mainline: Git-native memory for coding agents. Repo memory before the diff

by mainline-org · Codex Skill · ★ 199

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install mainline

go install github.com/mainline-org/mainline@latest
npx --yes skills update mainline --global --yes

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 66/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-contextagent-memoryai-agentsai-code-reviewai-codingclaude-codecodexcoding-agentscopilotcursor

Quick Facts

Stars199
Forks13
LanguageGo
CategoryCodex Skill
Quality Score66/100
Open Issues3
Last Updated2026-10-27
Created2026-04-25
Platformsclaude-code, codex, go
Est. Tokens~18k

About mainline

Mainline Website: https://mainline.sh Hosted Hub: https://mainline.sh/hub/ Detailed reference: docs/reference.md 中文版本: README.zh.md We have code review. Now we need intent review. Mainline is a Git-native memory layer for coding agents. It gives agents and reviewers repo memory before the diff: prior decisions, constraints, abandoned approaches, validation notes, and related in-flight work.

Compatible Skills

These tools work well together with mainline for enhanced workflows:

  • mnemon — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)

mainline alternative? Top 6 similar tools

Looking for a mainline alternative? If you're comparing mainline with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

View on GitHub → Browse Codex Skill tools