qianwen-text — Skillopedia

Qwen Text Chat (OpenAI-Compatible) Generate text, conduct conversations, write code, and invoke tools using Qwen models through the OpenAI-compatible API. This skill is part of QianWen-AI/qianwen-ai . Skill directory Use this skill's internal files to execute and learn. Load reference files on demand when the default path fails or you need details. | Location | Purpose | |-------------------------------------|-------------------------------------------------------------------------------------| | | Default execution — chat/completions request, streaming, output save | | | Fallback: curl, Pyth…

\n for candidate in candidates:\n m = re.search(pattern, candidate)\n if m:\n agent, skill = m.group(1) or \"default\", m.group(2)\n if len(agent) \u003c= 32 and len(skill) \u003c= 32:\n return json.dumps({\"channel\": \"qianwenai-skill\", \"tags\": {\"t1\": skill, \"t2\": agent}}, separators=(',', ':'))\n return None\n\n\n# ---------------------------------------------------------------------------\n# Section 6: HTTP infrastructure\n# ---------------------------------------------------------------------------\n\n_RETRYABLE_CODES = frozenset({429, 500, 502, 503, 504})\n\n\ndef http_request(\n method: str,\n url: str,\n api_key: str,\n payload: dict[str, Any] | None = None,\n *,\n extra_headers: dict[str, str] | None = None,\n timeout: int = 120,\n retries: int = 2,\n backoff: float = 1.5,\n) -> dict[str, Any]:\n \"\"\"Generic HTTP request with retry and exponential backoff.\n\n Handles JSON serialisation, provider header injection, and retryable\n HTTP codes.\n \"\"\"\n hdrs = get_provider().make_headers(api_key, payload)\n if extra_headers:\n hdrs.update(extra_headers)\n data = json.dumps(payload).encode(\"utf-8\") if payload else None\n last_err = \"\"\n for attempt in range(retries + 1):\n req = urllib.request.Request(url, data=data, headers=hdrs, method=method)\n try:\n with urllib.request.urlopen(req, timeout=timeout) as resp:\n return json.loads(resp.read().decode(\"utf-8\"))\n except urllib.error.HTTPError as exc:\n body = \"\"\n try:\n body = exc.read().decode(\"utf-8\", errors=\"replace\")\n except Exception:\n pass\n last_err = f\"HTTP {exc.code}: {body[:500]}\"\n if api_key and len(api_key) > 8 and api_key in body:\n last_err = last_err.replace(api_key, mask_key(api_key))\n if exc.code not in _RETRYABLE_CODES or attempt >= retries:\n raise RuntimeError(last_err) from exc\n time.sleep(backoff * (2 ** attempt))\n except urllib.error.URLError as exc:\n last_err = str(exc.reason)\n if attempt >= retries:\n raise RuntimeError(f\"Network error: {last_err}\") from exc\n time.sleep(backoff * (2 ** attempt))\n raise RuntimeError(last_err)\n\n\ndef http_post(\n url: str,\n api_key: str,\n payload: dict[str, Any],\n *,\n timeout: int = 120,\n retries: int = 2,\n backoff: float = 1.5,\n) -> dict[str, Any]:\n \"\"\"Convenience: non-streaming POST, returns parsed JSON.\"\"\"\n return http_request(\n \"POST\", url, api_key, payload,\n timeout=timeout, retries=retries, backoff=backoff,\n )\n\n\ndef _sse_error_message(\n chunk: Any,\n event_type: str,\n api_key: str,\n) -> Optional[str]:\n \"\"\"Return a safe error message for an SSE error payload, if present.\"\"\"\n error_payload = chunk.get(\"error\") if isinstance(chunk, dict) else None\n has_top_level_error = isinstance(chunk, dict) and (\n not chunk.get(\"choices\")\n and chunk.get(\"code\") is not None\n and chunk.get(\"message\") is not None\n )\n if event_type.lower() != \"error\" and error_payload is None and not has_top_level_error:\n return None\n\n payload = error_payload if error_payload is not None else chunk\n if isinstance(payload, dict):\n code = payload.get(\"code\") or payload.get(\"type\")\n message = payload.get(\"message\")\n if code and message:\n detail = f\"{code}: {message}\"\n elif code or message:\n detail = str(code or message)\n else:\n detail = json.dumps(payload, ensure_ascii=False)\n else:\n detail = str(payload)\n\n if api_key and len(api_key) > 8 and api_key in detail:\n detail = detail.replace(api_key, mask_key(api_key))\n return f\"SSE error: {sanitize_diagnostic(detail)[:500]}\"\n\n\ndef stream_sse(\n url: str,\n api_key: str,\n payload: dict[str, Any],\n *,\n timeout: int = 180,\n) -> Iterator[dict[str, Any]]:\n \"\"\"Streaming POST. Yields parsed SSE ``data:`` chunks.\n\n Automatically sets ``stream: true`` in the payload and sends the\n appropriate ``Accept`` header.\n \"\"\"\n payload[\"stream\"] = True\n hdrs = get_provider().make_headers(api_key, payload)\n hdrs[\"Accept\"] = \"text/event-stream\"\n data = json.dumps(payload).encode(\"utf-8\")\n req = urllib.request.Request(url, data=data, headers=hdrs, method=\"POST\")\n try:\n resp = urllib.request.urlopen(req, timeout=timeout)\n except urllib.error.HTTPError as exc:\n body = \"\"\n try:\n body = exc.read().decode(\"utf-8\", errors=\"replace\")\n except Exception:\n pass\n if api_key and len(api_key) > 8 and api_key in body:\n body = body.replace(api_key, mask_key(api_key))\n raise RuntimeError(f\"HTTP {exc.code}: {sanitize_diagnostic(body)[:500]}\") from None\n except urllib.error.URLError as exc:\n raise RuntimeError(f\"Network error: {sanitize_diagnostic(str(exc.reason))}\") from None\n\n buf = b\"\"\n event_type = \"\"\n try:\n while True:\n raw = resp.read(4096)\n if not raw:\n break\n buf += raw\n while b\"\\n\" in buf:\n line_bytes, buf = buf.split(b\"\\n\", 1)\n line = line_bytes.decode(\"utf-8\", errors=\"replace\").strip()\n if not line:\n event_type = \"\"\n continue\n if line.startswith(\"event:\"):\n event_type = line[6:].strip()\n continue\n if not line.startswith(\"data:\"):\n continue\n json_str = line[5:].strip()\n if json_str == \"[DONE]\":\n return\n try:\n chunk = json.loads(json_str)\n except json.JSONDecodeError:\n if event_type.lower() == \"error\":\n detail = json_str\n if api_key and len(api_key) > 8 and api_key in detail:\n detail = detail.replace(api_key, mask_key(api_key))\n detail = sanitize_diagnostic(detail)[:500]\n raise RuntimeError(f\"SSE error: {detail}\") from None\n pass\n else:\n error_message = _sse_error_message(chunk, event_type, api_key)\n if error_message:\n raise RuntimeError(error_message)\n yield chunk\n event_type = \"\"\n finally:\n resp.close()\n\n\n# ---------------------------------------------------------------------------\n# Section 7: File upload / download\n# ---------------------------------------------------------------------------\n\n_BASE64_FILE_LIMIT = 7 * 1024 * 1024 # 7 MB (base64 adds ~33%; API limit is 10 MB)\n\n\ndef upload_local_file(api_key: str, model: str, fp: Path) -> str:\n \"\"\"Upload a local file to the active provider's temp storage.\n\n Returns a provider-managed URL (e.g. ``oss://`` for DashScope).\n \"\"\"\n return get_provider().upload_file(api_key, model, fp)\n\n\ndef resolve_file(\n value: str,\n *,\n api_key: str | None = None,\n model: str | None = None,\n) -> str:\n \"\"\"Resolve a file reference for API consumption.\n\n URLs (``http``, ``https``, ``data``, and provider-managed schemes)\n pass through unchanged. Local files are handled based on context:\n\n - *api_key* + *model* provided: upload to temp storage.\n - Otherwise: convert to ``data:`` base64 URI (must be \u003c 7 MB).\n \"\"\"\n provider = get_provider()\n pass_through = (\"http://\", \"https://\", \"data:\") + provider.managed_url_schemes()\n if value.startswith(pass_through):\n return value\n if value.startswith(\"file://\"):\n parsed = urllib.parse.urlparse(value)\n if parsed.netloc and parsed.netloc.lower() != \"localhost\":\n raise ValueError(\n f\"Cannot resolve non-local file URI authority {parsed.netloc!r}; \"\n \"only an empty authority or 'localhost' is supported.\"\n )\n value = urllib.request.url2pathname(parsed.path)\n p = Path(value)\n if not (p.exists() and p.is_file()):\n return value\n\n file_size = p.stat().st_size\n\n if api_key and model:\n managed_url = provider.upload_file(api_key, model, p)\n tag = \"48 h TTL\" if managed_url.startswith(\"oss://\") else \"custom OSS\"\n print(sanitize_diagnostic(f\"Uploaded {p.name} -> {managed_url} ({tag})\"), file=sys.stderr)\n return managed_url\n\n if file_size > _BASE64_FILE_LIMIT:\n print(\n f\"Warning: {p.name} is {file_size / 1024 / 1024:.1f} MB -- \"\n \"base64 may exceed the 10 MB API limit. \"\n \"Use --upload-files to auto-upload, or provide an online URL.\",\n file=sys.stderr,\n )\n\n mime = mimetypes.guess_type(p.name)[0] or \"application/octet-stream\"\n b64 = base64.b64encode(p.read_bytes()).decode(\"ascii\")\n return f\"data:{mime};base64,{b64}\"\n\n\ndef download_file(url: str, dest: Path, *, timeout: int = 120) -> Path:\n \"\"\"Download a file from *url* to *dest*, creating parent dirs as needed.\"\"\"\n dest.parent.mkdir(parents=True, exist_ok=True)\n req = urllib.request.Request(url, headers={\"User-Agent\": \"qianwenai-skill\"})\n try:\n with urllib.request.urlopen(req, timeout=timeout) as resp:\n dest.write_bytes(resp.read())\n except urllib.error.URLError as exc:\n raise RuntimeError(f\"Download failed: {sanitize_diagnostic(str(exc))}\") from None\n return dest\n\n\n# ---------------------------------------------------------------------------\n# Section 8: Request / response utilities\n# ---------------------------------------------------------------------------\n\ndef load_request(args: Any) -> dict[str, Any]:\n \"\"\"Load request dict from ``--request`` or ``--file`` CLI argument.\"\"\"\n if getattr(args, \"request\", None):\n return json.loads(args.request)\n if getattr(args, \"file\", None):\n return json.loads(Path(args.file).read_text(encoding=\"utf-8\"))\n raise ValueError(\"Provide --request '{...}' or --file path/to/request.json\")\n\n\ndef save_result(result: dict[str, Any], output_path: str | Path) -> None:\n \"\"\"Write *result* as JSON to *output_path*, creating parent dirs.\"\"\"\n p = Path(output_path)\n p.parent.mkdir(parents=True, exist_ok=True)\n p.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n\n\ndef extract_text(content: Any) -> str:\n \"\"\"Extract plain text from a message ``content`` field.\n\n Handles ``str``, ``list[{type, text}]``, and ``None``.\n \"\"\"\n if isinstance(content, str):\n return content\n if isinstance(content, list):\n for item in content:\n if isinstance(item, dict) and item.get(\"type\") == \"text\":\n text = item.get(\"text\")\n if isinstance(text, str):\n return text\n return json.dumps(content, ensure_ascii=False)\n if content is None:\n return \"\"\n return json.dumps(content, ensure_ascii=False)\n\n\ndef try_parse_json(text: str) -> Any | None:\n \"\"\"Attempt to parse *text* as JSON; return ``None`` on failure.\"\"\"\n text = text.strip()\n if not text:\n return None\n try:\n return json.loads(text)\n except json.JSONDecodeError:\n return None\n\n\n# ---------------------------------------------------------------------------\n# Section 9: Async task polling\n# ---------------------------------------------------------------------------\n\ndef poll_task(\n task_id: str,\n api_key: str,\n *,\n timeout_s: int = 600,\n interval: int = 10,\n verbose: bool = True,\n) -> dict[str, Any]:\n \"\"\"Poll an async task until it reaches a terminal state.\n\n Returns the final result dict. Raises ``TimeoutError`` if\n *timeout_s* is exceeded.\n \"\"\"\n provider = get_provider()\n url = provider.task_poll_url(task_id)\n terminals = provider.terminal_statuses()\n start = time.time()\n poll_count = 0\n while True:\n result = http_request(\"GET\", url, api_key)\n elapsed = int(time.time() - start)\n poll_count += 1\n status = provider.extract_task_status(result)\n if verbose:\n print(f\" [{elapsed}s] task={task_id} status={status}\", file=sys.stderr)\n if status in terminals:\n return result\n if time.time() - start > timeout_s:\n raise TimeoutError(\n f\"Task {task_id} timed out after {timeout_s}s \"\n f\"({poll_count} polls, last status: {status})\"\n )\n time.sleep(interval)\n\n\n# ---------------------------------------------------------------------------\n# Section 10: Update-check signal\n# ---------------------------------------------------------------------------\n\ntry:\n from gossamer import run as _run_update_signal_impl\nexcept ImportError:\n _run_update_signal_impl = None # type: ignore[assignment]\n\n\ndef run_update_signal(caller: str | Path | None = None) -> None:\n \"\"\"Emit update-check signals to stderr (non-blocking, failure-safe).\n\n Parameters\n ----------\n caller : str or Path, optional\n ``__file__`` of the calling script.\n \"\"\"\n if _run_update_signal_impl:\n try:\n _run_update_signal_impl(caller=caller or __file__)\n except Exception:\n pass\n","content_type":"text/x-python; charset=utf-8","language":"python","size":43758,"content_sha256":"9343069363f8e31b7178d2462c79e2e7d56cb7a30665544d9072b1d39589d8b0"},{"filename":"scripts/text.py","content":"#!/usr/bin/env python3\n\"\"\"Chat with Qwen models via OpenAI-compatible API. Self-contained, stdlib only.\"\"\"\nfrom __future__ import annotations\n\nimport sys\n\nif sys.version_info \u003c (3, 9):\n print(f\"Error: Python 3.9+ required (found {sys.version}). \"\n \"Install: https://www.python.org/downloads/\", file=sys.stderr)\n sys.exit(1)\n\nimport argparse\nimport json\nfrom pathlib import Path\nfrom typing import Any\n\nsys.path.insert(0, str(Path(__file__).resolve().parent))\n\nfrom qianwen_lib import ( # noqa: E402\n chat_url,\n http_post,\n load_cdn_model_config,\n load_request,\n require_api_key,\n run_update_signal,\n stream_sse,\n validate_token_plan_model,\n)\n\n\ndef _default_model() -> str:\n config = load_cdn_model_config(\n \"qianwen-text-config.json\",\n required_keys=(\"default_model\",),\n )\n model = config[\"default_model\"]\n if not isinstance(model, str) or not model:\n raise RuntimeError(\"Invalid text model configuration: default_model must be a string\")\n return model\n\n\n# ---------------------------------------------------------------------------\n# Response helpers\n# ---------------------------------------------------------------------------\n\ndef _extract_content(resp: dict[str, Any]) -> str:\n choices = resp.get(\"choices\", [])\n if choices:\n msg = choices[0].get(\"message\", {})\n return msg.get(\"content\", \"\") or \"\"\n return \"\"\n\n\n# ---------------------------------------------------------------------------\n# Streaming\n# ---------------------------------------------------------------------------\n\ndef _run_stream(url: str, api_key: str, request: dict[str, Any],\n print_response: bool, print_reasoning: bool = True) -> dict[str, Any]:\n full_content = \"\"\n reasoning_content = \"\"\n is_answering = False\n model = request.get(\"model\", \"\")\n tool_calls: dict[int, dict[str, Any]] = {} # keyed by index per official docs\n finish_reason = \"stop\"\n\n for chunk in stream_sse(url, api_key, request):\n choices = chunk.get(\"choices\", [])\n if not choices:\n continue\n choice = choices[0]\n delta = choice.get(\"delta\", {})\n\n # Handle reasoning_content (thinking mode)\n rc = delta.get(\"reasoning_content\") or \"\"\n if rc:\n reasoning_content += rc\n if print_response and print_reasoning and not is_answering:\n print(rc, end=\"\", flush=True, file=sys.stderr)\n\n # Handle regular content\n text = delta.get(\"content\", \"\") or \"\"\n if text:\n if not is_answering:\n is_answering = True\n if print_response and reasoning_content:\n print(\"\\n\", file=sys.stderr) # newline after reasoning\n full_content += text\n if print_response:\n print(text, end=\"\", flush=True)\n\n # Handle function calling (delta.tool_calls) - per official docs pattern\n if \"tool_calls\" in delta and delta[\"tool_calls\"]:\n for tc in delta[\"tool_calls\"]:\n idx = tc.get(\"index\", 0)\n args = (tc.get(\"function\") or {}).get(\"arguments\") or \"\"\n if idx not in tool_calls:\n tool_calls[idx] = tc\n # Ensure arguments is string, not None\n if \"function\" in tool_calls[idx]:\n tool_calls[idx][\"function\"][\"arguments\"] = args\n else:\n # Accumulate arguments across chunks\n tool_calls[idx][\"function\"][\"arguments\"] += args\n\n # Track finish_reason\n if choice.get(\"finish_reason\"):\n finish_reason = choice[\"finish_reason\"]\n\n if not model and chunk.get(\"model\"):\n model = chunk[\"model\"]\n\n if print_response:\n print()\n\n message: dict[str, Any] = {\"role\": \"assistant\", \"content\": full_content}\n if tool_calls:\n message[\"tool_calls\"] = list(tool_calls.values())\n\n result: dict[str, Any] = {\n \"model\": model,\n \"choices\": [{\"message\": message, \"finish_reason\": finish_reason}],\n }\n if reasoning_content:\n result[\"reasoning\"] = reasoning_content\n return result\n\n\n# ---------------------------------------------------------------------------\n# Main\n# ---------------------------------------------------------------------------\n\ndef main() -> None:\n run_update_signal(caller=__file__)\n parser = argparse.ArgumentParser(\n description=\"Chat with Qwen models via OpenAI-compatible API\",\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=\"\"\"\\\nrequest JSON fields (--request / --file):\n messages (required) Array of {role, content} message objects\n model Model ID — overridden by --model flag\n enable_thinking true/false — enable chain-of-thought (default: let model decide)\n tools Array of tool/function definitions for function calling\n response_format {\"type\":\"json_object\"} or {\"type\":\"json_schema\",\"json_schema\":{...}}\n temperature Sampling temperature (0-2)\n max_tokens Maximum output tokens\n stream true/false — overridden by --stream flag\n\nenvironment variables:\n DASHSCOPE_API_KEY (required) API key — also loaded from .env file\n QIANWEN_API_KEY (alternative) Alias for DASHSCOPE_API_KEY\n QWEN_REGION cn-beijing (default)\n QWEN_BASE_URL Override the API base URL entirely\n\nexamples:\n # Simple chat\n python scripts/text.py --request '{\"messages\":[{\"role\":\"user\",\"content\":\"Hello\"}]}'\n\n # With system prompt and streaming\n python scripts/text.py --request '{\"messages\":[\n {\"role\":\"system\",\"content\":\"You are a helpful assistant\"},\n {\"role\":\"user\",\"content\":\"Explain quantum computing\"}\n ]}' --stream --print-response\n\n # From file, save output\n python scripts/text.py --file request.json --output results/ --print-response\n\n # Structured JSON output\n python scripts/text.py --request '{\"messages\":[{\"role\":\"user\",\"content\":\"List 3 colors\"}],\n \"response_format\":{\"type\":\"json_object\"}}' --print-response\n\"\"\",\n )\n parser.add_argument(\"--request\", help=\"Inline JSON: must contain 'messages' array\")\n parser.add_argument(\"--file\", help=\"Path to JSON file containing request body\")\n parser.add_argument(\"--output\", default=\"output/qianwen-text\",\n help=\"Directory to save response JSON (default: output/qianwen-text)\")\n parser.add_argument(\"--print-response\", action=\"store_true\", help=\"Print generated text to stdout\")\n parser.add_argument(\"--model\", default=None,\n help=\"Model ID (default loaded from CDN model configuration)\")\n parser.add_argument(\"--stream\", action=\"store_true\", help=\"Enable streaming response (SSE)\")\n parser.add_argument(\"--enable-thinking\", action=\"store_true\", dest=\"enable_thinking_flag\",\n help=\"Enable chain-of-thought thinking mode (overrides model defaults). \"\n \"Recommended for complex reasoning/math tasks. Adds latency for qwen3.6-plus/qwen3.5-plus/flash.\")\n parser.add_argument(\"--hide-reasoning\", action=\"store_true\",\n help=\"Suppress reasoning process output to stderr (reasoning still saved to JSON)\")\n args = parser.parse_args()\n\n try:\n request = load_request(args)\n except ValueError as e:\n print(f\"Error: {e}\", file=sys.stderr)\n sys.exit(1)\n\n if args.model:\n request[\"model\"] = args.model\n elif \"model\" not in request:\n request[\"model\"] = _default_model()\n \n # Thinking mode handling:\n # - User explicitly enabled via flag → set true\n # - User specified in request JSON → respect their choice \n # - Otherwise → let API apply model defaults (qwen3.6-plus/qwen3.5-plus/flash have thinking ON by default)\n if args.enable_thinking_flag:\n request[\"enable_thinking\"] = True\n print(\"Note: Thinking mode enabled explicitly. May add latency for qwen3.6-plus/qwen3.5-plus/flash.\", file=sys.stderr)\n elif \"enable_thinking\" not in request:\n # Do nothing - preserve API defaults per model\n pass\n\n api_key = require_api_key(script_file=__file__)\n validate_token_plan_model(api_key, request[\"model\"])\n url = chat_url()\n\n try:\n if args.stream or request.get(\"stream\"):\n print(\"Connecting to model (streaming)...\", file=sys.stderr)\n response_data = _run_stream(url, api_key, request, args.print_response,\n print_reasoning=not args.hide_reasoning)\n else:\n request[\"stream\"] = False\n print(\"Sending request to model, please wait...\", file=sys.stderr)\n response_data = http_post(url, api_key, request)\n # Extract reasoning_content from non-streaming response (normalize to top-level)\n msg = (response_data.get(\"choices\") or [{}])[0].get(\"message\", {})\n if msg.get(\"reasoning_content\"):\n response_data[\"reasoning\"] = msg[\"reasoning_content\"]\n if args.print_response:\n print(_extract_content(response_data))\n except RuntimeError as e:\n print(f\"API error: {e}\", file=sys.stderr)\n sys.exit(1)\n\n out = Path(args.output)\n if out.suffix.lower() == \".json\":\n out_file = out\n out_dir = out.parent\n else:\n out_dir = out\n out_file = out_dir / \"response.json\"\n out_dir.mkdir(parents=True, exist_ok=True)\n out_file.write_text(json.dumps(response_data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n print(f\"Response saved to {out_file}\", file=sys.stderr)\n\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":9713,"content_sha256":"4d25433aa6ed7373ccbcf911a11dd19f1377ac07568de7a443579bb9e64382c6"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"Qwen Text Chat (OpenAI-Compatible)","type":"text"}]},{"type":"paragraph","content":[{"text":"Generate text, conduct conversations, write code, and invoke tools using Qwen models through the OpenAI-compatible API. This skill is part of ","type":"text"},{"text":"QianWen-AI/qianwen-ai","type":"text","marks":[{"type":"strong"}]},{"text":".","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Skill directory","type":"text"}]},{"type":"paragraph","content":[{"text":"Use this skill's internal files to execute and learn. Load reference files on demand when the default path fails or you need details.","type":"text"}]},{"type":"table","attrs":{"layout":null},"content":[{"type":"tr","content":[{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Location","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Purpose","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"scripts/text.py","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Default execution — chat/completions request, streaming, output save","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"references/execution-guide.md","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Fallback: curl, Python SDK, function calling, thinking mode","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"references/api-guide.md","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"API supplement and full code examples","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"references/prompt-guide.md","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Prompt engineering: CO-STAR framework, CoT, few-shot, task steps","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"references/sources.md","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Official documentation URLs (manual lookup only)","type":"text"}]}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Security","type":"text"}]},{"type":"paragraph","content":[{"text":"NEVER output any API key or credential in plaintext.","type":"text","marks":[{"type":"strong"}]},{"text":" Always use variable references (","type":"text"},{"text":"$DASHSCOPE_API_KEY","type":"text","marks":[{"type":"code_inline"}]},{"text":" in shell, ","type":"text"},{"text":"os.environ[\"DASHSCOPE_API_KEY\"]","type":"text","marks":[{"type":"code_inline"}]},{"text":" in Python). Any check or detection of credentials must be ","type":"text"},{"text":"non-plaintext","type":"text","marks":[{"type":"strong"}]},{"text":": report only status (e.g. \"set\" / \"not set\", \"valid\" / \"invalid\"), never the value. 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It keeps you informed about new QianWen-AI/qianwen-ai features and bug fixes.","type":"text"}]},{"type":"ordered_list","attrs":{"order":1,"listStyle":"number"},"content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Install now","type":"text","marks":[{"type":"strong"}]},{"text":" — I'll run the install command for you","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Skip for now","type":"text","marks":[{"type":"strong"}]},{"text":" — do nothing this time","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Never remind me","type":"text","marks":[{"type":"strong"}]},{"text":" — permanently suppress this prompt","type":"text"}]}]}]}]},{"type":"paragraph","content":[{"text":"After the user responds:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"User chooses 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\u003cthis-skill-scripts-dir>/gossamer.py --never-install","type":"text","marks":[{"type":"code_inline"}]},{"text":", then continue normally","type":"text"}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Signal: ","type":"text"},{"text":"[UPDATE_AVAILABLE]","type":"text","marks":[{"type":"code_inline"}]}]},{"type":"paragraph","content":[{"text":"When stderr contains this signal, you MUST append a brief update notice to your response, including the version info and the update command shown in the stderr output.","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"No signal in stderr","type":"text"}]},{"type":"paragraph","content":[{"text":"If stderr contains neither ","type":"text"},{"text":"[ACTION_REQUIRED]","type":"text","marks":[{"type":"code_inline"}]},{"text":" nor ","type":"text"},{"text":"[UPDATE_AVAILABLE]","type":"text","marks":[{"type":"code_inline"}]},{"text":", no action is needed — the skill is installed and up to date (or cached within 24h).","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Explicit user request","type":"text"}]},{"type":"paragraph","content":[{"text":"When the user explicitly asks to check for updates (e.g. \"check for updates\", \"check version\"):","type":"text"}]},{"type":"ordered_list","attrs":{"order":1,"listStyle":"number"},"content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Look for ","type":"text"},{"text":"qianwen-update-check/SKILL.md","type":"text","marks":[{"type":"code_inline"}]},{"text":" in sibling skill directories.","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"If found — run: ","type":"text"},{"text":"python3 \u003cqianwen-update-check-dir>/scripts/check_update.py --print-response","type":"text","marks":[{"type":"code_inline"}]},{"text":" and report the result.","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"If not found — present the install options above.","type":"text"}]}]}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"References","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"execution-guide.md","type":"text","marks":[{"type":"link","attrs":{"href":"references/execution-guide.md","title":null}}]},{"text":" — Fallback paths (curl, SDK, autonomous), function calling, thinking mode","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"api-guide.md","type":"text","marks":[{"type":"link","attrs":{"href":"references/api-guide.md","title":null}}]},{"text":" — API supplementary guide with full code examples","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"sources.md","type":"text","marks":[{"type":"link","attrs":{"href":"references/sources.md","title":null}}]},{"text":" — Official documentation URLs","type":"text"}]}]}]}]},"metadata":{"date":"2026-11-02","name":"qianwen-text","author":"@skillopedia","source":{"stars":120,"repo_name":"qianwen-ai","origin_url":"https://github.com/QianWen-AI/qianwen-ai/blob/HEAD/skills/text/qianwen-text/SKILL.md","repo_owner":"QianWen-AI","body_sha256":"327546c71739afae57c6091daf403f5fc6f84db2c54c55c87d8f5d6f2dd065c2","cluster_key":"2204a99299cef46146762190617b925bb79aae18a43d4df4fd6298a06589534d","clean_bundle":{"format":"clean-skill-bundle-v1","source":"QianWen-AI/qianwen-ai/skills/text/qianwen-text/SKILL.md","text_bytes":87315,"source_commit":"11b1805167d91bc931dbaad85e17036a72e98ae0","skills_sh_rank":3959,"attachment_count":11,"text_attachments":11,"attachment_storage":"skill_attachments","binary_attachments":0},"cluster_size":1,"skill_md_path":"skills/text/qianwen-text/SKILL.md","import_metadata":{"date":"2026-11-02","author":"@skillopedia","version":"v1","category":"ai-agent-development","category_label":"AI"},"exact_dupes_collapsed_into_this":0},"version":"v1","category":"ai-agent-development","import_tag":"skills-sh-top20k-2026-11","description":"Generate text, have conversations, write code, reason, and call functions with Qwen models. TRIGGER when: user asks to chat with Qwen, generate text, write code with Qwen, use Qwen function calling, or explicitly invokes this skill by name (e.g. use qianwen-text). DO NOT TRIGGER when: general coding questions without Qwen, non-Qwen AI model usage (OpenAI, Gemini, etc.), image/video understanding (use qianwen-vision), image/video/audio generation.","compatibility":"Requires Python 3.9+ and curl. Cursor: auto-loaded. Claude Code: read this skill's SKILL.md before first use."}},"renderedAt":1793671786688}

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.