- Category
- AI Agents
- Rank
- No. 1190Tools index
Previous survey · No. 1177 ·
- Pricing
- Open Source
- Type
- AGENT
- Use case
- Workflow Automation
- Interfaces
- Host Plugin
- Builder
- @aaronjmars
- GitHub
- 787 stars
- Latest release
- v0.1.0
- Date
About
Background intelligence agent that runs on GitHub Actions and evolves with you. Features 47 skills across research, dev tooling, crypto monitoring, and productivity that run autonomously on schedules you set.
What it does
Aeon is a fork-and-run framework: you copy the repository, authenticate an agent CLI, and a dashboard lets you toggle small packaged tasks called skills on and off with a cron schedule. Each skill is just a text file: a short header naming its category and required credentials, followed by a plain-language prompt. A dispatcher hands that prompt to whichever coding-agent CLI you configured (Claude Code, Codex, Cursor and six others share one contract), then GitHub Actions runs it unattended. Skills can chain into each other, react to a previous run's failure, and a companion set watches the others and reopens broken ones automatically.
Why it's ranked here
Most "autonomous agent" repositories stop at scheduling a prompt. Aeon backs its read-only mode with an operating-system sandbox instead of trusting a coding agent's own tool permissions, since every skill runs unattended and unreviewed, and a skill that writes its own replacement code must pass a dry run against synthetic credentials before its pull request can even open. That is meaningfully more engineering than a cron job wrapping a model call, MIT licensed and built to run inside a repository you fork and own rather than a hosted platform.
What's good
The security writing is unusually honest for this category. It states plainly that a tool allowlist is not a real boundary, since a shell redirection can route around it, and that the actual guarantee is the operating-system sandbox underneath. A circuit breaker trips a skill after three straight failures and probes it again every six hours rather than either burning Actions minutes forever or dying silently. A separate integrity scanner fingerprints each skill's network destinations so one that quietly starts calling a new host after being approved gets caught, without flagging every wording edit as a false alarm.
Tradeoffs
Setup is heavier than it looks: this is a monorepo of separately versioned apps with deliberately mismatched toolchains that must be installed in a fixed order, not one workspace. The blast-radius tags an operator sees before installing a skill pack are documentation only; nothing enforces them. One of the nine supported agent CLIs cannot be restricted by a tool allowlist at all and aborts its whole run on a denied action, leaning entirely on the sandbox layer underneath. Two harnesses report only an estimated token count, and the newest one has not run live against a real credential yet.
How to use it well
This fits an operator who wants a fleet of small background jobs (research digests, dependency and PR triage, on-chain monitoring) running on a schedule they do not babysit, and who is comfortable owning a forked repository and its GitHub Actions minutes rather than a hosted product. Start with the three packs shown by default and the strictest write tier before turning on anything that moves money or opens external accounts; the financial and on-chain skills are real capabilities, not demos, and deserve the same caution as production credentials. It is the wrong tool for someone who wants a single reviewed assistant inside their existing editor.
Technical notes+
package.json is a non-workspace root aggregator: apps/dashboard, apps/cli, apps/mcp-server and apps/webhook each keep a separate lockfile and deliberately different toolchain pins, so bootstrap installs them in a fixed order instead of hoisting dependencies. apps/cli/src/index.ts is a thin CLI that reuses the dashboard's own library code, so its commands return exactly what the dashboard's API routes do, and fails closed on an unrecognized subcommand. apps/mcp-server/src/index.ts turns every loaded skill into an MCP tool named aeon-{slug} and spawns the same run-harness path GitHub Actions uses, so a local MCP call and a scheduled run execute identically; OpenTelemetry tracing is wired in but inert unless an endpoint is configured. docs/CONFIGURATION.md and docs/CAPABILITIES.md document the three-layer mode: read-only enforcement, tool allowlist stripping, then an OS sandbox, then a post-run revert, plus the six-value capabilities taxonomy validated at install time. docs/skill-integrity.md describes a lockfile that fingerprints each skill's content hash and network egress, failing CI only when a skill reaches a new host or introduces a new critical finding. aeon.yml lists dozens of individually schedulable skills with per-entry cron, var, harness and model overrides.
Observed
- License
- MIT
- Language & packaging
- TypeScript across a multi-app repository (dashboard, CLI, MCP server, webhook), each app pinned to its own dependency lockfile rather than a shared workspace; requires Node.js 20 or newer
- Interfaces
- Command-line tool, an MCP server exposing each skill as a callable tool, a web dashboard, and scheduled GitHub Actions workflows
- Read-only sandbox platform support
- OS-level write-lock sandbox on Linux and macOS; falls back to advisory (unenforced) elsewhere
- Repository structure
- No README.md at the repository root; the canonical README is linked from documentation pages in a subdirectory instead
- Skill taxonomy
- Every skill declares exactly one of six fixed categories, and one of two write-access modes (read-only or write)
- Supported agent CLI backends
- Nine, unified behind one internal execution contract
Read from package.json, apps/cli/src/index.ts, apps/mcp-server/src/index.ts, docs/CORE.md, docs/CAPABILITIES.md, docs/CONFIGURATION.md, docs/ECOSYSTEM.md, docs/harnesses.md, docs/skill-integrity.md, docs/skill-packs.md, LICENSE, STRATEGY.md, aeon.yml.
What it can do
Monitor cryptocurrency prices and trends
Cryptocurrency symbols and monitoring schedule → Price alerts and trend analysis reports
Conduct automated research on specified topics
Research query and parameters → Compiled research report with findings
Generate and review code automatically
Code requirements or existing codebase → Generated code files or code review feedback
Execute scheduled productivity tasks
Task definitions and schedule configuration → Completed task results and status reports
Run development tooling operations
Development workflow specifications → Automated build, test, or deployment results
Perform background intelligence gathering
Data sources and intelligence targets → Intelligence reports and data insights
Tags
Tech Stack
Comments (0)
No comments yet
Editorially curated, with community endorsements as a secondary signal. Corrections welcome.