القائمة
Full-stack research and code generation pipeline - research, code, and create
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/jeremylongshore/oss-agent-lab --skill deer-flowيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Self-improving research loops with hypothesis generation, experiment design, and result analysis
Natural language web UI control — element detection, targeted interaction, and automated form filling
Public opinion analysis and sentiment at scale — sentiment scoring, stance detection, and multi-dimensional bias measurement.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | deer_flow |
| display_name | Deer Flow Specialist |
| description | Full-stack research and code generation pipeline - research, code, and create |
| version | 0.1.0 |
| source_repo | bytedance/deer-flow |
| license | MIT |
| tier | core |
| capabilities | ["research","code_generation","creation","summarize"] |
| allowed_tools | ["research_topic","generate_code","create_artifact"] |
| output_formats | ["python_api","cli","mcp_server","agent_skill","rest_api"] |
The Deer Flow specialist wraps the bytedance/deer-flow SuperAgent pattern into a composable OSS Agent Lab specialist. It chains three stages into a single pipeline:
Each stage is also independently callable as a tool, making the specialist useful for partial workflows (research-only, code-only, wrap-existing-content-in-artifact).
shallow, standard, and deep depth settings.| Tool | Description | Parameters | Side Effects |
|---|---|---|---|
research_topic | Research a topic and return findings, sources, summary, confidence | topic, depth, sources | None |
generate_code | Generate code, tests, and explanation from a specification | specification, language, style | None |
create_artifact | Package content into a versioned artifact with metadata | content, artifact_type, format | None |
research_topic
topic: str — subject to research (required)depth: str — "shallow" | "standard" | "deep" (default: "standard")sources: list[str] | None — explicit source list; auto-selected when Nonegenerate_code
specification: str — natural-language description of the code to produce (required)language: str — target language, e.g. "python", "typescript" (default: "python")style: str — "clean" | "verbose" | "minimal" (default: "clean")create_artifact
content: dict[str, Any] — pipeline outputs to embed (required)artifact_type: str — "report" | "notebook" | "package" | "summary" (default: "report")format: str — "markdown" | "json" | "html" (default: "markdown")SpecialistRequest
│
▼
research_topic(topic, depth, sources)
│
▼ (if code generation needed)
generate_code(specification, language, style)
│
▼
create_artifact(content, artifact_type, format)
│
▼
SpecialistResponse(result={research, code?, artifact})
Code generation is triggered when:
"code""code_generation"generate_code is truthy (default: True)from agents.specialists.deer_flow.agent import DeerFlowSpecialist
from oss_agent_lab.contracts import Intent, Query, SpecialistRequest
specialist = DeerFlowSpecialist()
request = SpecialistRequest(
intent=Intent(
action="research_and_build",
domain="code_generation",
confidence=0.95,
parameters={"depth": "deep", "language": "python"},
),
query=Query(user_input="async rate limiter with token bucket algorithm"),
specialist_name="deer_flow",
)
response = await specialist.execute(request)
print(response.result["artifact"]["artifact_id"])
print(response.result["code"]["code"])
oss-lab run deer_flow "async rate limiter with token bucket algorithm"
from agents.specialists.deer_flow.tools import research_topic
findings = research_topic(
topic="transformer attention mechanisms",
depth="deep",
sources=["arxiv", "github"],
)
print(findings["summary"])
print(f"Confidence: {findings['confidence']}")
from agents.specialists.deer_flow.tools import generate_code
result = generate_code(
specification="LRU cache with O(1) get and put operations",
language="python",
style="clean",
)
print(result["code"])
print(result["tests"])
Wraps bytedance/deer-flow — a full-stack multi-agent research framework featuring deep research, report generation, and podcast/presentation creation pipelines built on top of LangGraph.