Build the three foundational CLI tools that form Conduit's core pipeline. `conduit parse` implements full Rholang and MeTTa parsers producing JSON ASTs with source location tracking, concurrent process structure annotations (Rholang), and type inference results (MeTTa). The parser handles error recovery so partial files produce partial ASTs instead of failures. `conduit verify` implements four verification levels: type checking (MeTTa dependent types, Rholang behavioral types), concurrency analysis (deadlock/race detection), cross-language boundary checking, and user-specified property assertions with counterexample generation. `conduit deploy` implements DevNet deployment with compilation, signing, submission, BlockDAG position tracking, CBC Casper finality monitoring, multi-contract dependency resolution, and structured JSON deployment receipts. All three tools follow Unix conventions: structured JSON output, standard exit codes, stdin/stdout piping, and composable flags. Each tool includes comprehensive --help documentation and man-page-style references.

1. `conduit parse` for Rholang: full process algebra parser with error recovery, source location tracking, concurrent structure annotations, and JSON AST output compatible with jq and standard JSON tools 2. `conduit parse` for MeTTa: atom expression parser with type annotations, grounded atom resolution, Atomspace binding info, and pattern match structure in JSON AST 3. `conduit verify` Level 1-2: MeTTa dependent type checking against Atomspace type system, and Rholang concurrency analysis detecting deadlocks, race conditions, and channel misuse 4. `conduit verify` Level 3-4: cross-language MeTTa-Rholang boundary type checking, and user-specified property assertions with counterexample generation for violated properties 5. `conduit deploy`: one-command DevNet deployment with compilation, signing, submission, BlockDAG position tracking, and CBC Casper finality monitoring with configurable thresholds 6. Multi-contract deployment with automatic dependency resolution via channel reference analysis, rollback support, and structured JSON deployment receipts 7. Unix-standard I/O: all tools support JSON stdin/stdout piping, standard exit codes, composable flags, and man-page-style --help 8. Test suite: 100+ test cases across all three tools covering parsing edge cases, verification accuracy, and deployment scenarios 9. All code published on GitHub under MIT license with CI/CD pipeline and contributor documentation

$10,000 USD

1. `conduit parse` correctly parses 95%+ of Rholang programs from the official examples repository, with error recovery producing partial ASTs for incomplete code 2. `conduit parse` correctly parses 95%+ of MeTTa programs from the Hyperon examples, including grounded atom types and Atomspace query patterns 3. `conduit verify` detects 4 of 5 known deadlock patterns and 3 of 5 known race conditions in test Rholang programs 4. `conduit verify` cross-language check catches type mismatches and channel naming inconsistencies at the MeTTa-Rholang boundary 5. Property checking generates valid, minimal counterexamples for 3 of 5 violated properties in test contracts 6. `conduit deploy` successfully deploys and confirms 10 different contract types on DevNet with complete deployment receipts 7. All tools produce valid JSON output that pipes correctly through `jq`, and all exit codes follow Unix conventions 8. Full pipeline `parse | verify && deploy` completes in <30 seconds for standard contracts on DevNet 9. External developer composes a 3-step pipeline using only --help output, without reading additional documentation

Build the testing framework and AI code intelligence tool. `conduit test` implements four testing modes: unit tests (isolated process testing with mock channels), integration tests (multi-contract interaction on local DevNet), consensus tests (behavioral testing under simulated CBC Casper conditions with multiple BlockDAG orderings), and fuzzing (random input and message ordering generation). Test results are structured JSON with coverage tracking across Rholang processes and MeTTa inference paths. `conduit ai` implements five capabilities: code generation from natural language, code review with concurrency analysis, code explanation for selected regions, pattern search from a curated library of 50+ Rholang/MeTTa patterns, and refactoring suggestions. All AI inference runs on Singularity Compute. The AI is specialized for Rholang process algebra and MeTTa Atomspace patterns — it understands channel semantics, concurrent composition, and behavioral types, not just surface syntax. Both tools integrate with the existing parse/verify/deploy pipeline through standard JSON I/O.

1. `conduit test` unit testing: Rholang process isolation with mock channel injection, assertion library, and structured JSON test results 2. `conduit test` integration testing: multi-contract interaction testing on local DevNet node with automatic setup and teardown 3. `conduit test` consensus testing: behavioral tests under 10+ simulated BlockDAG orderings with CBC Casper finality variation 4. `conduit test` fuzzing: random input and message ordering generation with configurable iterations, seed control, and minimized failing case output 5. Test coverage tracking: process-level Rholang coverage and inference-path-level MeTTa coverage with JSON and HTML report output 6. `conduit ai generate`: natural language to Rholang/MeTTa code generation with process-algebra-aware output 7. `conduit ai review`: automated code review identifying concurrency issues, channel misuse, and optimization opportunities 8. `conduit ai explain`: code section explanation with concurrency analysis; `conduit ai patterns`: searchable library of 50+ verified patterns 9. Singularity Compute integration: all AI inference runs on decentralized compute with local fallback for offline use 10. 50+ curated and verified patterns covering tokens, governance, oracles, agents, and multi-party coordination protocols

$33,000 USD

1. Unit tests correctly isolate individual Rholang processes with mock channel injection, preventing cross-test state leakage 2. Integration tests catch 4 of 5 known multi-contract interaction bugs in a curated test suite of 15+ interaction scenarios 3. Consensus tests detect behavior differences across 3 of 5 known ordering-sensitive contracts when run with varied BlockDAG orderings 4. Fuzzer discovers at least 2 previously unknown edge cases in the test contract set with reproducible failing seeds 5. Coverage reports accurately track process-level and inference-path-level execution, outputting both JSON and human-readable HTML 6. AI generates compilable Rholang for 8 of 10 natural language descriptions, with correct concurrent process composition 7. AI review correctly identifies concurrency issues (deadlocks, races, channel misuse) in 4 of 5 test programs 8. Singularity Compute processes AI requests with <10 second average response time, with graceful local fallback 9. Complete pipeline `ai generate | verify | test` works end-to-end without manual intervention for 5 test scenarios

Polish the complete toolkit for production use, create CI/CD integration templates, conduct user testing, and launch to the ASI:Chain ecosystem. Integration work includes ensuring all five tools compose seamlessly through pipes and JSON, adding global configuration management (`conduit config`), and creating workspace-level project files (`conduit.toml`) for multi-contract project settings. CI/CD templates provide ready-to-use GitHub Actions, GitLab CI, and generic shell script configurations for automated build-verify-test-deploy pipelines. Editor integration provides configuration snippets for VS Code tasks, Neovim commands, and Emacs compile mode. User testing recruits 5+ ASI:Chain developers to use Conduit for real projects, collecting feedback on CLI ergonomics, error messages, and pipeline composition patterns. Documentation includes a comprehensive CLI reference, a pipeline cookbook with 20+ composition recipes, and a tutorial series for new developers. Production hardening covers error handling, offline mode for parse/verify, and graceful degradation when Singularity Compute or DevNet are unavailable.

1. Global configuration management: `conduit config` for API keys, network settings, defaults 2. Project files: `conduit.toml` for multi-contract project configuration and dependency specs 3. CI/CD templates: GitHub Actions, GitLab CI, and shell script configurations 4. Editor snippets: VS Code tasks.json, Neovim commands, Emacs compile-mode configurations 5. User testing report: feedback from 5+ ASI:Chain developers using Conduit for real projects 6. CLI reference documentation: all commands, flags, output formats, and exit codes 7. Pipeline cookbook: 20+ composition recipes for common development workflows 8. Tutorial series: 3 guided walkthroughs from installation to DevNet deployment 9. npm/cargo packages published for cross-platform installation 10. Production hardening: offline mode, graceful degradation, comprehensive error messages

$4,000 USD

1. All five tools compose correctly through pipes for 20+ documented pipeline patterns 2. CI/CD templates run successfully in GitHub Actions and GitLab CI test environments 3. Editor integration works in VS Code, Neovim, and Emacs without custom configuration 4. 5 external developers successfully build and deploy contracts using Conduit pipelines 5. User satisfaction score of 4+/5 on CLI ergonomics and error message quality 6. Documentation enables a new developer to install and run a full pipeline within 20 minutes 7. Offline mode correctly handles parse and verify operations without network access 8. Cross-platform installation works on Linux, macOS, and Windows via npm/cargo 9. Total toolkit install size is under 50MB (keeping the Unix philosophy of small tools)