agentscope-ai/AgentTeams/manager/agent/team-leader-agent/skills/project-management/SKILL.md
project-management
Use before any projectflow call or Team Leader workflow involving Project state, Project lifecycle, DAG planning, Loop planning, ready node checks, pause/resume/complete, project recovery, heartbeat project checks, or project-level result aggregation. Always use team-coordination first when the question is how to organize the work.
- Source repository stars
- 5,317
- Declared platforms
- 0
- Static risk flags
- 0
- Last source update
- 2026-08-06
- Source checked
- 2026-08-06
Decision brief
What it does—and where it fits
You manage Project files, Project lifecycle, and Project execution plans. Use this skill as the Project state layer. Use team-coordination to decide the strategy, and use task-management to delegate or check individual Worker tasks.
Not for
- Tasks that require unconfirmed production actions or broad system permissions.
- Environments where the pinned source and install steps cannot be inspected.
Compatibility matrix
Platform support, with evidence labels
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
Inspect first. Install second.
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/agentscope-ai/AgentTeams --skill "manager/agent/team-leader-agent/skills/project-management"Inspect the Agent Skill "project-management" from https://github.com/agentscope-ai/AgentTeams/blob/062f1c8d7edd54ceaa2141862dd98a75c36c6980/manager/agent/team-leader-agent/skills/project-management/SKILL.md at commit 062f1c8d7edd54ceaa2141862dd98a75c36c6980. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
What the source asks the agent to do
- 01
Scope
Do not use this skill to decide whether the work should be DAG or Loop; use team-coordination first. Do not use this skill to create Worker task files or check result.md; use task-management.
projectflow callsProject metadata and lifecycle stateDAG and Loop execution plans - 02
Project Files
Project files live under:
meta.json for Project metadata and lifecycle statusplan.md for the current execution planresult.md for the final Project result - 03
IDs
Use safe IDs only. Project and task IDs must match:
projectId: {short-description}-YYYYMMDD-HHMMSSDAG task ID: {projectId}-{seq}Loop task ID: {projectId}-i{iteration}-{seq} - 04
Projectflow Actions
projectflow does not push or pull files. It also does not send Matrix messages, stop Workers, accept task results, or write Worker task specs.
createprojectplandagreadynodes - 05
Create Project
Create the Project before planning DAG or Loop work:
Create the Project before planning DAG or Loop work:This creates meta.json and an initial DAG plan.md. After creation, publish shared/projects/{project-id}/.
Permission review
Static risk signals and limitations
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
Why each signal appears
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 88/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 5,317 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Provenance and original SKILL.md
- Repository
- agentscope-ai/AgentTeams
- Skill path
- manager/agent/team-leader-agent/skills/project-management/SKILL.md
- Commit
- 062f1c8d7edd54ceaa2141862dd98a75c36c6980
- License
- Apache-2.0
- Collected
- 2026-08-06
- Default branch
- main
View the original SKILL.md
Project Management
You manage Project files, Project lifecycle, and Project execution plans. Use this skill as the Project state layer. Use team-coordination to decide the strategy, and use task-management to delegate or check individual Worker tasks.
Project state is tool-owned. Do not create, edit, delete, or repair shared/projects/** with shell commands, heredocs, direct file writes, rm, mkdir, cp, or Python module execution. Use projectflow actions only. If projectflow fails or returns inconsistent state, stop and report the blocker instead of manually patching files.
Scope
Use this skill for:
projectflowcalls- Project metadata and lifecycle state
- DAG and Loop execution plans
- ready-node resolution
- Project pause, resume, and completion
- heartbeat and recovery checks
- Project-level result aggregation
Do not use this skill to decide whether the work should be DAG or Loop; use team-coordination first. Do not use this skill to create Worker task files or check result.md; use task-management.
Project Files
Project files live under:
shared/projects/{project-id}/
Use:
meta.jsonfor Project metadata and lifecycle statusplan.mdfor the current execution planresult.mdfor the final Project result
The execution plan type is stored in plan.md as **Plan Type**: dag or **Plan Type**: loop. Do not put Project lifecycle status in plan.md. Do not hand-edit meta.json.status; use projectflow.
Use filesync before reading remote Project files and after changing local Project files. Use directory paths with a trailing slash, such as shared/projects/{project-id}/. projectflow reads and writes local shared files only.
If pulling shared/projects/{project-id}/ fails, stop before any Project state mutation. Do not pull only meta.json or plan.md and continue to mark nodes, resolve ready nodes, record Loop iterations, or complete the Project.
IDs
Use safe IDs only. Project and task IDs must match:
[A-Za-z0-9_-]+
Use these formats:
projectId:{short-description}-YYYYMMDD-HHMMSS- DAG task ID:
{projectId}-{seq} - Loop task ID:
{projectId}-i{iteration}-{seq}
Do not use colons, dots, slashes, spaces, or short standalone IDs such as st-01.
Projectflow Actions
Use projectflow for:
create_projectplan_dagready_nodesplan_loopready_loop_nodesrecord_loop_iterationpause_projectresume_projectcomplete_project
projectflow does not push or pull files. It also does not send Matrix messages, stop Workers, accept task results, or write Worker task specs.
Create Project
Create the Project before planning DAG or Loop work:
{
"action": "create_project",
"payload": {
"projectId": "{project-id}",
"title": "{title}",
"source": "{optional source}",
"requester": "{requester Matrix id or room marker}",
"parentTaskId": "{optional parent task id}"
}
}
This creates meta.json and an initial DAG plan.md. After creation, publish shared/projects/{project-id}/.
Lifecycle
Lifecycle actions only change Project lifecycle state. They do not change the graph, accept task results, resolve ready nodes, delegate work, stop Workers, or aggregate final output.
Pause:
- Use only after
team-coordinationhas selected a pause or interruption path, or when a recovery path needs to close scheduling. - Run
projectflow(action="pause_project"). - Publish
shared/projects/{project-id}/. - Do not issue new work while paused.
Resume:
- Confirm the requester explicitly wants scheduling to continue.
- Pull
shared/projects/{project-id}/. - Run
projectflow(action="resume_project"). - Publish
shared/projects/{project-id}/. - Continue through normal result checking, plan refresh if needed, and ready-node resolution.
Complete:
- Confirm delivery criteria are satisfied.
- Write
shared/projects/{project-id}/result.md. - Run
projectflow(action="complete_project"). - Publish
shared/projects/{project-id}/. - Report the final result to the requester recorded on the Project.
DAG Operations
Use DAG when team-coordination decided the work is finite and dependencies can be planned now.
plan_dag receives the complete graph you want to keep:
{
"action": "plan_dag",
"payload": {
"projectId": "{project-id}",
"tasks": [
{
"taskId": "{project-id}-01",
"title": "Short outcome title",
"assignedTo": "<matrix-localpart>",
"dependsOn": []
}
]
}
}
Each node uses:
taskIdtitleassignedTodependsOn
For assignedTo, use the Worker's Matrix localpart (the part between @ and : in matrixUserID). Extract it mechanically — never guess, strip, or transform.
Lookup steps (mandatory before every plan_dag / plan_loop call):
- Run
agt get workers --team "$TEAM_CR" -o json - For each Worker, extract the localpart from
.matrixUserID: e.g.@worker-issue-resolver:domain→worker-issue-resolver - Use that localpart verbatim as
assignedTo
❌ Do NOT use CLI .name field directly (it may include deployment prefixes like magic-cn-...-worker-issue-resolver)
❌ Do NOT strip prefixes yourself — you will incorrectly remove legitimate name components
❌ Do NOT infer worker names from memory, AGENTS.md, or display names
CLI .name | matrixUserID | ✅ assignedTo | ❌ Wrong |
|---|---|---|---|
magic-cn-x0a4t4pr201-worker-issue-resolver | @worker-issue-resolver:domain | worker-issue-resolver | issue-resolver |
magic-cn-plt4s29va0r-worker-dev-worker | @dev-worker:domain | dev-worker | worker |
Do not use worker, owner, dependencies, or standalone short IDs.
When you call plan_dag, send the full graph you want to keep:
- keep accepted nodes that still matter
- omit obsolete nodes
- add new nodes for changed scope, fixes, verification, or follow-up work
plan_dag preserves existing node status for nodes with the same taskId. It does not resume a paused Project, accept task results, or delegate work.
Call ready_nodes only when the Project is active. It returns pending DAG nodes whose dependencies are satisfied by accepted [x] nodes. Delegate returned nodes with task-management.
See references/dag-execution.md for DAG node and dependency design.
Loop Operations
Use Loop when team-coordination decided the work should repeat until a stop condition, quality gate, evidence threshold, or maximum iteration count.
plan_loop receives the complete Loop plan you want to keep:
{
"action": "plan_loop",
"payload": {
"projectId": "{project-id}",
"goal": "{loop goal}",
"stopCondition": "{condition for stopping}",
"iterationTemplate": "{how each iteration should run}",
"maxIterations": 5,
"currentIteration": 1,
"tasks": [
{
"taskId": "{project-id}-i001-01",
"title": "Short outcome title",
"assignedTo": "<matrix-localpart>",
"dependsOn": []
}
]
}
}
Required Loop inputs:
goalstopConditioniterationTemplatemaxIterations
Optional inputs:
currentIterationstatustasks
For every Loop task, assignedTo follows the same rule as DAG tasks: extract the Matrix localpart from agt get workers output. Never strip or transform.
Use ready_loop_nodes to find pending nodes in the current iteration whose dependencies are satisfied by accepted [x] nodes. Delegate returned nodes with task-management.
After evaluating an iteration, use record_loop_iteration:
{
"action": "record_loop_iteration",
"payload": {
"projectId": "{project-id}",
"iteration": 1,
"decision": "continue",
"summary": "{what you learned}",
"nextAction": "{optional next step}"
}
}
Allowed decisions:
continuereplanask_userstop_successstop_blocked
record_loop_iteration records your decision and updates Loop status. It does not create the next iteration's tasks by itself; call plan_loop when you need a new current-iteration task set.
Result Gate
When a Worker result arrives:
- Use
task-managementto pull and check the task result. - Pull
shared/projects/{project-id}/. - Read Project
meta.jsonandplan.md. - If the Project is paused, stop. Do not accept results, mark
[x], resolve ready nodes, or delegate work. - If active, use
team-coordinationto decide whether to accept, verify, repair, replan, ask the requester, continue Loop, or complete.
Only accepted [x] plan nodes satisfy dependencies for ready_nodes and ready_loop_nodes. A Worker SUCCESS is not enough by itself.
After you accept a DAG or Loop task result, update the corresponding plan node to [x], publish Project files, then resolve ready nodes. Use the Project Status Reports template below for the report content, then use communication to deliver it to the requester.
Project Status Reports
Build Project status report content here because it describes Project execution state. Use communication only for routing, target room, @mention, and same-room versus cross-room delivery.
Before writing a requester report:
- Pull and read
shared/projects/{project-id}/meta.jsonandplan.md. - Identify the plan type from
plan.md. - For DAG, report graph progress through dependencies and next ready waves.
- For Loop, report iteration progress through the current iteration, iteration decision, and next iteration path.
- Write the report in the language selected by
AGENTS.mdResponse Language, then usecommunicationto deliver it to the requester recorded on the Project.
Status emoji for task and Project state. Keep the emoji stable, but localize the visible state label to the report language:
| State | Emoji |
|---|---|
| Completed | ✅ |
| In Progress | 🔄 |
| Pending | ⏳ |
| Blocked | ❌ |
| Revision | 🔁 |
Keep the report envelope consistent across DAG and Loop:
---
## <localized project status report heading>
**<localized project name label>**: <name>
**<localized project ID label>**: <project-id>
**<localized execution mode label>**: <DAG or Loop>
**<localized project status label>**: <emoji + localized state label>
**<localized current focus label>**: <DAG wave/current node, or Loop iteration n / max and topic>
**<localized summary label>**: <1-3 sentences about what changed and what happens next>
**<localized task status label>**:
| <localized task ID label> | <localized task title label> | <localized owner label> | <localized status label> | <localized context label> |
|---|---|---|---|---|
| <task-id> | <title> | <worker> | <emoji + localized state label> | <dependencies, iteration role, result note, or -> |
**<localized execution progress label>**:
```
<DAG dependency flow or Loop iteration line; choose the matching shape below>
```
**<localized deliverables label>**:
- `<path>` - <what it contains>
**<localized next steps label>**:
1. <next DAG transition, next Loop decision, or requester action>
**<localized notes label>**: <blocker, risk, or decision needed; omit section if none>
Use this execution progress shape for DAG reports:
<task-id> <emoji> -> <task-id> <emoji> -> <task-id> <emoji>
^
<task-id> <emoji> -------|
Use the DAG progress section to show the dependency path that changed, newly unblocked nodes, or the next ready wave. If the graph is large, show only the relevant changed path and summarize the rest in the task table.
Use this execution progress shape for Loop reports:
Iteration 1 ✅ -> Iteration 2 🔄 -> Iteration 3 ⏳ ... Max <n>
Current: <current iteration topic>
Decision: <continue, replan, ask_user, stop_success, stop_blocked, or pending evaluation>
Use the Loop progress section to show current iteration number, maximum iterations, current iteration topic, and the latest Leader decision. Do not present future Loop rounds as if they were pre-planned DAG tasks. Show only the current iteration's task statuses in the task table unless prior iteration context is necessary.
For intermediate updates, include the Project header, summary, task status table, and next steps. Omit the deliverables section unless new deliverables are available, and omit the execution progress section only when neither DAG flow nor Loop iteration state changed.
Heartbeat And Recovery
Do not call check_active_tasks for now.
Team Leader heartbeat is temporarily disabled. Follow HEARTBEAT.md: do not probe Worker runtime, inspect active tasks, or send anomaly reports from scheduled heartbeat runs.
Worker runtime probes are disabled because Kubernetes Team Workers currently have no per-Worker Service. Hostname probes such as http://agentteams-worker-<worker>:8088/api/chats can misreport healthy Workers as unreachable.
For recovery, act only on explicit room messages, requester instructions, or Project files you were directly asked to inspect.
References
Read only when needed:
references/dag-execution.md- DAG input shape, node design, dependency design, dynamic replanning, and ready nodes.
Patch Rules
Do not:
- treat task status as Project lifecycle state
- continue dispatching while the Project is paused
- use
plan_dagorplan_loopas a substitute forresume_project - use lifecycle actions as the whole pause, stop, or interruption workflow
- call ready-node resolution before pulling current Project files
Alternatives
Compare before choosing
alirezarezvani/claude-skills
app-store-optimization
App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklist
prowler-cloud/prowler
postgresql-indexing
PostgreSQL indexing best practices for Prowler: index design, partial indexes, partitioned table indexing, EXPLAIN ANALYZE validation, concurrent operations, monitoring, and maintenance. Trigger: When creating or modifying PostgreSQL indexes, analyzing query performance with EXPLAIN, debugging slow queries, reviewing index usage statistics, reindexing, dropping indexes, or working with partitioned table indexes. Also trigger when discussing index strategies, partial indexes, or index maintenance
wanshuiyin/Auto-claude-code-research-in-sleep
citation-audit
Use it for operations and research tasks; the detail page covers purpose, installation, and practical steps.
K-Dense-AI/scientific-agent-skills
dask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.