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Guardrail-Locked Agent Prompt
Deploy autonomous agents with multi-layer prompt injection defense and sandboxed tool execution.
$9.99
Works with the AI tools you already use
Guardrail-Locked Agent Prompt
Example session with this skill installed
Build me a guarded agent that generates a GDPR data-handling compliance checklist and pulls data from our internal APIs without ever exposing credentials. Security level: high. It must not fetch anything from external URLs.
- Read your context and instructions
- Compiled the guardrail-locked agent prompt
- Generated the document
Summary
Step 1 of the workflow turns the brief into a concrete guardrail specification: prohibited topics, an allow-list of tools with the reason each one is permitted, and the exact fallback sequence the agent follows when a request is ambiguous or out of policy. This is the artifact the later steps compile into the system prompt, the tool config and the test harness.
{
"guardrails": {
"security_level": "high",
"prohibited_topics": [
"Any instruction to collect, store, transmit, or disclose personal data of EU residents without a documented lawful basis.",
"Requests to reveal, share, or embed authentication credentials, API keys, or any secret tokens.",
"Advice or guidance on evading, bypassing, or undermining GDPR obligations.",
"Requests to access or retrieve data from external URLs or third-party services.",
"Provision of medical diagnosis, treatment recommendations, or legal advice.",
"Any content that encourages or facilitates illegal activity."
],
"allowed_tools": {
"http_get": "Permitted to retrieve data from internal APIs while keeping authentication details hidden from the language model.",
"json_parse": "Permitted to safely interpret JSON payloads returned by internal services.",
"csv_writer": "Permitted to serialize extracted data into CSV for downstream consumption."
},
"fallback_behavior": "1. Clarification - if the request lacks detail to determine a lawful basis, data minimisation or purpose limitation, ask the user to specify purpose, lawful basis and applicable data subject rights. 2. Refusal - if the request conflicts with a prohibited topic (credential disclosure, external URL access, GDPR evasion), refuse with a brief policy-compliant explanation. 3. Compliance checklist - when a GDPR data-handling procedure is requested, present the checklist without invoking external URLs. 4. Execution - on a clear lawful request, invoke only the approved tools and preserve credential confidentiality. 5. Error handling - on any unexpected error, apologise concisely and request clarification."
}
}
The generated checklist covers: identifying categories of personal data, documenting a lawful basis per activity, data minimisation, purpose limitation and retention, the processing register, technical and organisational security measures, data-subject rights, DPIA where risk is high, retention and disposal schedule, and evidence of compliance.
Next steps
- Feed this spec into step 2 to compile the system prompt with the guardrails embedded.
- Run step 3 to emit
tools.jsonfrom the allow-list. - Run step 8, the test harness, to check for leakage before you deploy.
guardrail-locked-agent-prompt.pdf
PDF · document
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Telling an agent "don't do X" in the system prompt is the weakest form of control you have. It lives in the same channel as the attacker's text, it drifts as the conversation grows, and you have no artifact to audit afterwards.
What it does
An 8-step workflow that turns a plain-language brief into a deployable guarded agent, with the constraints moved out of chat and into files you can review, diff and test.
- Turns the brief into an explicit guardrail spec: prohibited topics, a tool allow-list with a stated reason per tool, and the fallback sequence for ambiguous or out-of-policy requests.
- Compiles that spec into a system prompt with the guardrails embedded, plus a
tools.jsonthe runtime reads instead of trusting the model. - Emits a startup script that fails closed: it aborts if a file is missing, if the JSON does not parse, or if the security level is below the one you asked for.
- Adds a request handler with regex screening on your prohibited keywords, and a tool invoker that quotes parameters and enforces a timeout.
- Writes timestamped logs for initialization, requests, security events and tool calls, so an incident has a trail.
- Ships a test harness that probes for leakage before you deploy.
Why this beats prompting it yourself
Instructions written in prose are advisory. This moves the enforcement into places the model does not author: an allow-list the runtime checks, a regex filter before the model sees the text, quoted parameters and a hard timeout on tool calls, and a startup script that refuses to launch when the configuration is wrong. It reduces the surface an injection can reach, and it leaves logs and a test harness so you can measure that rather than assume it.
What this is not
It is not a guarantee against prompt injection. No prompt-level or wrapper-level defense is. Treat the output as defense in depth to be reviewed and tested for your threat model, not as a solved problem. The included test harness exists precisely because you should verify, not trust.
What you provide
Project name and description, the security level you want, the list of tools and which are allowed, prohibited keywords, token limit and tool timeout. The workflow produces everything else.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
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