Defender logo

Defender

Organization
quillai-network
defender

Blue-team release-gate analysis for smart contract deployment and upgrade readiness. Classifies repositories, checks deploy/upgrade execution paths, CI/CD trust boundaries, config drift, secrets/signer operational security, and outputs evidence-backed release verdicts.

Overview

Publisherquillai-network
Repositoryquillshield_skills
Skill namedefender
Stars
131
Forks
28
Bundled files
22
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 22 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by quillai-network on GitHub. Read the source before you install it.

Installation

Install the Defender AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/quillai-network/quillshield_skills/tree/main/plugins/defender/skills/defender
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/quillai-network/quillshield_skills.git /tmp/quillshield_skills
mkdir -p .claude/skills
cp -r /tmp/quillshield_skills/plugins/defender/skills/defender .claude/skills/defender
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Defender in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Defender on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Defender is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Defender

A blue-team release-gate skill for smart contract systems.

Defender determines whether a repository is safe to deploy or upgrade. It focuses on release execution risk, not exploit discovery.

Use when

  • deploying smart contracts
  • preparing upgrades
  • reviewing deploy scripts
  • hardening CI/CD
  • rotating admin roles
  • validating ownership handoff
  • enforcing release hygiene

Non-goals

Defender does NOT replace:

  • full security audits
  • economic analysis
  • invariant discovery
  • deep proxy vulnerability analysis (proxy-upgrade-safety)

It focuses only on execution safety of release.


Core rule

Evidence first.

Only report findings from:

  • contracts
  • deploy scripts
  • CI workflows
  • dependency manifests
  • configs / address books
  • tests / fork scripts
  • docs / runbooks

Separate:

  • Detection → what repo proves
  • Policy → what should be enforced

Execution order (STRICT)

  1. Project classification
  2. Defence pass
  3. Severity scoring
  4. Release verdict

Reference packs

Always load:

  • references/finding-catalog.md
  • references/severity-model.md
  • references/evidence-query-playbook.md

Load contextually:

  • classification → project-classification.md
  • CI → ci-supply-chain.md
  • deploy drift → config-drift-checks.md
  • upgrade → upgrade-readiness.md
  • signer → signer-opsec.md
  • false confidence → false-confidence.md
  • post-deploy → post-deploy-validation.md

Templates:

  • defender-report-template.md
  • checklist templates as needed

Phase 1 — Project classification

Framework

Detect:

  • Foundry / Hardhat / hybrid / other

Evidence:

  • foundry.toml, hardhat.config.*, scripts

Language

  • Solidity / Vyper / Cairo / mixed

Upgradeability

  • upgradeable / immutable / mixed

Evidence:

  • OZ upgrade imports
  • proxies
  • initializer usage

Protocol type

Infer:

  • token / vault / AMM / lending / bridge / governance / NFT / staking / other

Deployment surface

  • manual / script / CI / multisig / upgrade-task

CI surface

  • GitHub Actions / GitLab / other / none

Output classification block.


Phase 2 — Defence pass

A. Build integrity

Check:

  • compiler version pinned
  • optimizer pinned
  • evmVersion consistency
  • lockfiles committed
  • no conflicting configs (Foundry vs Hardhat)
  • artifacts reproducible from repo
  • verification metadata aligned

Escalate if:

  • build settings differ from verification
  • configs produce ambiguous outputs

B. Dependencies, secrets, supply chain

Check:

  • committed secrets
  • .env usage for private keys
  • floating versions
  • unpinned git deps
  • install scripts
  • dependency confusion risk
  • abandoned/suspicious packages
  • OZ version mismatch
  • unsafe sidecar tooling
  • unverified binaries

Secret policy

Plaintext .env private keys are discouraged.

Preferred:

  • Foundry keystore-backed accounts

Classify:

  • private key in repo → BLOCKER/HIGH
  • deploy scripts using plaintext env keys → HIGH
  • .env for non-sensitive config → acceptable

C. CI/CD trust

Check:

  • unpinned GitHub Actions
  • secrets in untrusted workflows
  • deploys from weak branches
  • no approval gate
  • excessive secret access
  • curl/bash installs
  • unsafe runners
  • mixed trust zones

Escalate if CI can deploy unsafely.


D. Deployment config drift

CRITICAL

Check:

  • chain ID matches target
  • RPC matches chain
  • deployer is correct
  • constructor/initializer args final
  • addresses (oracle/token/admin) valid for chain
  • no test/stale addresses
  • decimals correct
  • verifier settings pinned
  • no silent fallback logic

Foundry FFI

  • default: HIGH
  • BLOCKER if affecting deploy logic/secrets

E. Deploy-script safety

Check:

  • chain assertions
  • deployer assertions
  • env validation
  • correct initialization order
  • idempotency assumptions clear
  • no test config reuse
  • address outputs reviewable
  • no opaque branching
  • verification steps present

Escalate if scripts can silently misdeploy.


F. Upgrade readiness (if applicable)

Check:

  • implementation initialized or locked
  • initializer calldata reviewed
  • storage diff reviewed
  • proxy admin correct
  • multisig/timelock path exists
  • pause/rollback defined
  • fork rehearsal exists

G. Signer & admin opsec

Check:

  • deployer is dedicated
  • secure signer preferred
  • admin is multisig
  • roles separated:
    • deployer / upgrader / pauser / treasury
  • emergency roles documented
  • no hot wallet admin unless justified
  • tx review process exists

H. Address & role mapping

Extract:

  • owner/admin
  • proxy admin
  • upgrader
  • pauser
  • treasury
  • oracle
  • keeper
  • timelock

Flag:

  • role concentration
  • EOA-only control
  • zero addresses
  • missing transfer flow

I. Fork rehearsal

Require evidence of:

  • deploy scripts tested
  • initializer tested
  • role transfers tested
  • upgrade tested
  • verification tested
  • smoke tests run

Absence → HIGH (mainnet)


J. Post-deploy readiness

Check defined plan for:

  • verification
  • ownership assertions
  • pause test
  • oracle checks
  • event expectations
  • integration smoke tests
  • monitoring
  • deployment manifest

K. Unsafe deploy ergonomics

Check:

  • one-command broadcast risk
  • missing confirmations
  • hidden env defaults
  • CREATE/CREATE2 assumptions undocumented
  • nonce-sensitive flows undocumented

Phase 3 — False confidence

MANDATORY

Passing does NOT imply safety:

  • unit tests
  • forge test
  • static analysis
  • lint
  • typecheck

Require:

  • fork rehearsal
  • permission diff
  • storage diff
  • address diff
  • event checks
  • role snapshot
  • post-deploy smoke tests

Phase 4 — Severity

BLOCKER

  • wrong chain
  • lost admin
  • leaked secrets
  • broken init
  • broken upgrade
  • unverifiable deployment

HIGH

  • unsafe CI
  • missing rehearsal
  • FFI misuse
  • admin EOA risk
  • stale config

MEDIUM

  • should fix before mainnet

LOW

  • hygiene gaps

Specify scope:

  • mainnet-only / all releases / upgrades only

Phase 5 — Verdict

Always output:

  • VERDICT: BLOCK DEPLOY
  • VERDICT: PROCEED WITH RISK
  • VERDICT: READY FOR STAGED RELEASE

Include:

  • top blockers
  • required actions
  • evidence reviewed

Output format

text
DEFENDER REPORT

1. Project Classification
- Framework:
- Language:
- Upgradeability:
- Protocol Type:
- Deployment Surface:
- CI Surface:

2. Release Findings

BLOCKER:
- ...

HIGH:
- ...

MEDIUM:
- ...

LOW:
- ...

3. False Confidence Warnings
- ...

4. Release Verdict

VERDICT: ...

Top blockers:
- ...

Required actions:
- ...

Evidence reviewed:
- ...

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Defender AI skill do?

Blue-team release-gate analysis for smart contract deployment and upgrade readiness. Classifies repositories, checks deploy/upgrade execution paths, CI/CD trust boundaries, config drift, secrets/signer operational security, and outputs evidence-backed release verdicts.

Why use Defender on TypingMind?

Because you install it once and use it with any model. Defender is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Defender in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/quillai-network/quillshield_skills/tree/main/plugins/defender/skills/defender. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Defender?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Defender?

As many as you like. As long as a model supports skills, you can use Defender with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Defender AI skill free?

Yes. It is published on GitHub by quillai-network under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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