Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Morningstar202604/awesome-skillkit --skill agent-designergit clone --depth 1 https://github.com/Morningstar202604/awesome-skillkitWrote 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/morningstar202604/awesome-skillkit/agent-designer)<a href="https://agentmods.dev/skills/morningstar202604/awesome-skillkit/agent-designer"><img src="https://agentmods.dev/badge/skills/morningstar202604/awesome-skillkit/agent-designer/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/morningstar202604/awesome-skillkit/agent-designer"><img src="https://agentmods.dev/badge/skills/morningstar202604/awesome-skillkit/agent-designer.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 | $0.00121 | $0.01173 |
| Opus 5 | $0.00060 | $0.00587 |
| Sonnet 5 | $0.00024 | $0.00235 |
| Haiku 4.5 | $0.00012 | $0.00117 |
Grade A, and why
agent-designer 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 9d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Designer — Multi-Agent System Architecture
Design, schema-generate, and evaluate multi-agent systems with three deterministic tools. The scripts are the workflow — do not freehand an architecture when the planner can score one from requirements.
When to use
- Designing a new multi-agent system from requirements (pattern choice, roles, comms)
- Generating provider-ready tool schemas (Anthropic + OpenAI formats) from plain tool descriptions
- Evaluating execution logs: success rate, latency distribution, cost, bottlenecks
When NOT to use: Claude Code Workflow-tool automations → workflow-builder; multi-agent fan-out at runtime → agenthub.
Pattern decision table
| Choose | When | Watch out for |
|---|---|---|
| Single agent | One bounded task, < ~5 tools | Don't add agents you don't need |
| Supervisor | Central decomposition, specialists report back | Supervisor becomes the bottleneck |
| Pipeline | Strictly sequential stages with handoffs | Rigid order; slowest stage gates throughput |
| Hierarchical | Multiple org layers, > ~8 agents | Communication overhead per level |
| Swarm | Parallel peers, fault tolerance over predictability | Hard to debug; needs consensus rules |
The planner applies this scoring deterministically — run it rather than picking by feel.
Workflow scaffolding (quick start)
For a fast skeleton before running the full planner pipeline:
# Sequential / parallel / router / orchestrator / evaluator skeletons
python3 workflow_scaffolder.py sequential --name content-pipeline
python3 workflow_scaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.json
Pattern templates and the minimum handoff contract (workflow_id, step_id,
task, constraints, upstream_artifacts, budget_tokens, timeout_seconds)
→ See references/workflow_patterns.md for details
Workflow discipline: start with the smallest pattern that satisfies the requirements; keep handoff payloads explicit and bounded; add retry/timeout policy to every external-model call; validate intermediate outputs before fan-in synthesis; dry-run with small context budgets before scaling.
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agent_evaluator.py 52 KB runs code
- agent_planner.py 38 KB runs code
- assets/sample_execution_logs.json 15 KB
- assets/sample_system_requirements.json 2.2 KB
- assets/sample_tool_descriptions.json 16 KB
- expected_outputs/sample_agent_architecture.json 14 KB
- expected_outputs/sample_evaluation_report.json 15 KB
- expected_outputs/sample_tool_schemas.json 12 KB
- README.md 12 KB
- references/agent_architecture_patterns.md 9.6 KB
- references/evaluation_methodology.md 20 KB
- references/tool_design_best_practices.md 13 KB
- references/workflow_patterns.md 1.5 KB
- tool_schema_generator.py 37 KB runs code
- workflow_scaffolder.py 3.3 KB runs code
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.
- 9d ago First seen · 102 lines · 121 tokens per session scan A f9c64438deb2
agent-designer is a skill published in the GitHub repository Morningstar202604/awesome-skillkit (1 stars, last pushed yesterday), licensed Apache-2.0. It adds 121 tokens to every session and 1,173 once invoked, about $0.0006 per session on Opus 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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