DocSentinel README

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DocSentinel
AI-powered SSDLC platform — Secure your software from requirements to operations

What is DocSentinel?

DocSentinel is an AI-powered SSDLC (Secure Software Development Lifecycle) platform for security teams. It automates security activities across all six phases of the software development lifecycle using intelligent AI agents orchestrated by LangGraph and powered by LangChain. It automates the review of security-related documents, forms, and reports — from requirements and design through development, testing, deployment, and operations — comparing inputs against your policy and knowledge base to produce structured assessment reports with risks, compliance gaps, and remediation suggestions.

Instead of only reviewing documents at the pre-release stage, DocSentinel embeds security from day one:

Built as a React console + FastAPI service + MCP/A2A agent gateway, DocSentinel integrates into local security review workflows, CI/CD pipelines, AI agents, and multi-agent platforms without giving external agents approval authority.

• LangGraph orchestration: Stateful, graph-based agent workflows with conditional branching per SSDLC stage.
• Multi-format input: PDF, Word, Excel, PPT, text — parsed into a unified format for the LLM.
• Knowledge base (RAG): Upload policy and compliance documents; the agent uses them as reference when assessing.
• Multiple LLMs: Use OpenAI, Claude, Qwen, or Ollama (local) via a single interface.
• Structured output: JSON/Markdown reports with risk items, compliance gaps, and actionable remediations.

Ideal for enterprises that need to scale security assessments across many projects and SSDLC stages without proportionally scaling headcount.

Product Tour

The local React console brings the main workflow into one operational surface:

• Command Center: live API and LLM status, assessment throughput, review demand, remediation queues, and recent activity.
• Assessment Workbench: upload project documents, choose SSDLC phase/skill, inspect AI-generated risks, and complete human review.
• Governance Portal: create projects, apply public framework overlays, generate controls, submit evidence, and track Pallas Lens readiness.
• Knowledge Base: ingest policies and standards for RAG-backed review.
• Agent Integrations: expose governed MCP and A2A tools to coding agents and multi-agent platforms without granting approval authority.
• Settings: switch providers such as DeepSeek, OpenAI, Anthropic, Qwen, or Ollama; API keys are accepted locally and only shown as masked previews.

Why DocSentinel?

See the full problem statement and SSDLC phase details in SPEC.md.

Architecture

DocSentinel is a React Console + FastAPI application with three governed entry paths: REST APIs for the console and CI, MCP tools for coding agents, and A2A JSON-RPC for remote agent delegation. These entry paths converge on the same AssessmentService, LangGraph assessment pipeline, knowledge base, and human-review lifecycle. Governance workflows from PallasGuard are now a first-class domain beside assessment, backed by SQLModel records, policy packs, control evidence, audit trails, and Pallas Lens readiness scoring.

flowchart TB
    subgraph Access["Users and Agent Access"]
        direction LR
        Staff["Security staff"]
        Console["React Console<br/>(Vite + Tailwind)"]
        RESTClient["REST / CI clients"]
        AgentClient["MCP / A2A clients"]
    end

    subgraph Runtime["FastAPI Runtime"]
        direction LR
        Security["Security boundary<br/>CORS, rate limit, JWT/RBAC,<br/>gateway token or loopback"]
        REST["REST API routers<br/>assessments, KB, skills,<br/>settings, governance"]
        Gateway["Agent Gateway<br/>MCP tools + A2A JSON-RPC"]
        Tasks["AssessmentService<br/>async tasks, activity log,<br/>human review queue"]
    end

    subgraph Pipeline["Assessment Pipeline"]
        direction LR
        Parse["Parse + guardrails<br/>Docling or legacy"]
        Graph["LangGraph workflow<br/>skill, isolated document data,<br/>policy/history/evidence context"]
        Review["LLM draft + review<br/>via LangChain"]
        Validate["Schema validation<br/>+ S2O rule checks"]
        Report["Structured report<br/>risks, gaps, remediations"]
    end

    subgraph Governance["Governance / Pallas Domain"]
        direction LR
        Projects["Projects + framework selection"]
        Controls["Control generator<br/>questionnaires + applicability"]
        Evidence["Gate submissions<br/>evidence + audit logs"]
        Lens["Pallas Lens<br/>readiness + exports"]
    end

    subgraph Support["Shared Support Services"]
        direction LR
        KB["KnowledgeBaseService<br/>Chroma + LightRAG + history"]
        Policies["Policy packs + overlays<br/>schema service + S2O ontology"]
        LLMFactory["LLM factory<br/>settings + llm_config.json<br/>base_url SSRF guard"]
        Providers["OpenAI, Anthropic, Qwen,<br/>DeepSeek, Ollama, local OpenAI"]
        DB["SQLModel DB<br/>SQLite or Postgres"]
    end

    Staff --> Console
    Console --> Security
    RESTClient --> Security
    AgentClient --> Security
    Security --> REST
    Security --> Gateway
    REST --> Tasks
    Gateway --> Tasks
    REST --> Projects
    REST --> KB
    Tasks --> Parse --> Graph --> Review --> Validate --> Report
    Report --> Tasks
    Report -->|project_id present| Evidence
    Projects --> Controls --> Evidence --> Lens
    Graph --> KB
    Graph --> Policies
    Review --> LLMFactory --> Providers
    Validate --> Policies
    Policies --> Controls
    Projects --> DB
    Controls --> DB
    Evidence --> DB

Data flow (simplified):

1. Security staff work in the React console; REST clients call /api/v1/*; coding agents call MCP or A2A through the agent gateway.
2. REST write paths use JWT/RBAC dependencies. Agent protocols use a bearer gateway token or loopback-only development access. LLM-costly POST paths are rate limited by IP or bearer token.
3. Assessment submissions enter AssessmentService, which creates an async task, parses uploaded or approved local documents, applies guardrails, and invokes the LangGraph assessment pipeline.
4. The LangGraph pipeline loads the selected skill, wraps untrusted document content as data, retrieves policy/history/evidence context from the KB, asks the LLM for draft/review text, and converts the result into the structured assessment schema.
5. Deterministic services remain authoritative for governance decisions: policy-pack schemas, the S2O rule engine, control applicability, and schema validation cross-check LLM output before it enters the review queue.
6. When an assessment is linked to a project, findings are persisted as Gate 3 control evidence. Governance workflows then use the same SQLModel store for projects, controls, submissions, audit logs, Pallas Lens scoring, and exports.
7. The KB persists chunks in Chroma, optional graph artifacts in LightRAG, and prior assessment history for reuse. Runtime LLM settings are loaded from .env plus llm_config.json, and every provider base URL is checked by the network guard before client construction.

Detailed architecture: ARCHITECTURE.md and docs/01-architecture-and-tech-stack.md.

Core Capabilities

SSDLC Full Lifecycle Coverage

Six dedicated AI agents, each with phase-specific skills, prompts, and knowledge base collections. Run individual phases or a full end-to-end SSDLC assessment:

• Requirements: Security requirements, compliance mapping, initial risk analysis.
• Design: Architecture review, STRIDE/DREAD threat modeling, SDR.
• Development: Secure coding standards, code review findings.
• Testing: SAST/DAST report triage, pen-test evaluation.
• Deployment: Release readiness, config security, hardening.
• Operations: Incident response, vulnerability monitoring, log audit.

Automated Security Assessment

Submit security questionnaires, design documents, or audit reports. DocSentinel analyzes them using configured LLMs and identifies:

• Security Risks: Classified by severity (Critical, High, Medium, Low).
• Compliance Gaps: Missing controls against frameworks like ISO 27001, PCI DSS.
• Remediation Steps: Actionable advice to fix identified issues.

Governance & Compliance Workflows

PallasGuard governance capabilities are merged into DocSentinel as first-class project workflows:

• Policy packs: generic-ssdlc plus eight public overlays for NIST SSDF, MAS TRM, ISO 27001:2022, EU AI Act, ISO 42001, China MLPS 2.0, OWASP SAMM, and EU CRA.
• Gate workflows: Gate 1/3 questionnaire, submission, approval, audit, and evidence tracking flows backed by SQLModel and Alembic migrations.
• Control generation: Framework overlays generate applicable SCD controls and expected evidence without replacing DocSentinel's existing assessment engine.
• Pallas Lens: Project readiness scoring summarizes control coverage, evidence depth, and next actions in the React governance portal.

Intelligent Agent Orchestration (LangGraph)

• Structured workflows: LangGraph nodes separate context collection, drafting, evidence review, schema conversion, and governance persistence
• Cross-phase traceability: Threats from Design link to test cases in Testing and monitoring rules in Operations
• Conditional routing: Agents activate based on project risk level, compliance requirements, or user selection
• Human-in-the-loop: Agent-created assessments enter a review queue and cannot approve themselves
• Planned hardening: Explicit plan gates, durable LangGraph checkpoints, and resumable execution are tracked in the development roadmap below

Threat Evidence Critic

Design-phase STRIDE threats are independently checked against the currently uploaded architecture documents in one inference-time pass:

• Every threat receives a supported, contradicted, or insufficient_evidence verdict with a support score and reviewer-facing rationale.
• Evidence references use content hashes plus stable line locators and include the exact source excerpt shown in the Assessment Workbench.
• Only current-project document passages are accepted. Invented, missing, policy, or historical references cannot make a threat appear supported.
• Verification failures safely abstain as insufficient_evidence; all verdicts still require human review.
• No training or fine-tuning pipeline is required. The critic uses the configured OpenAI, Anthropic, Qwen, DeepSeek, compatible, or local runtime model.

For a public-demo input and walkthrough, see examples/evidence-critic-architecture.md and examples/README.md.

RAG-Powered Knowledge Base

Upload your organization's security policies, standards, and past audits. Phase-specific collections ensure each agent retrieves the most relevant context:

• Requirements: compliance frameworks, security policies
• Design: threat catalogs, security patterns
• Development: secure coding standards (OWASP)
• Testing: vulnerability databases, remediation guides
• Deployment: CIS benchmarks, hardening guides
• Operations: CVE databases, incident playbooks

LangGraph Agent Orchestration

Powered by LangChain + LangGraph — stateful, graph-based agent workflows with conditional routing per SSDLC stage. Parallel execution of Policy and Evidence agents, followed by Drafter and Reviewer agents.

API-First, MCP & A2A Ready

Use the local React console for human review, integrate CI/CD pipelines through the REST API, or expose the same assessment capabilities to AI agents (Claude Desktop, Cursor, OpenClaw) through MCP. A2A-compatible platforms can delegate assessment tasks to DocSentinel as a specialist security agent.

Reproducible Evaluation Harness

DocSentinel includes an evals/ harness for measuring assessment quality against public and future golden datasets without going through HTTP or the UI. The first implemented path covers OWASP Benchmark v1.2 SAST triage:

• Dataset hygiene: raw benchmark data is fetched locally into ignored paths; only the fetch script, checksum, manifest, and tiny test fixture are committed.
• Direct pipeline execution: the runner calls AssessmentService.submit() with phase="testing" and skill_id="ssdlc-testing", matching production task semantics while staying self-contained.
• Hard-key scoring: M1 scores CWE-based binary triage with accuracy, precision, recall, F1, and false-positive rate, with no LLM judge.
• Threat grounding scoring: the Evidence Critic scorer measures verdict accuracy, supported precision/recall/F1, contradiction recall, abstention rate, citation validity, and the full verdict confusion matrix.
• Scorecards: every run writes machine-readable scorecard.json and a human-readable scorecard.md under evals/reports/<run_id>/.

python evals/datasets/owasp_benchmark/fetch.py
python -m evals.runner.run_eval \
  --dataset-id owasp_benchmark \
  --raw-dir evals/datasets/owasp_benchmark/raw/BenchmarkJava-master \
  --run-id local-owasp \
  --repeats 1

See docs/07-evaluation-plan.md for the full methodology and milestone roadmap.

Agent Architecture Development Roadmap

This roadmap turns the project's agent-safety principles into implementation work that can be delivered and reviewed incrementally. It is informed by Microsoft Learn's Designing Agent Architecture and SDLC Integration module and follows one governing rule: agents propose; deterministic policy and authorized humans accept.

The target control flow is:

Task contract -> Visible plan -> Bounded execution -> Objective evaluation
              -> Human/policy approval -> Published result

Status legend: [x] implemented baseline, [ ] planned work. Priorities indicate delivery order rather than release dates.

Current Baseline

• Phase-specific SSDLC skills and a shared assessment pipeline for REST, MCP, and A2A entry points.
• Human review is mandatory for agent-gateway submissions; external agents cannot approve their own work.
• MCP document-root confinement, token/loopback boundaries, bounded KB results, input guardrails, and LLM base-URL network checks.
• Structured reports with citations, document hashes, confidence, threat evidence verification, and remediation tracking.
• Reproducible evaluation harness with OWASP triage and threat-grounding scorecards.

M0 — Repository Governance (P0)

Goal: make the pull request the enforceable control point for human and agent-generated changes.

• Add CODEOWNERS for security-sensitive paths, including .github/workflows/, app/core/, app/agent_gateway/, migrations, deployment files, and policy packs.
• Expand the pull request template with required Goal, Scope, Plan, Success Criteria, Risk/Mitigations, Evidence, and Rollback/Escalation sections.
• Add a required Plan Gate workflow that validates meaningful PR fields and classifies changes as low, medium, high, or critical risk.
• Configure the main ruleset to require pull requests, passing CI and Plan Gate checks, resolved conversations, and CODEOWNERS approval for sensitive changes.
• Upload test, coverage, contract, and evaluation outputs as durable GitHub Actions artifacts named with the workflow run ID and commit SHA.

Exit criteria: a planless change or an unreviewed sensitive-path change cannot be merged into main.

M1 — Task Contract and Plan–Act–Evaluate Graph (P1)

Goal: make agent intent, authority, outputs, and success conditions inspectable before effects occur.

• Define a versioned AgentTaskContract schema containing goal, inputs, allowed paths/tools, expected outputs, success criteria, risk tier, approval mode, retry limit, and escalation owner.
• Persist the task contract and expose it through the assessment API and review console.
• Add an explicit read-only planning node and immutable PlanArtifact to the assessment graph.
• Implement a risk policy: low-risk tasks may use plan + execution in one review; workflow, authentication, infrastructure, and production-impacting tasks require plan-first approval.
• Separate evaluation from generation so schema validation, evidence grounding, deterministic rules, and evaluation thresholds remain independent of the drafting agent.
• Add transition and authorization tests proving that agents cannot skip the plan/evaluation stages or approve their own output.

Exit criteria: every assessment can answer what was requested, what the agent was allowed to do, what plan it followed, and which signals determined success.

M2 — Durable Execution and Reliability (P1)

Goal: make long-running assessments restart-safe, bounded, and recoverable.

• Replace in-memory assessment task state with database-backed task, revision, activity, and review records.
• Compile LangGraph workflows with a database-backed checkpointer using task_id as the stable execution/thread identifier.
• Make nodes idempotent and store node input/output hashes to prevent duplicate side effects during resume.
• Add bounded retries for transient failures only; after two repeated failures, stop and escalate with attempted actions, evidence, and a suggested human next step.
• Add cancellation, timeout, resume, and disaster-recovery tests.

Exit criteria: a process restart can resume an assessment without losing review history, duplicating effects, or entering an infinite retry loop.

M3 — Observability, Tools, MCP, and Secrets (P2)

Goal: make every meaningful agent action attributable and enforce least privilege independently of model instructions.

• Create an evidence manifest for each run containing task and graph-run IDs, node, source channel, document/output hashes, skill and policy-pack versions, model/prompt identifiers, citations, timestamps, and reviewer decision.
• Define capability profiles: planning and review agents are read-only; execution agents receive narrow allowlisted tools for the current task.
• Treat additions or expansions to MCP tools as high-risk governed changes with explicit security tests and owner review.
• Add pre-action policy hooks, post-action audit hooks, and error hooks for escalation; hooks must be able to block actions without relying on the LLM.
• Scope secrets to the smallest runtime component and environment, prevent secret values from entering prompts/artifacts, and document network-egress policy for hosted deployments.

Exit criteria: operators can determine who or what performed an action, against which inputs and code state, under which capability policy, and with what result.

M4 — Evaluation and Release Gates (P2)

Goal: turn assessment quality and production safety into objective release signals.

• Expand the evaluation harness across all SSDLC phases and add held-out expert-labelled cases with inter-annotator agreement.
• Version approved scorecard baselines and fail CI on agreed grounding, recall, false-positive, calibration, or latency regressions.
• Report variance across repeated runs and preserve judge rationales and human-audit results as reviewable artifacts.
• Require protected GitHub Environments and authorized reviewers for production-impacting exports or deployments; agents may prepare but not approve or execute the final release action.
• Deploy sensitive configuration from an explicit commit or tag rather than an unpinned branch head, with a tested rollback procedure.

Exit criteria: releases have reproducible quality evidence, an approved code reference, a human decision for critical effects, and a verified rollback path.

Definition of Done for Roadmap Work

Every roadmap PR must include:

• a bounded plan and changed-path scope;
• verifiable functional and security success criteria;
• automated tests for allowed and denied behavior;
• durable evidence or artifacts linked to the commit under test;
• documentation and migration notes where contracts or persistence change; and
• rollback and escalation guidance for high-risk changes.

Implementation should proceed in milestone order because M0 establishes the repository controls used to review M1–M4 safely. Each unchecked item is intended to be small enough to become a dedicated GitHub issue and pull request.

Agent Integration (MCP + A2A)

Use MCP to expose bounded tools to Claude Desktop, Cursor, coding agents, and workflow platforms. Use A2A to expose DocSentinel as a remote specialist agent. REST, MCP, and A2A submissions share the same task lifecycle and activity log. Agent-created assessments always remain drafts until a human reviewer approves them in the console.

MCP exposes five governed tools:

• submit_document_assessment
• get_assessment_status
• assess_document (compatibility tool)
• query_knowledge_base
• get_agent_gateway_status

The A2A Agent Card advertises assessment submission, assessment retrieval, and security knowledge query skills.

What can it do?

Once connected, you can ask your AI agent:

"Analyze the attached requirements.pdf for missing security requirements using DocSentinel."

"Run a STRIDE threat model on system-design.pdf using the Design Agent."

"Triage these SonarQube SAST findings and prioritize by risk."

Configuration Guide

1. Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "docsentinel": {
      "command": "/path/to/DocSentinel/.venv/bin/python",
      "args": ["/path/to/DocSentinel/app/mcp_server.py"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "CHROMA_PERSIST_DIR": "/absolute/path/to/data/chroma",
        "MCP_DOCUMENT_ROOTS": "/absolute/path/to/approved/documents"
      }
    }
  }
}

assess_document only reads files inside MCP_DOCUMENT_ROOTS. Use : to separate multiple roots on macOS/Linux, or ; on Windows. If unset, the server only allows ./examples.

FastAPI also serves Streamable HTTP MCP at /mcp/ and publishes the A2A Agent Card at /.well-known/agent-card.json. Tokenless protocol access is loopback-only. External agents cannot approve, reject, or bypass human review.

For network, Docker, or reverse-proxy access, configure:

AGENT_GATEWAY_TOKEN=generate-a-long-random-value
AGENT_GATEWAY_PUBLIC_URL=https://docsentinel.internal.example
AGENT_GATEWAY_ALLOWED_HOSTS=docsentinel.internal.example
AGENT_GATEWAY_ALLOWED_ORIGINS=https://trusted-agent-console.internal.example

Clients send Authorization: Bearer <AGENT_GATEWAY_TOKEN>. Keep MCP_DOCUMENT_ROOTS, allowed hosts, and allowed origins as narrow as possible. Docker bridge traffic is not treated as loopback, so published MCP/A2A ports require a token.

2. Cursor

1. Go to Settings > Features > MCP.
2. Click + Add New MCP Server.
   • Name: docsentinel
   • Type: stdio
   • Command: /path/to/DocSentinel/.venv/bin/python
   • Args: /path/to/DocSentinel/app/mcp_server.py

See full guide in docs/06-agent-integration.md.

Quick Start

Option A: One-Click Deployment (Recommended)

git clone https://github.com/arthurpanhku/DocSentinel.git
cd DocSentinel
chmod +x deploy.sh
./deploy.sh

• React Console: http://localhost:8000/console
• Agent Integrations: http://localhost:8000/console/integrations
• API Docs: http://localhost:8000/api-docs
• A2A Agent Card: http://localhost:8000/.well-known/agent-card.json

To connect an external agent through the published Docker port, set AGENT_GATEWAY_TOKEN in .env before running ./deploy.sh.

Option B: Manual Setup

Prerequisites: Python 3.11+ and Node.js 20+. Optional: Ollama (ollama pull llama2).

git clone https://github.com/arthurpanhku/DocSentinel.git
cd DocSentinel
python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env        # Edit if needed: LLM_PROVIDER=ollama or openai
npm install --prefix frontend
npm run build --prefix frontend
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

DeepSeek example

Set these values in .env, or use the Settings page after the server is running. Never commit real API keys.

LLM_PROVIDER=deepseek
DEEPSEEK_BASE_URL=https://api.deepseek.com
DEEPSEEK_MODEL=deepseek-chat
DEEPSEEK_API_KEY=sk-your-deepseek-key

• API docs: http://localhost:8000/api-docs · Health: http://localhost:8000/health
• React Console: http://localhost:8000/console
• Agent Integrations: http://localhost:8000/console/integrations
• MCP Streamable HTTP: http://localhost:8000/mcp/
• A2A Agent Card: http://localhost:8000/.well-known/agent-card.json
• Legacy Review Console (HITL): http://localhost:8000/docs/review-console.html

React Console

DocSentinel includes a React + TypeScript + Vite + Tailwind CSS console for assessments, knowledge base operations, skills, and system status.

npm install --prefix frontend
npm run build --prefix frontend
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Open http://localhost:8000/console. For frontend-only development, run:

npm run dev --prefix frontend

The Vite dev server proxies /api, /health, and /config to http://localhost:8000.

The Settings page can update the running server's LLM provider, model, base URL, and API key. API keys are only returned to the UI as masked previews. For persistent startup defaults, set the matching values in .env.

Example: Submit an SSDLC assessment

# Run a Design phase assessment (threat modeling)
curl -X POST "http://localhost:8000/api/v1/assessments" \
  -F "files=@examples/architecture-doc.pdf" \
  -F "phase=design" \
  -F "scenario_id=threat-modeling"

# Response: { "task_id": "...", "status": "accepted" }
# Get the result
curl "http://localhost:8000/api/v1/assessments/TASK_ID"

Example: Upload to KB and query

# Upload a security policy to the requirements KB collection
curl -X POST "http://localhost:8000/api/v1/kb/documents" \
  -F "file=@examples/sample-policy.txt" \
  -F "collection=kb_requirements"

# Query the KB (RAG)
curl -X POST "http://localhost:8000/api/v1/kb/query" \
  -H "Content-Type: application/json" \
  -d '{"query": "What are the access control requirements?", "top_k": 5}'

Hosted deployment

A hosted deployment is available on Fronteir AI.

Project Layout

DocSentinel/
├── frontend/             # React + TypeScript + Vite + Tailwind console
├── app/                  # Application code
│   ├── api/              # REST routes: assessments, KB, health, skills
│   ├── agent_gateway/    # MCP/A2A adapters, auth boundary, Agent Card
│   ├── agent/            # LangGraph orchestrator, phase agents, skills
│   │   ├── orchestrator.py    # LangGraph state machine & phase routing
│   │   ├── agents/            # Phase-specific agent implementations
│   │   ├── ssdlc/             # SSDLC pipeline: stage router, stage skills, checklists
│   │   ├── skills_registry.py # Built-in skills per SSDLC phase
│   │   └── skills_service.py  # Skill CRUD and management
│   ├── core/             # Config, guardrails, security, DB
│   ├── kb/               # Knowledge Base (Chroma + LightRAG graph RAG)
│   ├── llm/              # LangChain LLM abstraction (OpenAI, Ollama)
│   ├── parser/           # Document parsing (Docling + SAST/DAST + legacy)
│   ├── models/           # Pydantic / SQLModel models
│   ├── services/         # Shared assessment task lifecycle
│   ├── main.py           # FastAPI app entry point
│   └── mcp_server.py     # MCP stdio + Streamable HTTP tools
├── tests/                # Automated tests (pytest)
├── evals/                # Reproducible quality eval harness and scorecards
├── examples/             # Sample files (questionnaires, policies, reports)
├── docs/                 # Design & Spec documentation
│   ├── 01-architecture-and-tech-stack.md
│   ├── 02-api-specification.yaml
│   ├── 03-assessment-report-and-skill-contract.md
│   ├── 04-integration-guide.md
│   ├── 05-deployment-runbook.md
│   ├── 06-agent-integration.md
│   ├── 07-evaluation-plan.md
│   └── schemas/
├── .github/              # Issue/PR templates, CI (Actions)
├── Dockerfile
├── docker-compose.yml
├── docker-compose.ollama.yml
├── CONTRIBUTING.md
├── CODE_OF_CONDUCT.md
├── CHANGELOG.md
├── SPEC.md               # PRD with SSDLC phase definitions
├── ARCHITECTURE.md        # System architecture with LangGraph design
├── LICENSE
├── SECURITY.md
├── requirements.txt
├── requirements-dev.txt
└── .env.example

Configuration

See .env.example and docs/05-deployment-runbook.md for full options.

Tech Stack

Documentation and PRD

• ARCHITECTURE.md — System architecture: LangGraph design, SSDLC agents, data flow, deployment.
• SPEC.md — Product requirements: SSDLC phases, features, security controls.
• CHANGELOG.md — Version history; Releases.
• Design docs docs/: Architecture, API spec (OpenAPI), contracts, integration guides, deployment runbook.
• docs/07-evaluation-plan.md — Evaluation methodology and milestone plan for evals/.

Development & Testing

Option A: One-Click Test (Recommended)

chmod +x test_integration.sh
./test_integration.sh

Option B: Manual

pip install -r requirements-dev.txt
pytest
pytest tests/test_skills_api.py   # Run specific test
pytest evals/tests                # Run evaluation harness tests

Contributing

Issues and Pull Requests are welcome. Please read CONTRIBUTING.md for setup, tests, and commit guidelines. By participating you agree to the CODE_OF_CONDUCT.md.

AI-Assisted Contribution: We encourage using AI tools to contribute! Check out CONTRIBUTING_WITH_AI.md for best practices.

Submit a Skill Template: Have a great security persona for an SSDLC phase? Submit a Skill Template or add it to examples/templates/.

Security

• Vulnerability reporting: See SECURITY.md for responsible disclosure.
• Security requirements: Follows security controls in SPEC §7.2.
• Document confinement: MCP/A2A document paths must resolve inside MCP_DOCUMENT_ROOTS; symlink escapes are rejected before file reads.
• Agent authority: External agents may submit and inspect drafts, but cannot approve their own assessments.
• Remote access: Tokenless MCP/A2A access is loopback-only. Network deployments require a bearer token, TLS, and narrow Host/Origin allow-lists.

License

This project is licensed under the MIT License — see the LICENSE file for details.

Star History

Author and Links

• Author: PAN CHAO (Arthur Pan)
• Repository: github.com/arthurpanhku/DocSentinel
• SPEC and design docs: See links above.

If you use DocSentinel in your organization or contribute back, we'd love to hear from you (e.g. via GitHub Discussions or Issues).