Tiger CoWork v0.7.1

A self-hosted AI workspace with chat, code execution, parallel multi-agent orchestration, cross-machine agent connection, and a skill marketplace. Mix different AI providers in the same agent team — OpenAI-compatible APIs, Claude Code CLI, and Codex CLI. Connect agents across machines on your network so distributed teams can collaborate in real time. Connect external MCP servers to extend the AI's toolbox. Built with 16 built-in tools and designed for long-running sessions with smart context compression and checkpoint recovery.

What's New in v0.7.1

• Context Compaction Reliability — fixed a class of "chat content is empty (2013)" errors that surfaced as Context overflow after 3 compression retries in long pipeline runs. Emergency retries now bypass the 60-second compaction cooldown; trimConversationContext is tool-pair aware (assistant tool_calls and their matching tool results are dropped as a unit so nothing gets orphaned); a new truncateLargestToolResult helper targets the single biggest tool message on retries 2–3 instead of halving the whole transcript; and a structural validateMessageStructure pass runs after every compression/trim to drop any orphan that slipped through.
• Auto-Skill Run Visibility — when the synthesiser returned {"proposals":[]}, the run summary previously showed created=0 updated=0 skipped=0 candidates=N with no indication of why nothing happened. Empty-proposal outcomes now surface in skillAutoUpdateLastRunSummary as LLM returned no proposals for N session(s), and the LLM's actual reply head is logged to the server console for debugging.
• Stricter looksEmpty Heuristic — the prose-fallback that coerces a refusal-shaped reply (e.g. "no skills worth capturing") into proposals=[] now requires the whole trimmed reply to be under 200 characters. Previously a long, malformed proposal that happened to contain "no skill" inside its rationale could be silently swallowed.

What's New in v0.7.0

• Automatic Skill Generation — successful chats are mined on a cron (or via Run Now) to synthesise reusable SKILL.md workflows. The synthesiser proposes new skills or refines existing auto-generated ones; updates land as a side-by-side SKILL.md.proposed diff for review before going live. See Automatic Skill Generation for the full algorithm and settings.
• Skill Approval Workflow — pending auto skills surface in the Skills page with an Approve/Reject control. Approval renames the proposed file to SKILL.md and enables it; the next user turn in any chat picks it up automatically (the system prompt is rebuilt per turn — no restart needed).
• Per-Message Feedback Loop — opt-in 👍 / 👎 + comment on every assistant reply. 👍 tells the synthesiser "capture this procedure"; 👎 with a comment triggers a focused remediation pass that picks the auto-skill most likely responsible (chat-log-driven targeting first, token-overlap fallback) and rewrites just that one skill. 👎 alone is treated as "do not distil from this chat."
• Sub-Agent–Aware Auto-Skills — the pipeline now parses data/chat_logs/<sessionId>.log to surface a per-agent workflow trace (task, tools used, skills loaded). For multi-agent chats this fixes a previous gap where only the orchestrator's merged reply was visible to the synthesiser; remediation can now identify and rewrite the exact auto-skill a sub-agent loaded, and synthesis can capture the agent topology in the SKILL.md body — not just the outcome.

What's New in v0.6.1

• Per-Project Agent Mode Override — each project can override the global sub-agent mode (Auto Spawn, Auto Create, Manual, Realtime, Auto Swarm) and pick its own YAML config, architecture type, agent count, and connection protocols. The active override is shown as a clickable purple tag in the project header and chat banner.
• **Auto Architecture — AI-De

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