skill-upper

# use-skill-up-cli Help the user evaluate and evolve Agent Skills through the `skill-up` CLI. Manual: <https://alibaba.github.io/skill-up/> ## Language Policy **Default to English when responding to the user. If the user writes in Chinese (or any other language), switch to that language and stay consistent with the user's input throughout the session.** Detection rules (highest priority first): 1. The user explicitly specifies a language in the current message (e.g. "answer in English" or "reply in Chinese") → follow the user's instruction. 2. The natural language used in the user's current message → match it. 3. None of the above → use English (default). Regardless of the response language, technical identifiers in this SKILL — CLI commands, `eval.yaml` / `case.yaml` field names, report field names, etc. — MUST stay in their original English form. Do not translate them. ### Language Rules for Generated Artifacts Use the user's current language for replies, newly authored explanatory prose, and YAML comments. Preserve the language and semantics of test inputs, expected output literals, identifiers, commands, paths, and existing regression cases. Do not translate a deterministic assertion merely because the conversation language changed. When testing localized behavior, keep the original business text even in an English conversation. Treat `assets/*.tmpl` as structural references: adapt placeholder prose and comments to the requested language. Review only files created or changed for this task; never rewrite unrelated cases to enforce a language preference. Use short field-leading comments for generated YAML. Explain actual configured fields: `schema_version`, `environment.type`, `engine.name`, `cases.files`, and `report.formats` in eval.yaml; `id`, `title`, `input.prompt`, and `judge.type` in case YAML. For nested fields, place the comment inside the parent mapping immediately before the child field, rather than only before the parent. Comment `expect` and a top-level `judge` when present; do not add an optional field just to satisfy a comment check. Keep field names and enum values unchanged. `skill-up import` does not preserve template comments. ## What is skill-up `skill-up` is an evaluation CLI for Agent Skill authors. It installs the Skill into a real Agent Engine (Claude Code, Codex, qodercli, etc.), spins up an execution environment for each case, runs the prompt, then grades the result via declared rules / LLM judges / custom scripts, and finally produces a report. Typical layout: ``` my-skill/ SKILL.md evals/ eval.yaml cases/ <case-id>.yaml fixtures/ ``` ## When to trigger Use this skill in any of the following situations: - The user asks to "run / evaluate / verify / test this skill". - The user asks to "fix / improve / iterate / evolve this skill" from eval failures. - The user wants to "add evals, test cases, or regression cases to a skill". - The user asks to record explicitly attributed Skill usage, evidence, or feedback. - The user wants to review captured Skill observations or turn approved feedback into a regression case. - The user wants to edit `eval.yaml` / `case.yaml`, or asks you to choose an appropriate `judge` type. - The user mentions `skill-up run/validate/list-cases/report/import/init`. - The user wants to migrate from Anthropic `evals.json` to skill-up. - The current working directory contains `evals/eval.yaml` or `evals/evals.json` and the user wants to run it. ## Choose the requested workflow - **Diagnose or plan:** read the target Skill and supplied evidence, identify concrete problems, and propose changes or cases. Do not install tools, create eval files, change configuration, or run evaluations unless requested. - **Create or edit cases:** use Steps 1–4 to create or update the suite and validate its configuration. Stop before credentials and execution unless the user also requests a run. Report the actual config/case paths and validation status in the final reply. - **Evaluate:** follow Steps 0–7 for the requested suite; report failures without automatically editing the Skill. - **Improve:** follow the evaluation workflow and Step 8 within the requested scope. Preserve valid existing cases and assertions. ## Evaluation workflow ### Observation capture mode (optional) Use this mode when the user asks to record the current Skill interaction or feedback and observation tools are available. It requires either a current Codex release with the `codex-skill-up` plugin and lifecycle-hook support, or DSH with the skill-up plugin's observer explicitly enabled. Do not retrofit observation capture onto the pinned Codex 0.80.0 evaluation adapter. If the user asks how to install either host plugin, or the required observation tools are missing and the user asks to enable them, read `references/install.md` under "Install an observation host plugin (optional)". Do not install or enable a plugin unless the user asks. Claude Code, qodercli, Qwen Code, and other Agent Engines are not supported for this observation workflow. This restriction does not apply to normal `skill-up` evaluation runs, which remain multi-engine. 1. Attribute the interaction only when the user explicitly references a Skill (for example, `$my-skill`) or the responsible Skill is otherwise known with certainty. 2. In Codex, if explicit attribution is absent but certain, call `mark_skill_invocation` once with the Skill name and optional version. DSH derives exact attribution from its durable `/skill` injection or successful `skill` tool result; do not add inferred attribution. 3. In Codex, use `attach_skill_evidence` only for references that help reproduce or evaluate the behavior. Prefer paths and short summaries over copied file contents. 4. In Codex, use `record_skill_feedback` during the observed turn. In DSH, the observer stores the next user turn in the same session as an unclassified follow-up candidate. For explicit feedback, use `record_observation_feedback` with the completed observation's ID. Never invent sentiment or comments. Both adapters redact common credential shapes before local persistence, but still avoid sending secrets to marker tools. If the request is capture-only, stop after recording it; do not edit, evaluate, commit, or publish a Skill unless the user also asks for that work. ### Observation input mode (optional) When the request starts from a captured Skill observation, read `references/observations.md` first. Use the available observation tools to inspect and preview the observation, require explicit approval for the exact candidate, write it only after approval, and then continue at Step 4. If the request does not start from an observation, use the normal flow below. ### Step 0: Make sure skill-up is installed Before doing anything, verify `skill-up` is available: ```bash command -v skill-up && skill-up --version ``` If a version is printed, continue. If you see `command not found`, on **macOS / Linux**: ```bash curl -fsSL https://raw.githubusercontent.com/alibaba/skill-up/main/install.sh | bash export SKILL_UP_VERSION=v0.1.0 curl -fsSL https://raw.githubusercontent.com/alibaba/skill-up/main/install.sh | bash export INSTALL_DIR="$HOME/bin" curl -fsSL https://raw.githubusercontent.com/alibaba/skill-up/main/install.sh | bash ``` > **Platform:** `skill-up` currently supports **macOS / Linux** only; Windows is not supported. After installing, run `skill-up --version` again. If the command is still missing, add `~/.local/bin` to `PATH`. More details, including optional Codex and DSH observation host plugins: `references/install.md`. ### Step 0.5 (optional): User config and telemetry For OTLP defaults, `runtime_kwargs` (e.g. OpenSandbox `base_url`), etc.: ```bash skill-up init skill-up init --local skill-up init --print skill-up init --force ``` Precedence (low → high): embedded empty defaults < user config < project `.skill-up.yaml` < `--config`. `SKILL_UP_CONFIG` can point at the user config file (env var name is historical). See the upstream README "User config". ### Step 1: Locate the target Skill 1. Identify the root directory of the target Skill (the directory containing `SKILL.md`). Search in this priority: user path → nearest `SKILL.md` upward from CWD → recently viewed files. 2. Read the target `SKILL.md` for scope, triggers, and dependencies. Preserve localized behavior and expected literals when designing cases. 3. Check `evals/`: - `evals/eval.yaml` exists → Step 4 (optionally Step 3). - Only `evals/evals.json` → `references/migrate-anthropic.md` (`skill-up run --auto` or `skill-up import`). - Nothing → Step 2. ### Step 2: Scaffold the evals (only when none exist) - Copy `assets/eval.yaml.tmpl` to `<skill-root>/evals/eval.yaml`. - Copy `assets/case.yaml.tmpl` to `<skill-root>/evals/cases/<case-id>.yaml`. Adapt language per "Language Rules for Generated Artifacts". Replace the templates' generic English placeholders with case-specific prose in the requested output language. Preserve short field-leading comments in generated YAML; translate them when the output language differs from English, while keeping field names and enum values in English. Selection guidelines: - `environment.type`: use `none` for pure-text Skills; use `opensandbox` when you need a remote sandbox (set `OPENSANDBOX_API_KEY`, put non-secrets in `environment.kwargs`). - `engine.name` + `engine.model`: default `claude_code`; `model` is optional. For `qodercli`, often omit `model`. - `judge.type`: `rule_based` (preferred), `script`, `agent_judge` (expensive) — see `references/judge-types.md`. - Case ID = filename without `.yaml`; prompts should exercise real Skill value. - For interactive cases, preserve fixed `input.turns[].content` where the user message is known. Use `input.turns[].respond` only for a reply that must react to the agent, or `input.prompt` plus case-level `user_simulator.scenario` when the simulator should choose all later replies and when to stop. Set an independent eval-level `user_simulator` model and a bounded `max_turns`; see both YAML references. Do not reuse `engine.model` or `judge.model` as an implicit simulator model. See `references/eval-yaml.md` and `references/case-yaml.md`. ### Step 3: Fill the gaps (when evals already exist) - `skill-up list-cases <path>` - Review `eval.yaml` and representative cases; avoid `agent_judge` abuse. - Add or edit YAML under `cases/` as needed. - If adding simulated user turns, check that the selected engine supports session resumption, configure the separate simulator connection, and retain deterministic fixed turns and judge assertions wherever possible. ### Step 4: Validate the configuration ```bash skill-up validate <skill-root>/evals/eval.yaml ``` Expect: `✓ eval.yaml is valid (loaded N case(s))`. For a case-authoring request, finish here with the config and case paths plus validation status. Do not start a background evaluation as part of scaffolding. ### Step 5: Prepare credentials Check authentication for the selected engine and provider. Provider API keys resolve from `--api-key`, environment variables, or `~/.skill-up/credentials.yaml`; engine-specific tokens and saved login sessions follow the engine's auth rules. Never dump environment variables or credential files, or print secret values into tool output, logs, or the conversation. Check only whether a relevant variable has a non-empty value. For example, for an OpenAI API-key workflow: ```bash if printenv OPENAI_API_KEY | grep -q .; then printf '%s\n' 'OPENAI_API_KEY: configured' else printf '%s\n' 'OPENAI_API_KEY: not configured in the environment' fi ``` An absent environment variable does not rule out file-based credentials or an existing engine login. If the selected authentication path is unavailable, **stop and ask** the user to configure it locally; do not ask them to paste a secret into the conversation or write secrets into YAML without consent. The user simulator resolves credentials by its own `user_simulator.provider`. For `provider: simulation`, set `SIMULATION_API_KEY` and `SIMULATION_BASE_URL`, or configure the `simulation` provider in `~/.skill-up/credentials.yaml`. For `provider: openai`, use `OPENAI_API_KEY` and optionally `OPENAI_BASE_URL`; its standard endpoint is the fallback. Choose `protocol: openai` for Chat Completions or `protocol: anthropic` for Messages; custom base URLs must match that protocol. For `provider: anthropic` with `protocol: anthropic`, use `ANTHROPIC_API_KEY` and optional `ANTHROPIC_BASE_URL` (official endpoint fallback). `--api-key` for the agent does not supply the simulator connection. Never put a raw key in eval YAML. For `opensandbox`, also ensure `OPENSANDBOX_API_KEY` (and related env) as needed. ### Step 6: Run the evaluation ```bash skill-up run <skill-root>/evals/eval.yaml ``` | Scenario | Command | | -------------------------------- | ------------------------------------- | | Subset | `--include-case-name "basic-*"` | | Exclude | `--exclude-case-name "*-flaky"` | | HTML report | `--format html` | | Engine override | `--engine codex --model openai/gpt-4` | | Parallelism | `--parallelism 4` (1–256) | | Anthropic JSON | `--auto` | | Stability/flakiness sampling | `--iteration 3` | | Auto-append after last iteration | `--iteration 0` (default behavior) | | Verbose | `-v`, `-vv` | Exit `0` = all passed; `1` = failure or error — suitable for CI. When an explicit positive `--iteration N` runs more than one sample, inspect the terminal's simple current-command summary for lines like `case_a: 3 trials, 2 PASS, 1 FAIL -> flaky`. ### Step 7: Interpret the report Artifacts under `<skill-root>/<skill-name>-workspace/iteration-N/`: - `result.json`, `benchmark.json`, optional `report.html` - `<case-id>/with_skill/grading.json`, `outputs/` Summarize: pass rate and timing; for failures, case id, assertion `text`, and `evidence`; benchmark deltas if enabled; offer HTML path or `skill-up report result.json --format html`. For simulated cases, inspect each turn's `source` and any `stop_reason` alongside the transcript. Hitting `max_turns` is an execution error; a simulator stop does not itself mean PASS, because the configured judge still grades the result. ### Step 8: Evolve the Skill when requested Only enter this loop when the user asks to fix, improve, iterate, or evolve the target Skill. If the user only asks to evaluate or report results, stop after Step 7 without modifying it. 1. Diagnose failures from `result.json`, `grading.json`, and output evidence. 2. Fix `SKILL.md` or supporting files when the Skill behavior is incorrect. 3. Add or refine eval cases when coverage is missing. 4. Do not weaken valid assertions merely to make a failure pass. 5. Rerun failed cases first, then run the full eval suite. 6. Continue until the evals pass or clearly report what remains blocked. ## Command quick reference | Command | Purpose | | --------------------------------------------- | --------------------------------- | | `skill-up validate <eval.yaml>` | Validate before `run`. | | `skill-up list-cases <eval.yaml>` | List cases. | | `skill-up run [eval.yaml]` | Run evals. | | `skill-up run --auto` | Run from `evals/evals.json`. | | `skill-up report <result.json> --format html` | Re-render reports. | | `skill-up import <evals.json>` | Convert Anthropic format to YAML. | | `skill-up init` | Write user-config template. | | `skill-up debug judge <input.json>` | Debug judge. | | `skill-up debug report <input.json>` | Debug report. | Full flags: `references/cli.md`. ## Common pitfalls - Model IDs vs proxy aliases — preserve what works for the user's `base_url`. - `opensandbox` without `OPENSANDBOX_API_KEY` — auth failures. - Match the language of deterministic assertions to the behavior under test, not merely to the conversation language. - Abusing `agent_judge`. - Anthropic `evals.json` expectations → default `agent_judge`; use `import` + hand edits for deterministic checks. - Paths relative to Skill root (`SKILL.md` directory). - A simulator reply is literal user text, including `{{placeholders}}`; captured-variable substitution belongs only in configured fixed turns and `respond` instructions. - `--iteration 0` appends one run after the latest existing iteration without summarizing history; positive `--iteration N` runs N samples of the selected cases and, when N > 1, prints a simple stability/flakiness summary covering only samples from the current command. ## References - `references/install.md` - `references/eval-yaml.md` - `references/case-yaml.md` - `references/judge-types.md` - `references/cli.md` - `references/migrate-anthropic.md`