Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/bugrabilge/bilge-development-kitnpx agentmods add skills/bugrabilge/bilge-development-kit/agent-manager-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bugrabilge/bilge-development-kit/agent-manager-skill)<a href="https://agentmods.dev/skills/bugrabilge/bilge-development-kit/agent-manager-skill"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/agent-manager-skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bugrabilge/bilge-development-kit/agent-manager-skill"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/agent-manager-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.00976 |
| Opus 5.5 | $0.00014 | $0.00390 |
| Sonnet 5.5 | $0.00007 | $0.00195 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
agent-manager-skill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
git clone https://github.com/fractalmind-ai/agent-manager-skill.git
Common commands
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
tmuxandpython3. - Agents are configured under an
agents/directory (see the repo for examples).
Agent Performance Optimization Workflow
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
Phase 1: Performance Analysis and Baseline
Gather Performance Data
Collect metrics including:
- Task completion rate (successful vs failed tasks)
- Response accuracy and factual correctness
- Tool usage efficiency (correct tools, call frequency)
- Average response time and token consumption
- User satisfaction indicators (corrections, retries)
- Hallucination incidents and error patterns
User Feedback Pattern Analysis
Identify recurring patterns:
- Correction patterns: Where users consistently modify outputs
- Clarification requests: Common areas of ambiguity
- Task abandonment: Points where users give up
- Follow-up questions: Indicators of incomplete responses
- Positive feedback: Successful patterns to preserve
Failure Mode Classification
Categorize failures by root cause:
- Instruction misunderstanding, output format errors
- Context loss in long conversations
- Tool misuse or inefficient tool selection
- Constraint violations, edge case handling
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 137 lines · 34 tokens per session scan A 607291b2fe9e
agent-manager-skill is a skill published in the GitHub repository bugrabilge/bilge-development-kit (10 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 976 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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