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Opencli Autofix

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jackwener
opencli-autofix

Automatically fix broken OpenCLI adapters when commands fail. Load this skill when an opencli command fails — it guides you through collecting a trace artifact, patching the adapter, retrying, and filing an upstream GitHub issue after a verified fix. Works with any AI agent.

Overview

Publisherjackwener
RepositoryOpenCLI
Skill nameopencli-autofix
Stars
29.4K
Forks
2.9K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jackwener on GitHub. Read the source before you install it.

Installation

Install the Opencli Autofix AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jackwener/OpenCLI.git /tmp/OpenCLI
mkdir -p .claude/skills
cp -r /tmp/OpenCLI/skills/opencli-autofix .claude/skills/opencli-autofix
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Opencli Autofix in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Opencli Autofix on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Opencli Autofix is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OpenCLI AutoFix — Automatic Adapter Self-Repair

When an opencli command fails because a website changed its DOM, API, or response schema, automatically diagnose, fix the adapter, and retry — don't just report the error.

Safety Boundaries

Before starting any repair, check these hard stops:

  • AUTH_REQUIRED (exit code 77) — STOP. Do not modify code. Tell the user to log into the site in Chrome.
  • BROWSER_CONNECT (exit code 69) — STOP. Do not modify code. Tell the user to run opencli doctor.
  • CAPTCHA / rate limitingSTOP. Not an adapter issue.

Scope constraint:

  • Only modify the file at adapterSourcePath in the trace summary.md front matter — this is the authoritative adapter location (may be clis/<site>/ in repo or ~/.opencli/clis/<site>/ for npm installs)
  • Never modify src/, extension/, tests/, package.json, or tsconfig.json

Retry budget: Max 3 repair rounds per failure. If 3 rounds of diagnose → fix → retry don't resolve it, stop and report what was tried.

Prerequisites

bash
opencli doctor    # Verify extension + daemon connectivity

When to Use This Skill

Use when opencli <site> <command> fails with repairable errors:

  • SELECTOR — element not found (DOM changed)
  • EMPTY_RESULT — no data returned (API response changed)
  • API_ERROR / NETWORK — endpoint moved or broke
  • PAGE_CHANGED — page structure no longer matches
  • COMMAND_EXEC — runtime error in adapter logic
  • TIMEOUT — page loads differently, adapter waits for wrong thing

Before Entering Repair: "Empty" ≠ "Broken"

EMPTY_RESULT — and sometimes a structurally-valid SELECTOR that returns nothing — is often not an adapter bug. Platforms actively degrade results under anti-scrape heuristics, and a "not found" response from the site doesn't mean the content is actually missing. Rule this out before committing to a repair round:

  • Retry with an alternative query or entry point. If opencli xiaohongshu search "X" returns 0 but opencli xiaohongshu search "X 攻略" returns 20, the adapter is fine — the platform was shaping results for the first query.
  • Spot-check in a normal Chrome tab. If the data is visible in the user's own browser but the adapter comes back empty, the issue is usually authentication state, rate limiting, or a soft block — not a code bug. The fix is opencli doctor / re-login, not editing source.
  • Look for soft 404s. Sites like xiaohongshu / weibo / douyin return HTTP 200 with an empty payload instead of a real 404 when an item is hidden or deleted. The snapshot will look structurally correct. A retry 2-3 seconds later often distinguishes "temporarily hidden" from "actually gone".
  • "0 results" from a search is an answer. If the adapter successfully reached the search endpoint, got an HTTP 200, and the platform returned results: [], that is a valid answer — report it to the user as "no matches for this query" rather than patching the adapter.

Only proceed to Step 1 if the empty/selector-missing result is reproducible across retries and alternative entry points. Otherwise you're patching a working adapter to chase noise, and the patched version will break the next working path.

Step 1: Collect Trace Context

Run the failing command with failure-retained trace enabled:

bash
opencli <site> <command> [args...] --trace retain-on-failure 2>trace-error.yaml

On failure, stderr contains the normal error envelope plus a small trace block:

yaml
ok: false
error:
  code: SELECTOR
  message: "Could not find element: .old-selector"
trace:
  schemaVersion: 1
  opencliVersion: "..."
  traceId: "..."
  dir: "/path/to/.opencli/profiles/default/traces/..."
  summaryPath: "/path/to/.opencli/profiles/default/traces/.../summary.md"
  receiptPath: "/path/to/.opencli/profiles/default/traces/.../receipt.json"

Read summaryPath first. It is the LLM-oriented entry point and includes front matter:

yaml
---
schemaVersion: 1
opencliVersion: "..."
traceId: "..."
status: failure
site: "example"
command: "example/search"
adapterSourcePath: "/path/to/clis/example/search.js"
errorCode: "SELECTOR"
errorMessage: "Could not find element: .old-selector"
---

The artifact directory contains:

text
summary.md      # start here
receipt.json    # machine-readable trace receipt
trace.jsonl     # full redacted timeline
network.jsonl   # redacted network events
console.jsonl   # redacted console events
state/          # final snapshots when available
screenshots/    # final screenshots when available

If you redirected stderr to a file, read that file and copy trace.summaryPath.

Do not ask the user to rerun with legacy diagnostic env vars. Trace is the repair evidence path.

Step 2: Analyze the Failure

Read the trace summary and the adapter source. Classify the root cause:

Error CodeLikely CauseRepair Strategy
SELECTORDOM restructured, class/id renamedExplore current DOM → find new selector
EMPTY_RESULTAPI response schema changed, or data movedCheck network → find new response path
API_ERROREndpoint URL changed, new params requiredDiscover new API via network intercept
AUTH_REQUIREDLogin flow changed, cookies expiredSTOP — tell user to log in, do not modify code
TIMEOUTPage loads differently, spinner/lazy-loadAdd/update wait conditions
PAGE_CHANGEDMajor redesignMay need full adapter rewrite

Key questions to answer:

  1. What is the adapter trying to do? (Read the file at adapterSourcePath)
  2. What did the page look like when it failed? (Read summary.md, then state/ if needed)
  3. What network requests happened? (Read Failed Network in summary.md, then network.jsonl if needed)
  4. What's the gap between what the adapter expects and what the page provides?

Step 3: Explore the Current Website

Use opencli browser to inspect the live website. Never use the broken adapter — it will just fail again.

DOM changed (SELECTOR errors)

bash
# Open the page and inspect current DOM
opencli browser open https://example.com/target-page && opencli browser state

# Look for elements that match the adapter's intent
# Compare the snapshot with what the adapter expects

API changed (API_ERROR, EMPTY_RESULT)

bash
# Open page with network interceptor, then trigger the action manually
opencli browser open https://example.com/target-page && opencli browser state

# Interact to trigger API calls
opencli browser click <N> && opencli browser network

# Narrow to the request you care about by the fields its body should have
opencli browser network --filter author,text,likes

# Inspect specific API response (key is the `key` field from the default JSON output)
opencli browser network --detail <key>

Step 4: Patch the Adapter

Read the adapter source file at adapterSourcePath from the trace summary front matter and make targeted fixes. This path is authoritative — it may be in the repo (clis/) or user-local (~/.opencli/clis/).

Use the Read tool on the exact path from summary.md front matter.

Common Fixes

Selector update:

typescript
// Before: page.evaluate('document.querySelector(".old-class")...')
// After:  page.evaluate('document.querySelector(".new-class")...')

API endpoint change:

typescript
// Before: const resp = await page.evaluate(`fetch('/api/v1/old-endpoint')...`)
// After:  const resp = await page.evaluate(`fetch('/api/v2/new-endpoint')...`)

Response schema change:

typescript
// Before: const items = data.results
// After:  const items = data.data.items  // API now nests under "data"

Wait condition update:

typescript
// Before: await page.wait({ selector: '.loading-spinner', hidden: true })
// After:  await page.wait({ selector: '[data-loaded="true"]' })

Rules for Patching

  1. Make minimal changes — fix only what's broken, don't refactor
  2. Keep the same output structurecolumns and return format must stay compatible
  3. Prefer API over DOM scraping — if you discover a JSON API during exploration, switch to it
  4. Use @jackwener/opencli/* imports only — never add third-party package imports
  5. Test after patching — run the command again to verify
  6. Never relax verify/<cmd>.json fixtures to silence a failure. A failing patterns / notEmpty / mustNotContain / mustBeTruthy rule means the adapter's output is broken. Tighten the adapter so it produces correct values; do not loosen the fixture to accept the broken values. The one legitimate reason to edit a fixture during repair is when the site itself changed shape (e.g. URL format migration) — in that case update the fixture and note the change in ~/.opencli/sites/<site>/notes.md. Otherwise editing the fixture is covering up a silent correctness regression.

Step 5: Verify the Fix

bash
# Run the command normally
opencli <site> <command> [args...]

If it still fails, go back to Step 1 and collect a fresh trace. You have a budget of 3 repair rounds (trace → fix → retry). If the same error persists after a fix, try a different approach. After 3 rounds, stop and report what was tried.

Step 6: File an Upstream Issue

If the retry passes, the local adapter has drifted from upstream. File a GitHub issue so the fix flows back to jackwener/OpenCLI.

Do NOT file for:

  • AUTH_REQUIRED, BROWSER_CONNECT, ARGUMENT, CONFIG — environment/usage issues, not adapter bugs
  • CAPTCHA or rate limiting — not fixable upstream
  • Failures you couldn't actually fix (3 rounds exhausted)

Only file after a verified local fix — the retry must pass first.

Procedure:

  1. Prepare the issue content from the trace summary you already have:
    • Title: [autofix] <site>/<command>: <error_code> (e.g. [autofix] zhihu/hot: SELECTOR)
    • Body (use this template):
markdown
## Summary
OpenCLI autofix repaired this adapter locally, and the retry passed.

## Adapter
- Site: `<site>`
- Command: `<command>`
- OpenCLI version: `<version from opencli --version>`

## Original failure
- Error code: `<error_code>`

~~~
<error_message>
~~~

## Local fix summary

~~~
<1-2 sentence description of what you changed and why>
~~~

_Issue filed by OpenCLI autofix after a verified local repair._
  1. Ask the user before filing. Show them the draft title and body. Only proceed if they confirm.

  2. If the user approves and gh auth status succeeds:

bash
gh issue create --repo jackwener/OpenCLI \
  --title "[autofix] <site>/<command>: <error_code>" \
  --body "<the body above>"

If gh is not installed or not authenticated, tell the user and skip — do not error out.

When to Stop

Hard stops (do not modify code):

  • AUTH_REQUIRED / BROWSER_CONNECT — environment issue, not adapter bug
  • Site requires CAPTCHA — can't automate this
  • Rate limited / IP blocked — not an adapter issue

Soft stops (report after attempting):

  • 3 repair rounds exhausted — stop, report what was tried and what failed
  • Feature completely removed — the data no longer exists
  • Major redesign — needs full adapter rewrite via opencli-adapter-author skill

In all stop cases, clearly communicate the situation to the user rather than making futile patches.

Example Repair Session

1. User runs: opencli zhihu hot
   → Fails: SELECTOR "Could not find element: .HotList-item"

2. AI runs: opencli zhihu hot --trace retain-on-failure 2>trace-error.yaml
   → Gets trace summary with final state and failed action evidence

3. AI reads summary/state: page loaded but uses ".HotItem" instead of ".HotList-item"

4. AI explores: opencli browser open https://www.zhihu.com/hot && opencli browser state
   → Confirms new class name ".HotItem" with child ".HotItem-content"

5. AI patches: Edit adapter at `adapterSourcePath` — replace ".HotList-item" with ".HotItem"

6. AI verifies: opencli zhihu hot
   → Success: returns hot topics

7. AI prepares upstream issue draft, shows it to the user

8. User approves → AI runs: gh issue create --repo jackwener/OpenCLI --title "[autofix] zhihu/hot: SELECTOR" --body "..."

Frequently asked questions

What does the Opencli Autofix AI skill do?

Automatically fix broken OpenCLI adapters when commands fail. Load this skill when an opencli command fails — it guides you through collecting a trace artifact, patching the adapter, retrying, and filing an upstream GitHub issue after a verified fix. Works with any AI agent.

Why use Opencli Autofix on TypingMind?

Because you install it once and use it with any model. Opencli Autofix is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Opencli Autofix in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jackwener/OpenCLI/tree/main/skills/opencli-autofix. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Opencli Autofix?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Opencli Autofix?

As many as you like. As long as a model supports skills, you can use Opencli Autofix with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Opencli Autofix AI skill free?

Yes. It is published on GitHub by jackwener under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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