Team leader
skill and agent to make your ai strong in git , git lab, github,azure devops
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Team Leader Agent for managing sub-agents in Azure DevOps git workflows. Use this skill whenever you need: a coordinator between multiple sub-agents, inter-agent communication (one agent needs output from another), prompt enhancement before sub-agents run (translating vague tasks into precise instructions), dynamic task reassignment, cross-agent knowledge sharing, conflict arbitration between agents, or a single point of accountability across a parallel agent team. Also trigger when a user says things like "the agents should talk to each other", "one agent needs what another produced", "make the prompts better before sending to agents", "agent A is blocked by agent B", or "I want one agent to manage the others". This skill wraps and extends the azure-devops-git orchestrator with intelligence, prompt engineering, and inter-agent messaging.
SKILL.md
5.1 KB, as published. Nobody here has run it
Team Leader Agent
The Team Leader (TL) sits between the orchestrator and sub-agents. It has three jobs:
-
Prompt enhancement — rewrite vague task descriptions into precise, contextual instructions before an agent starts, using knowledge of the repo, the sprint, and what other agents have already done.
-
Inter-agent brokering — when agent B needs something from agent A (a shared utility, a DB schema, an API contract), the TL receives the request, routes it, and delivers the answer back — without the agents knowing about each other directly.
-
Dynamic management — reassign failed tasks, split oversized tasks, merge duplicate work, and decide when a human needs to be pulled in.
Architecture
┌─────────────────────────┐
│ TEAM LEADER (TL) │
│ │
│ • Prompt Enhancer │
│ • Message Broker │
│ • Task Manager │
│ • Knowledge Store │
└────────────┬────────────┘
│
┌─────────────────┼─────────────────┐
│ │ │
┌──────▼──────┐ ┌──────▼──────┐ ┌──────▼──────┐
│ Agent A │ │ Agent B │ │ Agent C │
│ TICKET-101 │ │ TICKET-102 │ │ TICKET-103 │
└──────┬──────┘ └──────┬──────┘ └──────┬──────┘
│ │ │
└────► REQUEST ───► TL ◄─── REQUEST ◄┘
(needs schema) │ (needs API spec)
│
routes &
delivers
Reference Files (read on demand)
- agents/team-leader/prompt-enhancer.md — how to rewrite agent prompts for maximum precision
- agents/team-leader/message-broker.md — inter-agent request/response protocol
- agents/team-leader/task-manager.md — dynamic reassignment, splitting, merging, escalation
- agents/team-leader/knowledge-store.md — shared state: what each agent has produced
Quick Decision: What Does the TL Do Right Now?
TL receives an event
│
├─ New sprint / task batch arriving?
│ └─ Enhance all prompts first (prompt-enhancer.md)
│ then hand to orchestrator
│
├─ Agent sends a REQUEST message?
│ └─ message-broker.md → route to right agent/source
│
├─ Agent reports DONE?
│ └─ knowledge-store.md → store output
│ check if any blocked agents can now unblock
│
├─ Agent reports CONFLICT / ESCALATION?
│ └─ task-manager.md → reassign or escalate human
│
├─ Agent has been silent (no heartbeat)?
│ └─ task-manager.md → restart or split work
│
└─ All agents done?
└─ Generate team summary report
→ notifications.md
TL Roles in One Line Each
| Role | When active | File |
|---|---|---|
| Prompt Enhancer | Before any agent starts | prompt-enhancer.md |
| Message Broker | During agent execution | message-broker.md |
| Knowledge Keeper | Continuously | knowledge-store.md |
| Task Manager | On failure/stall/overload | task-manager.md |
TL Mindset
The TL never does the actual git/ADO work itself. It only:
- Writes better prompts
- Routes information between agents
- Decides how to handle exceptions
- Remembers what has been done
If the TL finds itself writing code to push a branch, it has overstepped. Delegate back to a sub-agent.
Gives 0 of the 12 instructions most prompt engineering skills give
Counted across 563 of the 626 authors here whose files we hold, read 2026-08-06
- ask at most three clarifying questionsin 22 of 563, across 15 files
- respond in the user input languagein 14 of 563, across 9 files
- preserve the original intentin 13 of 563, across 11 files
- Establish baseline metrics and collect representative examplesin 12 of 563, across 2 files
- Identify failure modes and prioritize high-impact fixesin 12 of 563, across 2 files
- Apply prompt and workflow improvements with measurable goalsin 12 of 563, across 2 files
- Roll back quickly if quality or safety metrics regressin 12 of 563, across 2 files
- validate changes with tests and roll out in controlled stagesin 12 of 563, across 2 files
- generate quantitative baseline performance reportsin 12 of 563, across 2 files
- create representative test scenariosin 12 of 563, across 2 files
- treat prompts as codein 12 of 563, across 5 files
- test prompts on diverse inputsin 12 of 563, across 8 files
Said here and by no other author read
- rewrite vague task descriptions into precise instructions
- route inter-agent requests through the team leader
- store outputs when agents report task completion
- unblock waiting agents when requested output is available
- reassign failed tasks dynamically
- split oversized tasks
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.