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## Project Ploughshares — OSINT Intake Workflow
OSINT intake for international trade signals to help detect embargoevasion.
---
### Problem
- Fragmented intake
- Missed signals at scale
- Limited auditability
---
### Approach
- Unified intake + approval (Flask UI + JSON API)
- Automated ingestion (crawlers, curated feeds)
- Quality & accessibility checks in dev
---
### Architecture
```mermaid
flowchart LR
Crawler[Crawlers & Feeds] -->|POST /ingest| API
Analyst -->|Submit| WebUI
WebUI -->|REST| API
API --> DB[(DB)]
API --> Queue[Approval Queue]
Queue -->|Approve/Deny| API
```
---
### End-to-End
```mermaid
sequenceDiagram
participant Analyst
participant WebUI as Web UI
participant API
participant DB as DB
participant Crawler
Analyst->>WebUI: Submit signal
WebUI->>API: Create (pending)
API->>DB: Persist
Crawler->>API: Ingest leads
API->>DB: Upsert
Analyst->>WebUI: Approve/Deny
WebUI->>API: Decision
API->>DB: Finalize
```
---
### Implemented
- Flask app (`docker/ploughshares/app.py`)
- Schema/init (`docker/ploughshares/schema.sql`, `init_db.py`)
- Crawlers/feeds (`docker/crawler/*`, `docker/crawler_dorks/*`)
- Quality checks (`check_accessibility.py`, `check_contrast.py`, `run_lighthouse_test.sh`)
- Tests (`tests/`)
---
### Next
- Implement vector search for intake to perform semantic matching beyond literal keywords and synonyms, surfacing adjacent signals and concepts.
- Author and test Disaster Recovery Plan (DRP) and Business Continuity Plan (BCP), with RTO/RPO targets, backups, failover, and runbooks.
- Expand API docs and sample payloads.

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## Project Ploughshares - OSINT Intake Workflow Demo
This demo summarizes the initial implementation of a replacement intake workflow for OSINT signals related to international trade, intended to help detect and deter embargo-evasion patterns. The system focuses on capturing OSINT leads, triaging and approving them, and maintaining an auditable record suitable for collaboration with researchers and analysts.
### Goals
- **Streamline OSINT intake**: Provide a clear path to submit, normalize, review, and approve OSINT signals and related evidence.
- **Create an auditable workflow**: Every step - from ingestion to analyst approval - is persisted with status and metadata.
- **Enable automation**: Support ingestion of leads from crawlers and curated feeds to reduce manual toil.
- **Improve quality**: Bake in accessibility and quality checks (contrast, Lighthouse, basic headers) for the analyst-facing UI.
### What we built (repo tour)
- **Web/API app**: Flask application for UI and JSON endpoints
- `docker/ploughshares/app.py`, templates in `docker/ploughshares/templates/`, static assets in `docker/ploughshares/static/`
- Database schema and setup in `docker/ploughshares/schema.sql` and `docker/ploughshares/init_db.py`
- **Ingestion**: Crawler components to fetch and submit OSINT leads to the API
- `docker/crawler/*.py` and `docker/crawler_dorks/*.py` (e.g., `write_to_api.py`, `marketline_crawler.py`)
- **Quality/Accessibility**: Utility scripts and checks
- `check_accessibility.py`, `check_contrast.py`, `run_lighthouse_test.sh`
- **Tests and code quality**: `tests/` (API behavior, dependencies, and basic quality gates)
- **Runtime**: Docker Compose files for local development and deployment stacks
- `docker-compose.dev.yml`, `docker-compose.yml`, `stack.staging.yml`, `stack.production.yml`
### High-level workflow (sequence)
```mermaid
sequenceDiagram
participant Analyst
participant WebUI as Web UI (Flask)
participant API as API (Flask/JSON)
participant DB as Database
participant Crawler
Analyst->>WebUI: Submit OSINT signal for review
WebUI->>API: POST /transactions (signal + metadata)
API->>DB: Insert pending transaction with audit fields
Crawler->>API: POST /ingest (scraped leads)
API->>DB: Upsert leads and link evidence
Analyst->>WebUI: Review pending approvals
WebUI->>API: PATCH /transactions/:id approve/deny
API->>DB: Update status and record decision
```
### Quality gates and accessibility checks (sequence)
```mermaid
sequenceDiagram
participant Dev
participant CI as CI/Local Checks
participant LHT as Lighthouse
participant A11y as Accessibility
Dev->>CI: Push or run checks locally
CI->>LHT: run_lighthouse_test.sh (performance & a11y)
CI->>A11y: check_accessibility.py / check_contrast.py
CI->>Dev: Reports and action items
```
### How this supports Project Ploughshares
- **Mission-aligned intake**: The workflow is designed to help researchers and analysts surface potential embargo-evasion signals in international trade.
- **Traceability**: Signals and decisions are stored with clear statuses for collaboration and later analysis.
- **Extensible feeds**: Crawlers and curated feeds can be extended as new sources become relevant.
### Next steps (priority first)
- Implement vector search for intake to perform semantic matching beyond literal keywords and synonyms, surfacing adjacent signals and concepts.
- Author and test Disaster Recovery Plan (DRP) and Business Continuity Plan (BCP), with RTO/RPO targets, backups, failover, and runbooks.
- Expand API docs and sample payloads.