# Agent Manager Skill

> Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.

- Skill: `hybridlabor-api/agent-manager-skill` (Agent Skill)
- Install (CLI): `npx skillmds@latest add hybridlabor-api/agent-manager-skill`
- Raw SKILL.md: https://api.skillmd.com/api/skills/hybridlabor-api/agent-manager-skill/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: hybridlabor-api (https://skillmd.com/u/hybridlabor-api)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/hybridlabor-api/agent-manager-skill

---


# Agent Manager Skill

## When to Use
Use this skill when you need to:

- run multiple local CLI agents in parallel (separate tmux sessions)
- start/stop agents and tail their logs
- assign tasks to agents and monitor output
- schedule recurring agent work (cron)

## Prerequisites

Install `agent-manager-skill` in your workspace:

```bash
git clone https://github.com/fractalmind-ai/agent-manager-skill.git
```

## Common commands

```bash
python3 agent-manager/scripts/main.py doctor
python3 agent-manager/scripts/main.py list
python3 agent-manager/scripts/main.py start EMP_0001
python3 agent-manager/scripts/main.py monitor EMP_0001 --follow
python3 agent-manager/scripts/main.py assign EMP_0002 <<'EOF'
Follow teams/fractalmind-ai-maintenance.md Workflow
EOF
```

## Notes

- Requires `tmux` and `python3`.
- Agents are configured under an `agents/` directory (see the repo for examples).

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

## 1. Overview
This skill provides domain-specific logic and rules for its respective BDB pipeline component to ensure standardization across multi-agent workflows.

## 3. Core Process
1. Read the provided context and ensure preconditions are met.
2. Run the required script or tool and confirm the state change.
3. Verify exit codes, file modifications, or DB counts to guarantee success before reporting completion.

## 4. Common Rationalizations
| Rationalization | Reality |
|---|---|
| "The code change was small, so I skipped updating OpenWiki docs." | Every state change must be reflected in the relevant system records. |
| "The ingest script exited without an error, so the memB index must be updated." | Silent failures happen; explicit verification of the side effect is mandatory. |
| "I'll let the /startcycle proceed without a defined rollback path." | Proceeding without a rollback path corrupts the workflow integrity and safety. |
| "I trust the cached agent registry instead of rescanning after a skill change." | Caches stale out quickly; explicit rescans prevent ghost failures. |

## 5. Red Flags
- Bypassing the verification step after a script execution.
- Proceeding to the next pipeline stage without confirming the previous stage's side effects.
- Ignoring domain-specific constraints listed in this skill.

## 6. Verification
- [ ] Verified script exit codes are explicitly checked.
- [ ] Confirmed target files or database records reflect the expected change.
- [ ] Ensured no silent failures were ignored before reporting success.