# Swarm Test:review > > - **Type:** Skill - **Install:** `agentstack add skill-rob-morris-agent-skills-review` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [Rob-Morris](https://agentstack.voostack.com/s/rob-morris) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [Rob-Morris](https://github.com/Rob-Morris) - **Source:** https://github.com/Rob-Morris/agent-skills/tree/main/swarm-test/review ## Install ```sh agentstack add skill-rob-morris-agent-skills-review ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Swarm Test: Review ## Purpose Cheap, parallel quality gate. Haiku agents are small and fast but not brilliant — if they can't figure something out from the work product alone, a real user or contributor will struggle too. Their confusion is signal. ## Input The user provides one of: - **A description of what to test** — e.g. "the new documentation structure", "the MCP tool implementation", "the archive workflow" - **A file or directory** — e.g. `docs/`, `src/brain-core/scripts/create.py` - **A recent branch or set of commits** — e.g. "everything on this branch since main" If the argument is ambiguous, read the context (branch diff, recent files) to infer scope. Ask only if genuinely unclear. ## Process ### 1. Understand the target Read enough to know what was built/written and what it's supposed to accomplish. Sources: - The files themselves - Git diff if on a branch (`git diff main...HEAD --stat`, then read changed files) - Any plan or design doc the user references - README or entry points in the target directory Don't read exhaustively — read enough to generate good test scenarios. ### 2. Generate test scenarios Create 8-12 test scenarios (adjust to the target's scope). Each scenario is a **task a real person would try to accomplish** using the work product. Design for coverage: - **Happy paths** — can someone accomplish the core use cases? - **Discovery** — can someone find the right information starting from an entry point, without being told where to look? - **Edge cases** — what about the less obvious scenarios? - **Cross-references** — do related parts of the system agree with each other? - **Completeness** — is anything important missing entirely? Each scenario should be: - **Specific** — "explain how to add a new temporal artefact type with specific steps" not "test the docs" - **Self-contained** — the agent gets the scenario and a starting point, nothing else - **Answerable** — you know what a correct answer looks like, so you can judge the result Present the scenarios to the user as a numbered list before dispatching. The user can adjust, add, remove, or approve. ### 3. Dispatch the swarm Launch all agents in parallel using the smallest available model (in Claude Code: `model: haiku`). Each agent gets: ``` You are testing [docs/implementation/design] quality. You are in [repo] at [path]. Your task: [specific scenario]. Start by reading [entry point] to orient yourself, then find the answer using only the [docs/code/etc] available. Report: 1. The path you took (which files, in order) 2. The specific answer you found (or couldn't find) 3. Rate clarity: CLEAR (no confusion), MURKY (found it but it was hard), or LOST (couldn't find it or gave up) 4. One concrete suggestion to improve what you tested ``` The entry point should be whatever a real person would start from — a README, an index file, a module's `__init__.py`, the top-level directory. Never give agents the direct path to the answer. ### 4. Collect and synthesise As agents complete, track results. When all are done, produce a summary table: | # | Scenario | Rating | Key finding | |---|----------|--------|-------------| Then group findings into **gaps** — patterns across multiple agents, not just individual complaints: - **Gaps** — things that are missing, wrong, or unreachable - **Friction** — things that exist but are hard to find or unclear - **Strengths** — things that worked well (don't lose these when fixing gaps) ### 5. Fix gaps Present proposed fixes to the user for approval before implementing. Then apply the same judgment you would for any change: - Don't over-fix — address the actual problem, not a hypothetical one - Don't restructure what's working to fix what isn't - Small, targeted edits over rewrites - Follow the repo's existing conventions (CLAUDE.md, contributing docs) After fixing, run tests if applicable (`make test` or equivalent). ### 6. Iterate (optional) If fixes were significant (new sections added, structure changed, major rewrites), offer to run another round targeting the fixed areas. The second round should be smaller (4-6 agents) and focused on the gaps that were patched. One iteration is usually enough. Two if the first round found structural problems. Never more than three — diminishing returns. ## Principles - **Haiku confusion is signal, not noise.** If a small model can't follow the docs, a tired human won't either. - **Test from the outside in.** Agents start from entry points, not from the file that has the answer. - **Coverage over depth.** 10 agents each testing one thing beats 3 agents testing deeply. - **Fix the gap, not the agent.** When an agent fails, the question is "why couldn't it find this?" not "why is this agent bad?" - **Preserve what works.** Note strengths explicitly so fixes don't accidentally degrade them. ## Output After each round, present: 1. The results table 2. Grouped gaps with specific file references 3. Proposed fixes (or fixes already applied, depending on auto mode) 4. Whether another round is warranted ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [Rob-Morris](https://github.com/Rob-Morris) - **Source:** [Rob-Morris/agent-skills](https://github.com/Rob-Morris/agent-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-rob-morris-agent-skills-review - Seller: https://agentstack.voostack.com/s/rob-morris - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.