Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PracticalSwan/agent-skills --skill defense-in-depthgit clone --depth 1 https://github.com/PracticalSwan/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/practicalswan/agent-skills/defense-in-depth)<a href="https://agentmods.dev/skills/practicalswan/agent-skills/defense-in-depth"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/defense-in-depth/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/practicalswan/agent-skills/defense-in-depth"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/defense-in-depth.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 131 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Data Exfiltration · line 80 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.01377 |
| Opus 5 | $0.00008 | $0.00688 |
| Sonnet 5 | $0.00003 | $0.00275 |
| Haiku 4.5 | $0.00002 | $0.00138 |
Grade A, and why
defense-in-depth scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Defense-in-Depth Validation
Overview
When you fix a bug caused by invalid data, adding validation at one place feels sufficient. But that single check can be bypassed by different code paths, refactoring, or mocks.
Core principle: Validate at EVERY layer data passes through. Make the bug structurally impossible.
Why Multiple Layers
Single validation: "We fixed the bug" Multiple layers: "We made the bug impossible"
Different layers catch different cases:
- Entry validation catches most bugs
- Business logic catches edge cases
- Environment guards prevent context-specific dangers
- Debug logging helps when other layers fail
The Four Layers
Layer 1: Entry Point Validation
Purpose: Reject obviously invalid input at API boundary
function createProject(name: string, workingDirectory: string) {
if (!workingDirectory || workingDirectory.trim() === '') {
throw new Error('workingDirectory cannot be empty');
}
if (!existsSync(workingDirectory)) {
throw new Error(`workingDirectory does not exist: ${workingDirectory}`);
}
if (!statSync(workingDirectory).isDirectory()) {
throw new Error(`workingDirectory is not a directory: ${workingDirectory}`);
}
// ... proceed
}
Layer 2: Business Logic Validation
Purpose: Ensure data makes sense for this operation
function initializeWorkspace(projectDir: string, sessionId: string) {
if (!projectDir) {
throw new Error('projectDir required for workspace initialization');
}
// ... proceed
}
Layer 3: Environment Guards
Purpose: Prevent dangerous operations in specific contexts
async function gitInit(directory: string) {
// In tests, refuse git init outside temp directories
if (process.env.NODE_ENV === 'test') {
const normalized = normalize(resolve(directory));
const tmpDir = normalize(resolve(tmpdir()));
if (!normalized.startsWith(tmpDir)) {
throw new Error(
`Refusing git init outside temp dir during tests: ${directory}`
);
}
}
// ... proceed
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed 31bb7008661b
- 3d ago Changed 148743fc30a5
- 6d ago Changed 733af3d2ddde
- 9d ago First seen · 178 lines · 16 tokens per session scan A bc09724d809e
defense-in-depth is a skill published in the GitHub repository PracticalSwan/agent-skills (13 stars, last pushed yesterday), licensed MIT. It adds 16 tokens to every session and 1,377 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
fs-notebook-tabs
A computer-science capstone: an on-device ML keyboard that predicts next words privately — problem, method, evaluation, and defense answers. Built as a decision-grade coursework defense deck for professor, defense committee.
html-ppt-zhangzara-cartesian
An economics senior thesis on the employment effects of local minimum-wage increases — identification strategy, evidence, and limitations. Built as a decision-grade coursework defense deck for thesis committee.
html-ppt-zhangzara-pin-and-paper
A field-biology capstone on urban pollinator decline — the survey design, the data, the contribution, and the caveats. Built as a decision-grade coursework defense deck for faculty reviewers.
html-ppt-zhangzara-scatterbrain
A design-school graduation project: a civic wayfinding system for a transit hub — the brief, the process, and the outcome. Built as a decision-grade coursework defense deck for crit panel, faculty.
Blue Team Defense & Hardening
System hardening, detection engineering, security baseline monitoring, patch management, defense-in-depth architecture, and security posture improvement.
tao-train-depth-anything-v2
Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts per-pixel depth from single RGB images. Use when training, evaluating, exporting, or running inference for a TAO monocular depth model. Trigger phrases include "train monocular depth", "DepthAnything v2"…