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 mickeylorenzini/harden --skill hardengit clone --depth 1 https://github.com/mickeylorenzini/hardenWrote 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/mickeylorenzini/harden/harden)<a href="https://agentmods.dev/skills/mickeylorenzini/harden/harden"><img src="https://agentmods.dev/badge/skills/mickeylorenzini/harden/harden/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/mickeylorenzini/harden/harden"><img src="https://agentmods.dev/badge/skills/mickeylorenzini/harden/harden.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00132 | $0.02473 |
| Opus 5 | $0.00066 | $0.01236 |
| Sonnet 5 | $0.00026 | $0.00495 |
| Haiku 4.5 | $0.00013 | $0.00247 |
Grade A, and why
harden 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 8d ago.
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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Harden
MANDATORY: Re-read this entire file before every audit pass. Do not rely on earlier readings within the same conversation. Context window drift causes skipped steps.
Purpose
Ensures substantial deliverables are structurally sound, internally consistent, and aligned with domain best practices before delivery. Replaces ad hoc quality checks with a repeatable methodology.
How It Works
- Research — Searches the web for domain-specific best practices before auditing (not just internal assumptions)
- Dual-pass audit — Checks both ideas (are they sound?) and structure (do paths/references/formatting work?)
- Severity classification — 🔴 Critical / 🟡 Significant / 🟢 Minor with specific locations
- Fix — Implements all critical and significant fixes (not just reports)
- Converge — Re-audits until only minor issues remain (max 3 passes)
The Process
Step 1: Research
Search for current best practices specific to the deliverable's domain.
Scope guidance:
- Minimum 2 searches for focused domains (e.g., a skill file -> Anthropic skill docs)
- 3-5 searches for broad or unfamiliar domains (e.g., a grant aims page -> NIH guidelines, successful examples, common reviewer complaints)
- Always check for official/canonical sources first, then practitioner insights
- Fetch the canonical source. If search results include the single most authoritative document for this domain (official guide, spec, standard), fetch and read it — do not rely on search snippets. Snippets capture ~5% of a document's content and miss testing, edge cases, and patterns. Limit to the first ~5000 tokens if the source is very long; prioritize sections on requirements, validation, and common mistakes.
- Synthesize findings into actionable bullets that inform the audit — not a reading list
If web search is unavailable: State this limitation explicitly. Proceed using training knowledge but flag that the audit lacks external validation.
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.
- 8d ago First seen · 243 lines · 132 tokens per session scan A 9fab02c7cb8e
harden is a skill published in the GitHub repository mickeylorenzini/harden (2 stars, last pushed 6mo ago), licensed MIT. It adds 132 tokens to every session and 2,473 once invoked, about $0.0007 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 2026-08-31.
Other skills, from other repositories
neo4j-document-import-skill
Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or…
neo4j-driver-dotnet-skill
Neo4j .NET Driver v6 — IDriver lifecycle, DI registration (singleton), ExecutableQuery fluent API, ExecuteReadAsync/ExecuteWriteAsync managed transactions, IResultCursor (FetchAsync/ ToListAsync), record value access (.Get /As ), null safety, UNWIND batching, temporal types, await using, EagerResult, object mapping…
neo4j-getting-started-skill
Orchestrates zero-to-running-app in 8 stages — prerequisites → context → provision → model → load → explore → query → build. Each stage reads its own reference file. Supports HITL and fully autonomous operation. Use when starting a new Neo4j project from scratch, provisioning Aura, generating synthetic data, building…
neo4j-aura-agent-skill
Manages Neo4j Aura Agents via the v2beta1 REST API — create, list, get, update, delete, and invoke Aura agents backed by an AuraDB instance. Use when configuring Aura Agent tools (CypherTemplate, SimilaritySearch, Text2Cypher), setting system prompts, deploying agents to REST or MCP endpoints, or invoking agents with…
neo4j-graphql-skill
Build and configure a GraphQL API backed by Neo4j using @neo4j/graphql v7 (current) or v5 (LTS). Covers Neo4jGraphQL constructor, getSchema(), assertIndexesAndConstraints(), type definitions with @node, @relationship (IN/OUT/UNDIRECTED), @cypher for custom resolvers, @authorization/@authentication for JWT/JWKS…
neo4j-modeling-skill
Design, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with…