Autonomous AI Agents

Provided by TippyEntertainment

https://github.com/tippyentertainment/skills.git

This skill is designed for use on the Tasking.tech agent platform (https://tasking.tech) and is also compatible with assistant runtimes that accept skill-style handlers such as .claude, .openai, and .mistral. Use this skill for both Claude code and Tasking.tech agent source.

goals:

• Allow multiple agents to work concurrently on the same project or session.
• Route user input (text/voice) to the right agent or group of agents.
• Coordinate shared edits to files and folders through explicit turn-taking.
• Maintain a clear, reviewable history of which agent changed what and why.
• Use AI chat / team chat / collab chat for agent-to-agent coordination instead of silent edits.

non_goals:

• Replacing all single-agent skills; this is an orchestration layer on top.
• Building new domain skills (e.g., new coding, design, or research capabilities).
• Directly managing infrastructure beyond what’s exposed by tools.
• Making irreversible changes without logging or an option for human review.

roles:

• coordinator: description: > Supervises all agents, assigns tasks, manages shared-resource schedules, and resolves conflicts. Decides which agent has the "current turn" on a file and uses shared chat channels to announce plans and decisions.
• worker: description: > Performs focused tasks (coding, refactors, UI spacing, documentation, research, etc.) within the boundaries set by the coordinator and shared-resource rules, and communicates via AI chat / team chat / collab chat.
• reviewer: description: > Reviews diffs, checks for conflicts, and recommends merges or rollbacks before changes are finalized or deployed, summarizing outcomes in shared chat.

triggers:

• The user asks for several things at once across the same codebase.
• Multiple agents are enabled on the same repo or folder tree.
• There is a risk that agents will edit the same files or features at overlapping times.
• Voice and text inputs should be routed to specific agents without chaos.

resources:

• source_code_repos
• project_files_and_folders
• conversation_threads (user ↔ agents, agent ↔ agent)
• voice_input_output_streams
• logs / change history

communication_channels:

• ai_chat: description: > Per-agent or per-task chat where an agent can reason, ask clarifying questions, and receive instructions from the user or coordinator.
• team_chat: description: > Shared room where multiple agents (and optionally the user) coordinate plans, announce what they’re working on, and negotiate edit turns on shared files.
• collab_chat: description: > Task-focused collaboration threads used when several agents are working on the same feature or area; includes quick status updates and mini-plans.

coordination_model:

• shared_resources:
  • Files and folders are shared by default; multiple agents may intend to work on them.
  • The coordinator controls “edit turns” rather than hard exclusive locks.
  • Only one agent at a time should be actively writing to a given file, but others may read, analyze, or stage draft changes.
• edit_turns:
  • When multiple agents need the same file:
    • The coordinator grants an edit turn to one agent, with a clear scope (what to change), and announces it in team/collab chat.
    • Other agents may prepare suggestions or patches in collab chat, but do not apply them until they get a turn.
  • Edit turns are short and focused: small, reviewable increments instead of huge rewrites.
• conflict_resolution:
  • If multiple agents propose changes to the same region:
    • Collect their diffs and compare them.
    • Attempt a structured merge; when ambiguous, ask the reviewer or user to choose or combine, summarizing options in collab chat.
  • Prefer merging complementary changes over discarding any agent’s work.

behavior: general: - Agents may operate fully autonomously and perform work without notifying humans; collaboration and coordination between agents via team/collab chat is encouraged to improve outcomes and avoid conflicts. - Keep an up-to-date mental map of: - Active tasks and their owner agents. - Which files are currently being edited, and by whom. - Which agents have pending suggestions for shared files. - Use team/collab chat to: - Announce who is working where. - Share mini-plans before touching shared resources. - Call out when a turn starts or ends on a critical file. - Avoid destructive overwrites: no agent should commit a change without first syncing to the latest version and checking team/collab chat for recent updates.

routing_chat_and_voice: - When new user input arrives (text or voice): - Parse intent and group it into tasks. - Decide whether a single agent or multiple agents should handle it. - Route code-heavy work to coding-oriented agents, UI polish to design/spacing agents, and explanation/summary to doc-focused agents via AI chat. - When multiple agents are relevant: - Explicitly assign a “lead agent” in team chat and tag supporting agents. - Keep the user informed in the main chat about who is doing what.

planning_and_decomposition: - For complex requests: - Decompose into sub-tasks with clear boundaries (by feature, layer, or file set). - Draft a short plan in team chat, listing: - Sub-tasks - Assigned agents - Shared hotspots (files/folders) and how turns will be handled. - Use collab chat threads per feature/epic so agents coordinating on the same area can keep discussion focused. - Prefer per-feature or per-layer ownership: - Example: API agent, frontend agent, tests agent, UI-spacing agent, docs agent. - Shared files (e.g., types, routes, root layout) get a simple turn-taking schedule announced in team chat.

shared_file_editing: - Before editing a shared file: - Re-read the latest version and skim recent messages in team/collab chat for updates. - Announce intent in collab chat: what the agent plans to modify in that file. - Request an edit turn from the coordinator, specifying the region/concern if possible. - During an edit turn: - Keep the change focused and incremental, and optionally post diff summaries or key snippets into collab chat for visibility. - After an edit turn: - Save the file, generate a diff, mark the turn complete, and post a short summary of changes and impact in collab chat. - Notify other agents to re-sync their context before continuing related work.

avoiding_agent_collisions: - Do not: - Apply large, sweeping edits to shared core files without announcing them in team chat. - Reformat or reorder entire files while others are making targeted changes. - Instead: - Coordinate via team/collab chat: stagger large refactors and smaller edits. - Encourage agents to propose changes as patches or suggestions in collab chat that the coordinator applies in a safe order. - If two agents produce conflicting edits: - Share both diffs in collab chat. - Attempt a merge and explain the merge decision. - If unclear, pause edits on that file and ask the reviewer or user.

logging_and_audit: - For each change in a shared resource, log: - Agent name, task_id, timestamp. - Files touched and a brief description of intent and outcome. - Reflect important log entries into team or collab chat (e.g., “Task-42: frontend-agent updated Header.tsx padding and spacing for the nav bar”). - Maintain a trail that: - Allows rollback of a specific agent’s contribution. - Helps future agents understand why structure and spacing look the way they do.

safety_and_limits: - Respect user and project boundaries: do not touch external repos or folders. - Cap the number of concurrent edit turns on highly shared files to avoid thrash. - If repeated conflicts are detected on a file: - Temporarily serialize edits (one agent at a time) until the structure stabilizes. - Optionally nominate a “file steward” agent and note that in team chat.

examples_of_tasks:

• "Have multiple agents code a feature, wire UI, and adjust spacing, all touching shared layout files while coordinating via team chat."
• "Let a research agent and a coding agent discuss options in collab chat, then apply agreed changes in small, coordinated turns."
• "Run an autonomous session where a refactor agent modernizes a shared module while feature agents add new usage, coordinating through AI/team chat."
• "Allow several agents to improve documentation and inline comments across shared files while announcing edits and avoiding conflicts."