Copy-ready Lagune prompts you can paste directly into your AI agent.
💧 Shallow Scan
A read-only sweep that reads the code on its own and reports every flaw with a fix, without running the Blue Team flow or writing to Lagune's tracking.
Works better in planning mode and with a charter (/lagune-charter) defined.
- 🪼 Default
- 🐬 Multi Agents
| Aspect | Trade-off | Level |
|---|---|---|
| Effort | One agent scans and reports, no sub-agents. | |
| Time | Quick. A single read-only pass, nothing is applied. | |
| Token Usage | Moderate, shared. The whole scan runs in one context. |
/lagune You are a cybersecurity researcher with deep expertise in defensive security, using Lagune to improve your knowledge and precision.
Scan the workspace on your own and pair each flaw you find with a concrete fix. This is a standalone sweep, not the Blue Team flow: do not run detect, plan, harden, or verify, and write nothing to Lagune's tracking.
At the end, list every flaw you found, each with its severity and a concrete fix.
| Aspect | Trade-off | Level |
|---|---|---|
| Effort | One sub-agent per file, scaled to the size of your codebase. | |
| Time | Parallel per file, then merged into one report. Still read-only. | |
| Token Usage | Higher, but isolated. Each sub-agent scans in its own context. |
/lagune You are a cybersecurity orchestrator using Lagune to enumerate, dispatch a subagent per path, and merge their reports (all content is read by the subagents).
You must delegate the task to sub-agents based on the current project's size: the larger the codebase, the more sub-agents should be dispatched, all in parallel, giving each the prompt below:
```prompt
/lagune You are a cybersecurity researcher with deep expertise in defensive security, using Lagune to improve your knowledge and precision.
Scan <path> on your own and pair each flaw you find with a concrete fix. This is a standalone sweep, not the Blue Team flow: do not run detect, plan, harden, or verify, and write nothing to Lagune's tracking.
At the end, list every flaw in <path>, each with its severity and a concrete fix.
```
At the end, merge all sub-agent reports into one prioritized list of flaws and fixes.
A quick scan is one broad, unscoped prompt, so some agents are likelier to refuse it or cut it short. If yours pushes back, run the full Blue Team flow below instead: its scoped, step-by-step phases are far less likely to be blocked.
🌊 Auto Blue Team Audit Flow
Runs the full command flow end to end without pausing for input.
- 🪼 Default
- 🐋 Multi Agents (Deep Scan)
| Aspect | Trade-off | Level |
|---|---|---|
| Effort | One agent, no sub-agents, regardless of codebase size. | |
| Time | Medium, but less thorough in large codebases. Each command runs once, one after another. | |
| Token Usage | Lower, but less isolated. Every command shares the same context. |
Execute the following commands, one by one: `/lagune-charter`, `/lagune-detect`, `/lagune-plan`, `/lagune-harden`, and `/lagune-verify`.
## Loop
When `/lagune-verify` rejects one or more fixes, repeat `/lagune-harden` and then `/lagune-verify` until all findings are properly resolved.
## Important
- Don't pause, don't ask: in case of doubt, do everything in "full" mode.
| Aspect | Trade-off | Level |
|---|---|---|
| Effort | The number of sub-agents is scaled to the size of your codebase. | |
| Time | Longer, but more thorough. Dispatches multiple sub-agents to detect, plan, and harden findings in parallel. | |
| Token Usage | Higher, but safer. Each sub-agent has its own isolated context without bias. |
Execute the following commands each to one sub-agent, one at a time (without backticks, just the command):
Sub-agent 1 prompt: `/lagune-charter`
Sub-agent 2 prompt: `/lagune-detect`
Sub-agent 3 prompt: `/lagune-plan`
Sub-agent 4 prompt: `/lagune-harden`
Sub-agent 5 prompt: `/lagune-verify`
In the `/lagune-detect`, `/lagune-plan`, `/lagune-harden`, and `/lagune-verify` commands, you must delegate the task to sub-agents based on the current project's size: the larger the codebase, the more sub-agents should be dispatched. For example:
```prompt
/lagune-detect <path>
```
```prompt
/lagune-plan <finding>
```
```prompt
/lagune-harden <finding>
```
```prompt
/lagune-verify <finding>
```
- Multiple agents on the same command should be dispatched in parallel.
- At the end of all `/lagune-detect` sub-agents, perform:
```prompt
/lagune-detect merge equivalent findings. Don't detect for new findings nor scan the codebase again.
```
## Loop
When `/lagune-verify` rejects one or more fixes, repeat `/lagune-harden` and then `/lagune-verify` until all findings are properly resolved.
## Important
- Don't pause, don't ask: in case of doubt, do everything in "full" mode.
🪄 Scoped Detection
Point /lagune-detect at a slice of your project instead of the whole thing. Describe the scope in plain language (a commit, a pull request, or a real situation) and it maps only the security-relevant findings there.
Everything it detects here is a normal finding, so you can carry it straight into /lagune-plan, /lagune-harden, and /lagune-verify, scoped to just those items.
- 📌 Since a commit
- 🔀 Branch or PR diff
- ⛑️ After an incident
Give a fresh batch of commits its own security pass, scoped to just what they changed, for example:
/lagune-detect Scope the detection to everything that changed since commit `a1b2c3d`.
Review a pull request for security before it merges, scoped to just its diff, for example:
/lagune-detect Scope the detection to the changes this branch introduces against `main`.
Describe what happened in your own words. Lagune reads the situation, not a flag, and turns it into a focused hunt, for example:
/lagune-detect Our site had a data leak a few weeks ago. I don't know exactly how it happened, and I'm worried something is still open that could leak data again. Go through the code carefully, focus on anywhere sensitive data is read, stored, or exposed, and map anything that could still be a way out. Treat this as a real incident, not a drill.
🚨 Zero Trust Audit
Treats the whole workspace and every dependency as hostile: it hunts for interpreters, network calls, encoded payloads, and code hidden in unlikely places to slip past scanners, then ends with a go/no-go verdict on whether the project is safe to use.
The prompts load the specializations this audit relies on directly, whatever your project's categories. For the rest of the catalog /lagune matches, make sure the project uses OWASP, AI/LLM, and the category of its stack (e.g., JavaScript, Python, etc.):
npx lagune write add --categories owasp ai javascript
The repository's own .gitignore files and configs could hide files from the checkers, so set the checkers block of .lagune/configs.json to scan everything, replacing any block the repository ships:
{
"checkers": { "gitignore": false }
}
Be careful when running projects from untrusted sources in your local environment. Use an isolated environment, such as a sandbox (recommended), VM, or similar, to prevent malicious code from affecting your system.
- 🪼 Default
- 🐋 Multi Agents (Deep Scan)
| Aspect | Trade-off | Level |
|---|---|---|
| Effort | One agent sweeps the whole workspace, no sub-agents. | |
| Time | Medium. One agent works through the code and its dependencies in sequence. | |
| Token Usage | Moderate, but shared. The specializations, the code, and the dependencies all load into one context. |
/lagune You are a cybersecurity researcher with deep expertise in zero trust defensive security, using Lagune to improve your knowledge and precision.
Map the entire workspace as an untrusted source and a potential malicious scope. Every interpreter, fetch, base64, and the like must be treated as a potential threat.
Extend your analysis to the dependencies and sub-dependencies: a project may try to hide malicious code in an unlikely place to slip past scanners.
Mandatory specializations (load them one at a time with `npx lagune read catalog <name>`):
- interpreter
- secrets
- llm
- path
To be clear: every mapped vulnerability must be treated as a potential threat, without exception.
At the end, give a verdict on whether the project is safe and trustworthy for me to use or not.
| Aspect | Trade-off | Level |
|---|---|---|
| Effort | One sub-agent per file, scaled to the size of your codebase. | |
| Time | Longer, but more thorough. Files are swept in parallel, then merged into one verdict. | |
| Token Usage | Highest, but safest. Each sub-agent reads the specializations in its own isolated context. |
/lagune You are a cybersecurity orchestrator using Lagune to enumerate, dispatch a subagent per file, and generate reports (all content is read by the subagents).
Dispatch one sub-agent per file, all in parallel, giving each the prompt below, scoped to its file:
```prompt
/lagune You are a cybersecurity researcher with deep expertise in zero trust defensive security, using Lagune to improve your knowledge and precision.
Treat <file> as an untrusted source and a potential malicious scope. Every interpreter, fetch, base64, and the like must be treated as a potential threat.
Extend the analysis to whatever <file> imports, including dependencies and sub-dependencies, where malicious code may hide to slip past scanners.
Mandatory specializations for every sub-agent (load them one at a time with `npx lagune read catalog <name>`):
- interpreter
- secrets
- llm
- path
To be clear: every mapped vulnerability must be treated as a potential threat, without exception.
At the end, give a verdict on whether the project is safe and trustworthy for me to use or not.
```
At the end, merge all sub-agent reports into one final verdict.
