Comet:将想法转化为可评估工作流的智能体技能框架
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╚═════╝ ╚═════╝ ╚═╝ ╚═╝╚══════╝ ╚═╝Comet is a resumable long-running task workflow and Skill platform for coding.
It provides two independent requirements workflows: Native for strong models, powered only by Comet's own runtime, and Classic, which preserves the full OpenSpec + Superpowers phase-governance model. Comet also covers Skill creation, evaluation, and release.
It allows you to use a toolchain to handle everything from requirements to archiving, combine any skill to make it like Comet, evolving your skills based on scientific Rubric, Pass@k, and Pass^k scoring.
[!IMPORTANT] Comet 0.4.0 stable release — Building on 0.3.9, this release adds Native workflows, multi-agent collaboration, memory and knowledge management, and a complete toolchain for creating, distributing, and evaluating Skills.
- Native workflows for strong models: After requirements are confirmed, the Agent chooses how to plan, implement, test, and review. Comet handles state checks, acceptance, and resumable archive. Native runs independently of Classic, which retains the OpenSpec + Superpowers five-phase methodology; both share configuration, status, Dashboard, and Eval entry points.
- Parallel delivery for complex requirements: Supervisor Changes split a goal into dependency-aware child changes. Independent Codex sessions or Claude Code Agent Teams implement and verify them in isolated worktrees, then integrate them in dependency order before the parent change's final acceptance.
- Manageable Personal Memory and Project Knowledge: Retain collaboration preferences and reusable experience across tasks, and progressively supply context relevant to the current task. You can inspect, correct, forget, or pause it; new lessons start in trial and gain supporting evidence through actual adoption and verification.
- Skill creation, distribution, and evaluation: Compose any Skills with
/comet-anyand package them for distribution. Usecomet evalwith Rubric, Pass@k, Pass^k, and LangSmith to compare results and guide Skill improvements.- A unified three-pane Dashboard: Inspect Native and Classic progress, Git worktrees, acceptance results, and archives in your browser, and manage Personal Memory, Project Knowledge, and plugin settings.
- Cross-platform execution and recovery: A pure Node.js Runtime supports Windows, macOS, and Linux without Bash or WSL. Task state stays in the project so work can resume after interruptions or session changes; CLI output identifies the next step and decisions that need your input.
In the existing aligned Native vs. 0.4.0 Classic experiment (16 tasks, 48 runs per treatment), the 41 paired samples where both passed showed 76.8% fewer total tokens, 57.4% fewer Agent rounds, and 47.4% less time for Native. Across the full sample, Native achieved 87.5% pass^3 (+12.5 percentage points), while both treatments reached 100% pass@3. See the evaluation report for conditions and limitations, and the Changelog for all changes.
Native and Classic are not lightweight and heavyweight tiers, and neither upgrades into the other. Native is for strong models that can plan and verify autonomously; Classic is for scenarios that benefit from a complete phased methodology and stronger constraints.
/comet-native uses a detailed brief, complete target specifications, phase checks, and recoverable archive to constrain outcomes while leaving planning, implementation, testing, and review methods to the model. User-readable artifacts live under docs/comet/ by default, fully separate from Classic. See the Native workflow guide./comet-classic./comet reads only the project's .comet/config.yaml and deterministically forwards to /comet-native or /comet-classic. It does not guess from task size or mix changes, state, or directories across workflows. comet resume-probe uses the same configuration to resume through the correct permanent entry point./comet-any to organize them into distributable
Bundles, so Skills you create can be distributed to coding platforms with one command, much like comet init.When a requirement contains multiple deliverables that can be implemented and verified independently, a Supervisor Change confirms the child changes and their dependencies before coordinating independent Codex sessions or Claude Code Agent Teams. The Runtime remains responsible for worktrees, task identity, verification, ordered integration, and the parent change's final Verify.
Complex goal → child-change DAG → isolated worktrees → independent implementation and verification → ordered integration → final parent Verify
Codex multi-session execution
https://github.com/user-attachments/assets/96114cb0-f542-4f58-aa27-256f32adc46e
Claude Code Agent Teams execution
https://github.com/user-attachments/assets/41428669-a49a-46e3-a0ae-0775e4f4bb6f
With Comet, you only need to remember two skills and one command, covering coding, creation, and evaluation with an extremely low barrier to entry:
/comet to enter the project's configured Native or Classic workflow/comet-any to compose any Skillscomet eval to evaluate any SkillThe following charts are from 16 Comet workflow tasks, with 5 samples per treatment, comparing no Comet, Comet 0.3.9, and Comet 0.4.0.
The core observations were the differences in Pass@5, Pass^5, and Rubric scores. The baseline without Comet Skills only validated business behavior.
Many Comet capabilities have parallels in current industry practice.
To compare Comet with those patterns, see Comet Docs.
.comet.yaml, so an agent can continue after interruption instead of rereading documents and guessing progress.comet-guard.mjs, comet-yaml-validate.mjs, and comet-state.mjs check tasks, state fields, verification
evidence, and archive conditions before the workflow advances./comet-any composes arbitrary Skills. You describe your Skill
preferences, and the agent handles stability-related hooks, rules, scripts, and referenced Skill files for you.Requirements:
npm install -g @rpamis/cometInitialize Comet in the project where you want to use it:
cd your-project
comet init
# Invoke /comet in the hostInteractive setup explains and offers Native, Classic, or both. Native is for strong models that can implement and verify autonomously; Classic is for tasks th
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