Verification and reliability
Skill AnamKwon/agent-skill-router/plugins/skill-router/skills/verification-and-reliability
Cross-agent skill router that organizes large local skill libraries into category routers and loads leaf skills on demand.
npx -y skills add AnamKwon/agent-skill-router --skill verification-and-reliabilityAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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Use first when the user asks for tests, correctness, high reliability, static reasoning, proof, or safety evidence.
SKILL.md
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Verification and Reliability
Use first when the user asks for tests, correctness, high reliability, static reasoning, proof, or safety evidence. This is a router skill: use it to select the smallest relevant leaf skill, then read that leaf skill before doing the work.
Route First
Choose the proof style before changing code: test-first behavior discovery, specification-driven prevention, or sound approximation.
- Restate the user's task as one concrete force or uncertainty.
- Choose the first route that directly names that force.
- Read the selected linked leaf
SKILL.mdbefore implementing, reviewing, or advising. - Load a second leaf only when the task has two independent forces that both affect the outcome.
- If no route fits, continue without a leaf skill and say the category did not match.
Routes
| Leaf Skill | Use When |
|---|---|
test-driven-development | Use when the next observable behavior can be captured by a failing test before implementation. |
cleanroom-software-engineering | Use when reliability risk requires precise specification, defect prevention, and small verifiable increments. |
abstract-interpretation | Use when many possible executions must be reasoned about with a sound over-approximation. |
design-by-contract | Use when reliability depends on explicit preconditions, postconditions, or invariants at a boundary. |
stepwise-refinement | Use when proof depends on preserving a high-level specification through implementation steps. |
Avoid
- If the behavior is still ambiguous, route to ambiguity-and-learning before writing tests.
- If the issue is primarily human workflow evidence, route to workflow-and-operations.
Prompting Pattern
Before loading a leaf, answer briefly:
- Category force: What makes this task belong here?
- Chosen route: Which leaf skill most directly matches the force?
- Why not others: Which nearby route was rejected and why?
Then load the chosen leaf skill and follow its workflow. Do not blend every nearby theory into the task; route narrowly and let evidence pull in more context only when needed.