2026-02-16 00:30:41 +00:00
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module forge.lthn.ai/core/cli/internal/bugseti
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feat: BugSETI app, WebSocket hub, browser automation, and MCP tools (#336)
* feat: add security logging and fix framework regressions
This commit implements comprehensive security event logging and resolves critical regressions in the core framework.
Security Logging:
- Enhanced `pkg/log` with a `Security` level and helper.
- Added `log.Username()` to consistently identify the executing user.
- Instrumented GitHub CLI auth, Agentic configuration, filesystem sandbox, MCP handlers, and MCP TCP transport with security logs.
- Added `SecurityStyle` to the CLI for consistent visual representation of security events.
UniFi Security (CodeQL):
- Refactored `pkg/unifi` to remove hardcoded `InsecureSkipVerify`, resolving a high-severity alert.
- Added a `--verify-tls` flag and configuration option to control TLS verification.
- Updated command handlers to support the new verification parameter.
Framework Fixes:
- Restored original signatures for `MustServiceFor`, `Config()`, and `Display()` in `pkg/framework/core`, which had been corrupted during a merge.
- Fixed `pkg/framework/framework.go` and `pkg/framework/core/runtime_pkg.go` to match the restored signatures.
- These fixes resolve project-wide compilation errors caused by the signature mismatches.
I encountered significant blockers due to a corrupted state of the `dev` branch after a merge, which introduced breaking changes in the core framework's DI system. I had to manually reconcile these signatures with the expected usage across the codebase to restore build stability.
* feat(mcp): add RAG tools (query, ingest, collections)
Add vector database tools to the MCP server for RAG operations:
- rag_query: Search for relevant documentation using semantic similarity
- rag_ingest: Ingest files or directories into the vector database
- rag_collections: List available collections
Uses existing internal/cmd/rag exports (QueryDocs, IngestDirectory, IngestFile)
and pkg/rag for Qdrant client access. Default collection is "hostuk-docs"
with topK=5 for queries.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat(mcp): add metrics tools (record, query)
Add MCP tools for recording and querying AI/security metrics events.
The metrics_record tool writes events to daily JSONL files, and the
metrics_query tool provides aggregated statistics by type, repo, and agent.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: add 'core mcp serve' command
Add CLI command to start the MCP server for AI tool integration.
- Create internal/cmd/mcpcmd package with serve subcommand
- Support --workspace flag for directory restriction
- Handle SIGINT/SIGTERM for clean shutdown
- Register in full.go build variant
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat(ws): add WebSocket hub package for real-time streaming
Add pkg/ws package implementing a hub pattern for WebSocket connections:
- Hub manages client connections, broadcasts, and channel subscriptions
- Client struct represents connected WebSocket clients
- Message types: process_output, process_status, event, error, ping/pong
- Channel-based subscription system (subscribe/unsubscribe)
- SendProcessOutput and SendProcessStatus for process streaming integration
- Full test coverage including concurrency tests
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat(mcp): add process management and WebSocket MCP tools
Add MCP tools for process management:
- process_start: Start a new external process
- process_stop: Gracefully stop a running process
- process_kill: Force kill a process
- process_list: List all managed processes
- process_output: Get captured process output
- process_input: Send input to process stdin
Add MCP tools for WebSocket:
- ws_start: Start WebSocket server for real-time streaming
- ws_info: Get hub statistics (clients, channels)
Update Service struct with optional process.Service and ws.Hub fields,
new WithProcessService and WithWSHub options, getter methods, and
Shutdown method for cleanup.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat(webview): add browser automation package via Chrome DevTools Protocol
Add pkg/webview package for browser automation:
- webview.go: Main interface with Connect, Navigate, Click, Type, QuerySelector, Screenshot, Evaluate
- cdp.go: Chrome DevTools Protocol WebSocket client implementation
- actions.go: DOM action types (Click, Type, Hover, Scroll, etc.) and ActionSequence builder
- console.go: Console message capture and filtering with ConsoleWatcher and ExceptionWatcher
- angular.go: Angular-specific helpers for router navigation, component access, and Zone.js stability
Add MCP tools for webview:
- webview_connect/disconnect: Connection management
- webview_navigate: Page navigation
- webview_click/type/query/wait: DOM interaction
- webview_console: Console output capture
- webview_eval: JavaScript execution
- webview_screenshot: Screenshot capture
Add documentation:
- docs/mcp/angular-testing.md: Guide for Angular application testing
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* docs: document new packages and BugSETI application
- Update CLAUDE.md with documentation for:
- pkg/ws (WebSocket hub for real-time streaming)
- pkg/webview (Browser automation via CDP)
- pkg/mcp (MCP server tools: process, ws, webview)
- BugSETI application overview
- Add comprehensive README for BugSETI with:
- Installation and configuration guide
- Usage workflow documentation
- Architecture overview
- Contributing guidelines
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat(bugseti): add BugSETI system tray app with auto-update
BugSETI - Distributed Bug Fixing like SETI@home but for code
Features:
- System tray app with Wails v3
- GitHub issue fetching with label filters
- Issue queue with priority management
- AI context seeding via seed-agent-developer skill
- Automated PR submission flow
- Stats tracking and leaderboard
- Cross-platform notifications
- Self-updating with stable/beta/nightly channels
Includes:
- cmd/bugseti: Main application with Angular frontend
- internal/bugseti: Core services (fetcher, queue, seeder, submit, config, stats, notify)
- internal/bugseti/updater: Auto-update system (checker, downloader, installer)
- .github/workflows/bugseti-release.yml: CI/CD for all platforms
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* fix: resolve import cycle and code duplication
- Remove pkg/log import from pkg/io/local to break import cycle
(pkg/log/rotation.go imports pkg/io, creating circular dependency)
- Use stderr logging for security events in sandbox escape detection
- Remove unused sync/atomic import from core.go
- Fix duplicate LogSecurity function declarations in cli/log.go
- Update workspace/service.go Crypt() call to match interface
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* fix: update tests for new function signatures and format code
- Update core_test.go: Config(), Display() now panic instead of returning error
- Update runtime_pkg_test.go: sr.Config() now panics instead of returning error
- Update MustServiceFor tests to use assert.Panics
- Format BugSETI, MCP tools, and webview packages with gofmt
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---------
Co-authored-by: Snider <631881+Snider@users.noreply.github.com>
Co-authored-by: Claude <developers@lethean.io>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-02-05 17:22:05 +00:00
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go 1.25.5
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2026-02-05 21:36:33 +00:00
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2026-02-10 15:23:35 +00:00
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require (
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feat: add ML inference, scoring, and training pipeline (pkg/ml)
Port LEM scoring/training pipeline into CoreGo as pkg/ml with:
- Inference abstraction with HTTP, llama-server, and Ollama backends
- 3-tier scoring engine (heuristic, exact, LLM judge)
- Capability and content probes for model evaluation
- GGUF/safetensors format converters, MLX to PEFT adapter conversion
- DuckDB integration for training data pipeline
- InfluxDB metrics for lab dashboard
- Training data export (JSONL + Parquet)
- Expansion generation pipeline with distributed workers
- 10 CLI commands under 'core ml' (score, probe, export, expand, status, gguf, convert, agent, worker)
- 5 MCP tools (ml_generate, ml_score, ml_probe, ml_status, ml_backends)
All 37 ML tests passing. Binary builds at 138MB with all commands.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 00:34:53 +00:00
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codeberg.org/mvdkleijn/forgejo-sdk/forgejo/v2 v2.2.0
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2026-02-10 15:23:35 +00:00
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github.com/mark3labs/mcp-go v0.43.2
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feat: add ML inference, scoring, and training pipeline (pkg/ml)
Port LEM scoring/training pipeline into CoreGo as pkg/ml with:
- Inference abstraction with HTTP, llama-server, and Ollama backends
- 3-tier scoring engine (heuristic, exact, LLM judge)
- Capability and content probes for model evaluation
- GGUF/safetensors format converters, MLX to PEFT adapter conversion
- DuckDB integration for training data pipeline
- InfluxDB metrics for lab dashboard
- Training data export (JSONL + Parquet)
- Expansion generation pipeline with distributed workers
- 10 CLI commands under 'core ml' (score, probe, export, expand, status, gguf, convert, agent, worker)
- 5 MCP tools (ml_generate, ml_score, ml_probe, ml_status, ml_backends)
All 37 ML tests passing. Binary builds at 138MB with all commands.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 00:34:53 +00:00
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github.com/stretchr/testify v1.11.1
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2026-02-10 15:23:35 +00:00
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)
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2026-02-05 21:36:33 +00:00
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require (
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feat: add ML inference, scoring, and training pipeline (pkg/ml)
Port LEM scoring/training pipeline into CoreGo as pkg/ml with:
- Inference abstraction with HTTP, llama-server, and Ollama backends
- 3-tier scoring engine (heuristic, exact, LLM judge)
- Capability and content probes for model evaluation
- GGUF/safetensors format converters, MLX to PEFT adapter conversion
- DuckDB integration for training data pipeline
- InfluxDB metrics for lab dashboard
- Training data export (JSONL + Parquet)
- Expansion generation pipeline with distributed workers
- 10 CLI commands under 'core ml' (score, probe, export, expand, status, gguf, convert, agent, worker)
- 5 MCP tools (ml_generate, ml_score, ml_probe, ml_status, ml_backends)
All 37 ML tests passing. Binary builds at 138MB with all commands.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 00:34:53 +00:00
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github.com/42wim/httpsig v1.2.3 // indirect
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2026-02-05 21:36:33 +00:00
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github.com/bahlo/generic-list-go v0.2.0 // indirect
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github.com/buger/jsonparser v1.1.1 // indirect
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feat: add ML inference, scoring, and training pipeline (pkg/ml)
Port LEM scoring/training pipeline into CoreGo as pkg/ml with:
- Inference abstraction with HTTP, llama-server, and Ollama backends
- 3-tier scoring engine (heuristic, exact, LLM judge)
- Capability and content probes for model evaluation
- GGUF/safetensors format converters, MLX to PEFT adapter conversion
- DuckDB integration for training data pipeline
- InfluxDB metrics for lab dashboard
- Training data export (JSONL + Parquet)
- Expansion generation pipeline with distributed workers
- 10 CLI commands under 'core ml' (score, probe, export, expand, status, gguf, convert, agent, worker)
- 5 MCP tools (ml_generate, ml_score, ml_probe, ml_status, ml_backends)
All 37 ML tests passing. Binary builds at 138MB with all commands.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 00:34:53 +00:00
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github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc // indirect
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github.com/davidmz/go-pageant v1.0.2 // indirect
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github.com/go-fed/httpsig v1.1.0 // indirect
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github.com/google/go-cmp v0.7.0 // indirect
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2026-02-05 21:36:33 +00:00
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github.com/google/uuid v1.6.0 // indirect
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feat: add ML inference, scoring, and training pipeline (pkg/ml)
Port LEM scoring/training pipeline into CoreGo as pkg/ml with:
- Inference abstraction with HTTP, llama-server, and Ollama backends
- 3-tier scoring engine (heuristic, exact, LLM judge)
- Capability and content probes for model evaluation
- GGUF/safetensors format converters, MLX to PEFT adapter conversion
- DuckDB integration for training data pipeline
- InfluxDB metrics for lab dashboard
- Training data export (JSONL + Parquet)
- Expansion generation pipeline with distributed workers
- 10 CLI commands under 'core ml' (score, probe, export, expand, status, gguf, convert, agent, worker)
- 5 MCP tools (ml_generate, ml_score, ml_probe, ml_status, ml_backends)
All 37 ML tests passing. Binary builds at 138MB with all commands.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 00:34:53 +00:00
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github.com/hashicorp/go-version v1.7.0 // indirect
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2026-02-05 21:36:33 +00:00
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github.com/invopop/jsonschema v0.13.0 // indirect
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feat: add ML inference, scoring, and training pipeline (pkg/ml)
Port LEM scoring/training pipeline into CoreGo as pkg/ml with:
- Inference abstraction with HTTP, llama-server, and Ollama backends
- 3-tier scoring engine (heuristic, exact, LLM judge)
- Capability and content probes for model evaluation
- GGUF/safetensors format converters, MLX to PEFT adapter conversion
- DuckDB integration for training data pipeline
- InfluxDB metrics for lab dashboard
- Training data export (JSONL + Parquet)
- Expansion generation pipeline with distributed workers
- 10 CLI commands under 'core ml' (score, probe, export, expand, status, gguf, convert, agent, worker)
- 5 MCP tools (ml_generate, ml_score, ml_probe, ml_status, ml_backends)
All 37 ML tests passing. Binary builds at 138MB with all commands.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 00:34:53 +00:00
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github.com/mailru/easyjson v0.9.1 // indirect
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github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2 // indirect
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github.com/rogpeppe/go-internal v1.14.1 // indirect
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github.com/spf13/cast v1.10.0 // indirect
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2026-02-05 21:36:33 +00:00
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github.com/wk8/go-ordered-map/v2 v2.1.8 // indirect
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github.com/yosida95/uritemplate/v3 v3.0.2 // indirect
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feat: add ML inference, scoring, and training pipeline (pkg/ml)
Port LEM scoring/training pipeline into CoreGo as pkg/ml with:
- Inference abstraction with HTTP, llama-server, and Ollama backends
- 3-tier scoring engine (heuristic, exact, LLM judge)
- Capability and content probes for model evaluation
- GGUF/safetensors format converters, MLX to PEFT adapter conversion
- DuckDB integration for training data pipeline
- InfluxDB metrics for lab dashboard
- Training data export (JSONL + Parquet)
- Expansion generation pipeline with distributed workers
- 10 CLI commands under 'core ml' (score, probe, export, expand, status, gguf, convert, agent, worker)
- 5 MCP tools (ml_generate, ml_score, ml_probe, ml_status, ml_backends)
All 37 ML tests passing. Binary builds at 138MB with all commands.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 00:34:53 +00:00
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golang.org/x/crypto v0.47.0 // indirect
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golang.org/x/sys v0.40.0 // indirect
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gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c // indirect
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2026-02-05 21:36:33 +00:00
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gopkg.in/yaml.v3 v3.0.1 // indirect
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)
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