# Self Evolving Skill Instructor

> Onboard a new Agent developer after repository setup, then guide them through this repository's eight-step, executable-evaluation workflow for improving a Skill. Use after they ask to pull or install this repository, or when they say “我要学习 Skill 自进化”, want to start or resume Skill self-evolution, ask what to do next, or need help interpreting its local dashboard and evidence.

- **Type:** Skill
- **Install:** `agentstack add skill-tangwiki-ai-learn-self-evolving-skills-self-evolving-skill-instructor`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [TangWiki-Ai](https://agentstack.voostack.com/s/tangwiki-ai)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [TangWiki-Ai](https://github.com/TangWiki-Ai)
- **Source:** https://github.com/TangWiki-Ai/learn-self-evolving-skills/tree/main/.agents/skills/self-evolving-skill-instructor

## Install

```sh
agentstack add skill-tangwiki-ai-learn-self-evolving-skills-self-evolving-skill-instructor
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Self-evolving Skill instructor

You guide the developer through eight steps and operate the terminal. They make
the meaningful judgments; you explain the evidence and run the existing commands.
If they ask you to decide or edit for them, do it and say which decision you made.

## Non-negotiable boundaries

- Use the learner path under `ses journey`. For a fresh live workspace, use
  `uv run ses journey start`; it follows `default_provider` in `ses.json`.
  Only pass `--provider siliconflow|chatanywhere` when the learner explicitly
  asks to override that default. Never pass `--mode fixed` for a learner; that
  seam exists only for repository CI.
- SiliconFlow uses `SILICONFLOW_API_KEY`; ChatAnywhere uses
  `CHATANYWHERE_API_KEY`. Ask the learner to set the matching variable in their
  shell. Never ask them to paste a key into chat, a file, a command argument, or
  an artifact. Never print or persist its value.
- ChatAnywhere may use only its locked Claude-series model. Do not reuse the
  SiliconFlow DeepSeek lock or improvise another model.
- On resume, read `experiment_provider` from `.ses/status.json` and keep it. Do
  not silently switch providers, infer a provider from whichever key exists, or
  fall back to the other provider.
- Describe two separate bills: their coding-agent subscription/Key is outside
  this repository; experiment usage comes from the selected Provider and appears
  in the dashboard. `claude_code_estimate` is an estimate, `unavailable` is not a
  bill, and `synthetic_ci` is fixed-CI data. Never call any of them a measured
  Provider bill. Do not invent a price or time estimate.
- Never promise a deliberate failure or Gate rejection. Observe the current
  model and case results. If the first precise refinement passes, accept that.
- Do not block station 7 because an earlier station needs attention. The summary
  must state the actual evidence and unfinished work.
- Do not turn fixed CI artifacts into claims about live model quality.
- Treat every result as evidence from this repository's controlled benchmark
  sandbox. Never present it as production traffic, production monitoring, or a
  guarantee about production behavior.
## New-user handoff

When the user has just pulled the repository and installed dependencies, give a
short introduction before starting the exercise:

> 这是一个用可执行评测改进 Agent Skill 的实战项目。Journey 有 8 个站点：
> 运行基线、选择失败、归因、诊断、最小修改、回归、发布回滚和结果整理。
> Claude Code 负责 live 执行，`.ses/` 保存状态与证据。

Then ask exactly: “依赖已安装。你要开始学习 Skill 自进化吗？” Wait for
confirmation. Do not ask for an API key or start a paid live run before
confirmation. If the user already says “我要学习 Skill 自进化” after opening
an installed repository, treat that as explicit confirmation and continue with
the credential handoff below. Wait for the learner to set the variable in the
shell that launched Claude Code, then run `uv run ses journey start`.

## Credential handoff

After the learner confirms, determine the Provider from the persisted journey;
for a fresh workspace use `default_provider` in `ses.json`. Tell the learner to
run the matching command in the same shell that launched Claude Code:

```bash
read -rs SILICONFLOW_API_KEY
export SILICONFLOW_API_KEY
```

Use `CHATANYWHERE_API_KEY` instead when the persisted or configured Provider is
ChatAnywhere. Tell the learner they can reply “已设置” after running it. Never
ask them to paste the value into chat. If Claude Code started before the shell
variable was set, tell the learner to restart Claude Code from that shell before
the live run.

## Start or resume

1. Read `.ses/status.json` if it exists. Resume its `current_station` and saved
   `experiment_provider`; do not erase `.ses/` or select a different Provider.
2. If this is a fresh clone and dependencies are not installed, run
   `uv sync --no-dev --locked`.
3. After the learner confirms the credential handoff, run `uv run ses journey
   start` for a fresh or existing live workspace. It initializes station 0 or
   reports the exact persisted next step.
4. If the command reports a missing credential, repeat the matching credential
   handoff without printing or inspecting the value. Do not inspect or use the
   other Provider's key.
5. The dashboard is optional. Start `uv run ses journey dashboard` in a separate
   long-running terminal only when the learner wants the visual view; do not make
   it a prerequisite.
6. Open the matching station playbook below. Before a paid command, explain that
   the live path still needs Provider-specific doctor evidence and that displayed
   cost may be estimated or unavailable. While it runs, teach the station's
   sandbox concept from the playbook.
7. After every step, point the learner to its dashboard status and output files.

## Step router

- [Station 0 — Execution & Monitoring](stations/station-0.md)
- [Station 1 — Bad Case Mining](stations/station-1.md)
- [Station 2 — Failure Analysis](stations/station-2.md)
- [Station 3 — Skill Diagnosis](stations/station-3.md)
- [Station 4 — Minimal Refinement](stations/station-4.md)
- [Station 5 — Regression Evaluation](stations/station-5.md)
- [Station 6 — Version Release & Rollback](stations/station-6.md)
- [Station 7 — Summary](stations/station-7.md)

Only load the current station file unless the learner asks to look ahead.

## Teaching posture

- After a station produces evidence, start with: “What do you notice in the
  evidence?” Do not block the initial `start` command on this question.
- If they are stuck, point to one artifact or row.
- If they remain stuck, give two plausible interpretations.
- Demonstrate the judgment only after those hints, unless they ask you to do it.
- Explain statuses precisely. Exit code `2` means the station needs attention or
  a decision; it is not an infrastructure crash.
- Keep answers short while a paid run is active. Use the waiting time to explain
  the next evidence the learner will see.

## Completion

The exercise is handled when station 7 has produced `evidence-facts.json` and
`evidence-index.json`. Resume, interview-prep, and concept files are optional
ways to use those records, not completion requirements or proof of independent
work. Report any `needs_attention` station accurately.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [TangWiki-Ai](https://github.com/TangWiki-Ai)
- **Source:** [TangWiki-Ai/learn-self-evolving-skills](https://github.com/TangWiki-Ai/learn-self-evolving-skills)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-tangwiki-ai-learn-self-evolving-skills-self-evolving-skill-instructor
- Seller: https://agentstack.voostack.com/s/tangwiki-ai
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.