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Silinosic-X

Multi-engine OSINT orchestration for profile intelligence, domain-surface reconnaissance, public-media analysis, OCR image scanning, fused correlation, and Reporter-grade reporting.

Silinosic-X is a Python framework that brings several investigation lanes into one operator flow. It can scan usernames across platforms, inspect domain surface exposure, process public-media and OCR-heavy cases, combine those signals in a fusion run, and drive a policy-led orchestration pipeline that turns raw findings into usable artifacts.

4 main workflows 71 platforms Reporter HTML OCR lane Media recon silinosic-x / silinosic_x

Start Here See the Pipeline CLI Reference

What the repo says today

The codebase centers on prompt and flag workflows, a scope-aware plugin and filter layer, engine-level execution control, correlation, confidence scoring, media and OCR lanes, and output generation to CLI, JSON, CSV, SQL, DOCX, PDF, Reporter HTML, and logs.

4Primary workflows: profile, surface, fusion, orchestrate

5+Engine and specialized intelligence lanes active in the runtime

3Supported Python versions in CI: 3.11, 3.12, 3.13

0Inventory error counts in the current runtime snapshot

1Unified docs shell for the project website under docs/website

What it actually is

Silinosic-X is not one scanner. It is a runtime that coordinates several intelligence lanes.

01

Profile Intelligence

Username and public-profile collection across a large platform set, followed by analysis passes such as contact extraction, identity overlap, activity hints, and link enrichment.

02

Surface Reconnaissance

Domain normalization, CT and RDAP-aware surface collection, subdomain classification, security.txt review, transport posture checks, and domain governance signals.

03

Fusion and Correlation

Profile and domain evidence can be combined into a fused run so entities, issues, and patterns are correlated instead of staying in separate output buckets.

04

Operator-Facing Runtime

Prompt mode, wizard mode, history, explain surfaces, live results view, source-study translation commands, and configurable output roots all live in the same CLI surface.

05

Extension Layer

Plugins and filters are auto-discovered and scope-aware. They enrich raw findings, suppress noise, add prioritization, and surface risk signals that matter to an analyst.

06

Structured Artifact Output

The reporting layer renders results for humans and automation alike through CLI, JSON, CSV, HTML, and persistent logs under a configurable output tree.

Repository-accurate framing

Silinosic-X already ships profile, surface, fusion, orchestration, OCR image scanning, media reconnaissance, plugin/filter discovery, explain surfaces, Docker wrappers, and a Reporter-centered artifact layer.

Execution flow

From operator command to fused report in six stages

Layer guarantees from the architecture docs

Orchestrator stays tool-agnostic.It selects policy and engines, then coordinates capability execution.
Capabilities speak entities.Adapters normalize raw collection output so later stages do not depend on tool-specific dictionaries.
Reporting is presentation-only.Fusion and intelligence decide meaning first; reporting renders afterward.

Hybrid runtime lanes

Console dispatchBanner, prompt rhythm, help flow, and command recovery.
Registry sessionPlugins, filters, modules, presets, selectors, and runtime inventory thinking.
Event flow and fusion graphExecution handoff, correlation, confidence, and analyst reporting.

Quick start

Install the package, run the CLI, import the module with an underscore

shell

pip install silinosic-x
silinosic-x
python -c "import silinosic_x"
# then try:
help
show plugins
profile alice
surface example.com
fusion alice example.com

Naming rule

Install and run the package as silinosic-x. Inside Python code, import it as silinosic_x.

Installation details Command reference Docker runner path

Fit check

When Silinosic-X makes sense and when it does not

Use it if

You want profile intelligence, domain-surface visibility, plugin/filter enrichment, and fused reporting inside one local Python framework.

Skip it if

You only need a tiny single-purpose scanner and do not care about structured artifacts, prompt UX, or cross-lane analysis.

Keep in mind

Some roadmap ideas appear in repo notes, but the website sticks to what the current codebase and docs support today.

Authorized use only

The repository explicitly frames Silinosic-X for legal and authorized investigations. Use it only on systems and identities you own or have explicit permission to assess.