Install
openclaw skills install @mohitagw15856/agent-design-reviewReview an LLM agent design and find where it will be unreliable, expensive, or unsafe. Use when asked to review an agent architecture, critique a multi-step/tool-using agent, debug an agent that loops or goes off-task, or harden an agent before launch. Produces a structured review — task fit, control flow, tools, memory/context, failure handling, cost, and safety — with prioritised findings and fixes.
openclaw skills install @mohitagw15856/agent-design-reviewMost agents don't fail because the model is weak — they fail because the design lets them loop, call the
wrong tool, lose the thread across steps, or burn tokens with no stopping rule. This skill reviews an agent's
architecture against the decisions that actually determine reliability, and ranks the fixes — so "it works in
the demo but not in prod" becomes a specific list of changes. (Writing a new agent spec? Use
agent-spec.)
Given a sketch ("a research agent that searches, reads, and writes a report"), deliver the full review anyway — infer the likely control flow and tools, label the inference, and flag what to confirm. Never withhold the review for missing detail.
Ask for these only if they aren't already provided (else infer and label):
1. Summary — will this be reliable in production? The top 3 risks and the single change that helps most.
2. Findings by dimension — for each, what's sound and what's fragile:
| Dimension | Finding | Severity | Fix |
|---|---|---|---|
| Control flow | no max-steps / no progress check → loops | High | step budget + "am I making progress?" check + halt |
| Tool use | overlapping tools confuse selection | Med | fewer, sharply-described tools; allowlist |
| Context | full history re-sent each step → cost + drift | High | summarise/scope memory per step |
| Failure handling | one tool error aborts the run | Med | retry/backoff + graceful degradation |
| Safety | acts without confirmation on writes | High | human/confirm gate on side-effecting actions |
3. Reliability checklist — termination guarantee (it always stops), error recovery, idempotency of side-effecting actions, and determinism where it matters.
4. Cost & latency — where tokens/steps are spent and how to cut them (cheaper model for sub-steps, caching, fewer round-trips) without losing quality. Pair with llm-cost-latency-budget.
5. Safety — untrusted input/tool output handled as data not instructions, least-privilege tools, and
confirmation gates on high-impact actions. Pair with llm-guardrails-spec.
6. Prioritised fix plan — ordered by impact-to-effort.
LLM agent design practice — bounded control flow, least-privilege tool use, context management, error recovery, and safety gating.