# Show HN: Skill-up – Regression testing for Agent Skills > Source: > Published: 2026-07-30 14:03:43+00:00 **skill-up** is an evaluation and evolution tool for Agent Skills. **Evaluation** makes Skill quality measurable and repeatable: declarative YAML cases run across multiple Agent Engines, use rule, script, or Agent judges, and produce structured reports locally or in CI.**Evolution** turns those results into the next improvement: through conversation,**skill-upper** reads failures, automatically repairs or expands the eval suite, reruns skill-up, and keeps iterating with you. **Eval-to-Evolution Loop with skill-upper**: Create evals through natural conversation, diagnose failures, automatically repair or expand cases, and rerun skill-up until the eval suite evolves.**Declarative Eval Config**: Define evaluation environment, engine, model, and cases through YAML (`eval.yaml` +`cases/*.yaml` ).**Multi-Engine Support**: Works with Qoder CLI, Claude Code, and Codex as built-in Agent Engines, plus user-defined agents via`engine.custom` (local transport — see[docs/design/custom-engine.md](/alibaba/skill-up/blob/main/docs/design/custom-engine.md)).**Flexible Judging**: Supports`rule_based` ,`script` , and`agent_judge` evaluation strategies.**Structured Reports**: Outputs Anthropic-compatible`grading.json` ,`benchmark.json` ,`benchmark.md` , plus`result.json` , JUnit XML, and HTML reports.**Anthropic Compatible**: Import`evals.json` via`skill-up import` , or auto-detect with`--auto` .**CI-Ready**: Designed for local development and continuous integration pipelines. The official [Agent Skills evaluation guide](https://agentskills.io/skill-creation/evaluating-skills) describes the right evaluation loop: write realistic cases, run with and without the Skill, grade outputs, aggregate results, and iterate. `skill-up` turns that workflow into a reusable CLI: - Replaces ad hoc run folders with a declarative `eval.yaml` +`cases/*.yaml` format. - Closes the improvement loop: skill-upper can interpret failed reports, repair or add eval cases, and drive the next skill-up run through conversation. - Automates workspace setup, Skill installation, Agent Engine invocation, judging, and report generation. - Supports multiple engines ( `claude_code` ,`codex` ,`qodercli` ,`qwen_code` ) instead of tying the workflow to one client. - Keeps compatibility with Anthropic-style `evals.json` while adding richer judges, CI-friendly commands, and structured reports. The recommended way to use skill-up is through **skill-upper**, the Agent Skill shipped in this repository. It lets your AI agent create evals, run skill-up, understand failures, fix the Skill or its evals, add regression coverage, and repeat the loop through conversation. ``` # Codex, global install npx skills add https://github.com/alibaba/skill-up/tree/main/skills/skill-upper -g -a codex -y # Claude Code, global install npx skills add https://github.com/alibaba/skill-up/tree/main/skills/skill-upper -g -a claude-code -y ``` You normally do not need to install skill-up first. skill-upper checks for the CLI when it runs and guides the agent through installation if needed. Open a project that contains your Skill's `SKILL.md` in Codex, Claude Code, or another compatible Agent, then ask: ``` Use skill-upper to evaluate this Skill. Read SKILL.md, identify its most important behaviors, create realistic eval cases with appropriate judges, validate the configuration, and run skill-up. Summarize the results and the highest-impact failures. ``` skill-upper creates the declarative eval suite and drives the CLI for you: ``` my-skill/ SKILL.md evals/ eval.yaml cases/ .yaml my-skill-workspace/ iteration-1/ result.json ``` Continue in the same conversation: ``` Review the latest skill-up results. For each failure, determine whether the Skill or the eval is wrong. Fix SKILL.md and supporting files, or repair the eval case and judge as appropriate. Add regression cases for the bugs you found, rerun skill-up, and continue until the important behaviors pass. ``` This is the evolution loop: reports become fixes, fixes become regression cases, and every iteration makes the Skill and its eval suite stronger. You can still install the CLI directly and hand-write `eval.yaml` and case files: ``` curl -fsSL https://raw.githubusercontent.com/alibaba/skill-up/main/install.sh | bash ``` See the official documentation for [Getting Started](https://alibaba.github.io/skill-up/guide/getting-started), [Writing Evals](https://alibaba.github.io/skill-up/guide/writing-evals), [CLI Reference](https://alibaba.github.io/skill-up/guide/cli-reference), and [User Configuration](https://alibaba.github.io/skill-up/guide/user-config). Windows-specific setup and limitations are covered in the [Windows guide](https://alibaba.github.io/skill-up/guide/windows). skill-up auto-loads an optional user-level config that supplies default OpenTelemetry env vars and per-environment runtime kwargs. The embedded defaults are empty; downstream consumers maintain their own config file. ``` embed (empty) < user (~/.config/skill-up/config.yaml) < project ($PWD/.skill-up.yaml) < explicit (--config) ``` | Source | Path | |---|---| `embed` | empty `Config{}` — no vendor defaults baked in | `user` | `$SKILL_UP_CONFIG` , else `$XDG_CONFIG_HOME/skill-up/config.yaml` , else `~/.config/skill-up/config.yaml` | `project` | `$PWD/.skill-up.yaml` | `explicit` | `--config ` (must exist) | Missing files at the `user` and `project` layers are silently skipped; a missing `--config` path is a hard error. A corrupt config at any layer also fails the run. ``` skill-up init # writes a template to ~/.config/skill-up/config.yaml (XDG-aware) skill-up init --local # writes a template to $PWD/.skill-up.yaml skill-up init --print # prints the template to stdout skill-up init --force # overwrite an existing file skill-up init --config foo.yaml # reads foo.yaml, writes it to ~/.config/skill-up/config.yaml skill-up init --config foo.yaml --local # reads foo.yaml, writes it to $PWD/.skill-up.yaml ``` With `--config ` , `init` reads that file (validating it as a skill-up config) and writes its raw bytes to the target — comments and formatting are preserved. Without `--config` , `init` writes a commented YAML template. ``` schema_version: v1alpha1 kind: SkillUpConfig telemetry: service_name: skill-up # OTEL_SERVICE_NAME traces_exporter: otlp # OTEL_TRACES_EXPORTER traces: endpoint: http://localhost:4317 # OTEL_EXPORTER_OTLP_TRACES_ENDPOINT (4317 for grpc, 4318/v1/traces for http/protobuf) protocol: grpc # OTEL_EXPORTER_OTLP_TRACES_PROTOCOL (grpc | http/protobuf); skill-up defaults to grpc resource_attributes: # serialized into OTEL_RESOURCE_ATTRIBUTES deployment.environment: local verbose: false # if true, also enables OTEL_LOG_* payload capture env: # arbitrary defaults, applied only-if-unset OTEL_EXPORTER_OTLP_HEADERS: authorization=${OTLP_TOKEN} runtime_kwargs: # keyed by environment.type opensandbox: base_url: http://localhost:8080 # extensions: '{}' ``` For environment variables: any value already set in the process environment wins; the config only fills in missing keys. For `runtime_kwargs` : explicit `--runtime-kwarg` on `run` > `eval.yaml` `environment.kwargs` > user-config `runtime_kwargs[environment.type]` . Prefer `${ENV_VAR}` references inside the config file rather than baking secret literals. The redaction mechanism (`userconfig.Redact` ) masks fields tagged `secret:"true"` when printing; currently no Config field carries the tag, but the mechanism is in place for future fields. Use `skill-up import` to migrate an Anthropic-compatible `evals.json` into the YAML layout used by this repo: ``` skill-up import ./evals/evals.json --output ./evals ``` | Command | Description | |---|---| `skill-up run [path]` | Run evaluation cases and produce reports | `skill-up validate [path]` | Validate `eval.yaml` and case files | `skill-up list-cases [path]` | List all cases referenced by the config | `skill-up report ` | Generate reports from a previous run | `skill-up import ` | Import Anthropic `evals.json` to YAML cases | `skill-up debug judge ` | Debug judge module with a JSON input | `skill-up debug report ` | Debug report module with a JSON input | Run your Agent Skill evals in CI on every pull request — and check the same skill **across engines** (`claude_code` / `codex` / `qodercli` / `qwen_code` ) in one step. This repo ships an action at its root ([ action.yml](/alibaba/skill-up/blob/main/action.yml)): ``` # .github/workflows/skill-eval.yml name: Skill Eval on: pull_request: paths: ['skills/**', 'evals/**', '**/SKILL.md'] jobs: eval: runs-on: ubuntu-latest # Docker container action — Linux only steps: - uses: actions/checkout@v4 - uses: alibaba/skill-up@main # see "Versioning" below with: engine: claude_code # or codex / qodercli / qwen_code; empty = let eval.yaml decide api-key: ${{ secrets.ANTHROPIC_API_KEY }} base-url: https://api.anthropic.com # your model endpoint skill-target: evals/eval.yaml ``` Requirements for the caller: a **Linux** runner (it's a Docker container action), and your model credential stored as a repo secret. The runner image is public, so no extra registry auth is needed. Key inputs: `engine` , `model` , `provider` , `api-key` , `base-url` , `skill-target` , `parallelism` . The action prebuilds skill-up + the three engine CLIs into its runner image, so a run is just "pull image, eval". See [ action.yml](/alibaba/skill-up/blob/main/action.yml) for the full input/output reference. The container image includes a deliberately pinned skill-up CLI version. The version is not resolved from `latest` when a workflow starts, so a given image digest always runs the same CLI. The `skill-up-version` input is only a fallback for a custom image that does not already contain the `skill-up` binary. The official image contains the binary, so this input cannot override its bundled version. To find the effective version, check the `skill-up --version` line in the Action log. Publishing a new skill-up CLI release does not automatically update the GitHub Action image. Maintainers must synchronize the pinned version, publish and test a new runner image, and update the image digest in `action.yml` . The complete maintainer procedure is documented in the [CI maintenance runbook](/alibaba/skill-up/blob/main/docs/guide/ci-maintenance.md#manual-skill-up-version-synchronization). The production Action must use an immutable `sha256:` image digest. Do not replace it with `skill-up-runner:latest` ; a mutable tag would allow existing Action references to change behavior without a repository commit. `uses:` points at any git ref that contains `action.yml` . Pin a **release tag** (the first release that includes the action onward) or a commit SHA for stability; `@main` always tracks the latest. Release tags published **before** the action was added do not contain `action.yml` and cannot be used as the ref. A CLI release tag captures the `action.yml` and runner-image digest that existed when that tag was created. Because the current runner image is refreshed manually after CLI release assets become available, do not assume that a CLI tag automatically contains an Action image with the same CLI version. Until a separate Action release tag process is introduced, use a post-refresh commit SHA for an immutable reference or `@main` when intentionally following Action updates. Apache License 2.0 — see [LICENSE](/alibaba/skill-up/blob/main/LICENSE).