terraphim / terraphim/terraphim-ai

Phase 2: Add Validation Pipeline via Hooks (Week 2)

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Dominant language
Rust
Stars
62
Forks
5
Avg merge
2h 27m
Merged PRs (30d)
1

Description

Part of Epic #270

Objective

Implement 4-layer validation pipeline using rust-genai + MCP hooks.

Tasks

1. Wrap rust-genai Client with Validation

Location: crates/terraphim_multi_agent/src/genai_llm_client.rs

Create ValidatedGenAiClient with:

  • Pre-LLM validators (context, permissions, token budget)
  • Post-LLM validators (parsing, confidence, security)
2. Add Hooks to MCP Server

Location: crates/terraphim_mcp_server/src/lib.rs

  • Pre-tool validation hooks
  • Post-tool validation hooks
  • Use existing permission system
3. Implement Knowledge-Graph-Based Security

Location: crates/terraphim_rolegraph/src/repository_security.rs

  • RepositorySecurityGraph structure
  • Command matching via automata
  • Synonym resolution via thesaurus
  • Learning system for user decisions
4. Command Extraction

Location: crates/terraphim_automata/src/command_matcher.rs

Extract commands from LLM output:

  • Backticks
  • Code blocks
  • Shell prompts
  • Natural language

Success Criteria

  • 4-layer validation working
  • Commands validated in <10µs
  • Security graph per repository
  • Learning system reduces prompts

Time: 1 week

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reading the four named entry points: genai_llm_client.rs, the MCP server lib.rs, repository_security.rs, and command_matcher.rs, along with epic #270. Trace how validation, permissions, command extraction, and learning are currently represented before planning the cross-cutting changes. Done means the four-layer pipeline works, commands validate in under 10µs, each repository has a security graph, and learning reduces prompts.

Written by the indexing model from the issue text.

Assessment

Tech stack
rust
Domain
ai, backend-api-design, security
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
25/100

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