Salus Agent Guardrails

Workflow

When this skill triggers, follow these steps in order.

Step 1 — Locate the policy file

Check whether the user has specified a policy file path.

• If a path is provided, confirm the file exists and is readable.
• If no path is provided, ask: "Please provide the path to your policy JSON file. It should be an array of { effect, tool, args?, reason } rules. See examples/policy.json for a working example."
• If the user has no policy, offer to generate a starter policy based on their described use-case before proceeding.

Step 2 — Capture the proposed tool-call

Capture the tool-call the agent is about to execute. It must be valid JSON matching:

{ "tool": "<name>", "args": { "<argName>": "<value>", ... } }

Wrap it in a JSON array if it is a single object (the engine accepts an array of calls). If the user provides multiple calls at once, evaluate them all together.

Step 3 — Run the engine

Execute the rule matcher from the skill root:

node scripts/policy-eval.mjs <policyFile> <callsFile>

The engine outputs a JSON array of { tool, decision, reason } — one entry per call.

Capture stdout. If the process exits non-zero, surface the stderr message to the user and stop.

Step 4 — Report the decision

For each result:

• allow — report the tool name, decision, and reason. The call may proceed.
• deny — report the tool name, decision, and the rule that fired. Explain what the agent attempted and why the policy blocked it. Suggest a safe alternative if one exists (e.g. read instead of write, a scoped path instead of a wildcard).
• ask — report that the call is paused pending user confirmation. Show the exact tool-call JSON so the user knows exactly what will execute. Wait for explicit approval before proceeding.

Output format (use this shape in your response)

Tool:     <tool>
Decision: ALLOW | DENY | ASK
Reason:   <rule reason text>

If decision is DENY or ASK, add a "What to do next" paragraph.

Example

See examples/input.md for a worked scenario with three calls and examples/output.md for the corresponding decisions produced by the engine.

Run the example yourself:

cd skills/salus-agent-guardrails
bash examples/run.sh