & Export

Polishes STAR-format achievement bullets, compiling machine-parseable PDFs.

Configuration Parameters Reference (YAML / JSON)

ParameterTypeDefaultDescription
targetLocalestringenTarget resume output language
strictATSbooleantrueEnforce single-column ATS-safe layout styles
anonymizePiibooleanfalseMask personal identifiable info during processing

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.

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