Orchestrator skills, agent prompts, and state management for the Jira autofix pipeline
npx claudepluginhub opendatahub-io/autofix-skillsUse when orchestrating CVE remediation for a Jira Vulnerability ticket. Uses a Python state machine (cve_pipeline.py) for deterministic routing between scan, fix, verify, VEX, review, and PR creation agents. Never writes fix code directly.
Use when researching a Jira spike ticket with no associated repository. Investigates the topic and writes structured findings to autofix-output/.autofix-verdict.json.
Use when orchestrating a Jira ticket fix end-to-end. Dispatches to implement and review prompt agents in a loop, uses state.py for persistence, and evaluates findings to decide iteration. Never writes code directly.
Use when assessing a Jira bug ticket for AI autofix readiness. Produces a structured JSON verdict (ready/needs_info/not_fixable) based on a three-gate rubric. Designed for CI pipeline use with the autofix pipeline orchestrator.
Ultra-compressed communication mode. Cuts ~75% of tokens while keeping full technical accuracy by speaking like a caveman.
Memory compression system for Claude Code - persist context across sessions
Intelligent prompt optimization using skill-based architecture. Enriches vague prompts with research-based clarifying questions before Claude Code executes them
Qiushi Skill: methodology skills for AI agents guided by seeking truth from facts, with Claude Code, Cursor, OpenClaw, Codex, OpenCode, and Hermes guidance.
Share bugs, ideas, or general feedback.
Claude Code plugin for the Jira autofix pipeline. Provides orchestrator skills, agent prompt files, and deterministic Python scripts for automated bug fixing, CVE remediation, and ticket triage.
This plugin is the inner layer of the autofix pipeline, the skills that run inside the Claude Code container. The outer layer (Python orchestration, GitLab CI, ticket management) lives in jira-autofix and ai-agentic-lib.
| Skill | Description |
|---|---|
autofix-resolve | Orchestrates end-to-end bug fixing: implement, review, evaluate loop (max 3 iterations) |
autofix-cve-resolve | CVE remediation across multiple repos with state-machine dispatch |
autofix-triage | Assesses bug tickets for AI autofix readiness (ready/needs_info/not_fixable) |
autofix-research | Investigates spike tickets with no associated repository |
| Script | Description |
|---|---|
merge_findings.py | Merges core review findings with extension findings, tags each with source |
state.py | State persistence utility for context-compression recovery |
cve_pipeline.py | CVE state machine: deterministic routing, wave dispatch, progress polling |
This plugin is pre-installed in the ghcr.io/opendatahub-io/ai-helpers container image. For local development:
git clone git@github.com:opendatahub-io/autofix-skills.git
# Install as a Claude Code plugin (local directory source)
claude plugin install /path/to/autofix-skills
make lint
See CONTRIBUTING.md for the full development workflow.
See AGENTS.md for architecture details and conventions.
Team-specific extension skills go in ai-helpers, not this repo. Extensions are normal Claude skills that read from .autofix-context/ and write findings to .autofix-context/extension-findings/<skill-name>.json.
Use git tags (v0.1.0, v0.2.0, etc.) for releases. The outer-layer runner pins a release via SkillConfig.skill_ref. The main branch is the development head.
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge.
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge.
Sign in to claim