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 ChrisLamDev/hermes-core-skills --skill security-hardening-checklistgit clone --depth 1 https://github.com/ChrisLamDev/hermes-core-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/chrislamdev/hermes-core-skills/security-hardening-checklist)<a href="https://agentmods.dev/skills/chrislamdev/hermes-core-skills/security-hardening-checklist"><img src="https://agentmods.dev/badge/skills/chrislamdev/hermes-core-skills/security-hardening-checklist/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/chrislamdev/hermes-core-skills/security-hardening-checklist"><img src="https://agentmods.dev/badge/skills/chrislamdev/hermes-core-skills/security-hardening-checklist.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 | $0.00043 | $0.01657 |
| Opus 5 | $0.00022 | $0.00829 |
| Sonnet 5 | $0.00009 | $0.00331 |
| Haiku 4.5 | $0.00004 | $0.00166 |
Grade A, and why
security-hardening-checklist scanned grade A with 1 finding 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 11d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
os.system(f"ls {user_input}") How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Hardening Checklist
Overview
Security-first development practices. Treat every external input as hostile, every secret as sacred, and every authorization check as mandatory. Security isn't a phase — it's a constraint on every line of code that touches user data, authentication, or external systems.
When to Use
- Building anything that accepts user input
- Implementing authentication or authorization
- Storing or transmitting sensitive data
- Integrating with external APIs or services
- Adding file uploads, webhooks, or callbacks
- Handling payment or PII data
- Mini Program specific: Handling user's WeChat OpenID, UnionID, or phone number
Process: Threat Model First
Controls bolted on without a threat model are guesses. Before hardening, spend five minutes thinking like an attacker:
-
Map the trust boundaries. Where does untrusted data cross into your system? HTTP requests, form fields, file uploads, webhooks, third-party APIs, message queues, and LLM output. Every boundary is attack surface.
-
Name the assets. What's worth stealing or breaking? Credentials, PII, payment data, admin actions, money movement.
-
Run STRIDE over each boundary:
| Threat | Ask | Typical mitigation |
|---|---|---|
| Spoofing | Can someone impersonate a user/service? | Authentication, signature verification |
| Tampering | Can data be altered in transit or at rest? | Integrity checks, parameterized queries, HTTPS |
| Repudiation | Can an action be denied later? | Audit logging of security events |
| Information disclosure | Can data leak? | Encryption, field allowlists, generic errors |
| Denial of service | Can it be overwhelmed? | Rate limiting, input size caps, timeouts |
| Elevation of privilege | Can a user gain rights they shouldn't? | Authorization checks, least privilege |
- Write abuse cases next to use cases. For each feature, ask "how would I misuse this?" — then make that your first test.
If you can't name the trust boundaries for a feature, you're not ready to secure it. Most breaches begin in design, not code.
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.
- 11d ago First seen · 179 lines · 43 tokens per session scan A bab4d7c7e8e5
security-hardening-checklist is a skill published in the GitHub repository ChrisLamDev/hermes-core-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 43 tokens to every session and 1,657 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
unicli-repair
Evidence-driven repair workflow for a broken Uni-CLI adapter. Trigger on a failed unicli envelope, a quarantined adapter, or an explicit adapter-repair request. Classifies non-source failures, edits only the reported adapter path, and uses the original command as a bounded oracle.
autonomous-run
Prepare, start, inspect, resume, or stop a finite local overnight coding run after a human has accepted a Wayfinder terminal spec; coordinates a declared Claude/Codex maker and independent checker without pushing, merging, or writing to external systems.
authentication-patterns
OAuth 2.0, JWT, SSO, MFA, NextAuth/Clerk/Supabase Auth implementation patterns.
case-interview-practice
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i18n-localization
Internationalization and localization for global applications. Use when adding multi-language support, handling regional formats, or preparing apps for global markets.
research-archival
Scrape AI research URLs, archive with frontmatter, create GitHub Issues with identity verification.