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https://github.com/bytedance/deer-flow.git
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442248dd06
* feat: preserve durable context across summarization * fix: harden durable context review gaps * style: format delegation ledger live test * chore: remove stale delegation ledger prefix * fix: address durable context review feedback
181 lines
7.3 KiB
Python
181 lines
7.3 KiB
Python
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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from deerflow.agents.middlewares.skill_context import extract_skills, render_skill_context
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_ROOT = "/mnt/skills"
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_READ = frozenset({"read_file", "read", "view", "cat"})
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_SKILL_BODY = """---
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name: data-analysis
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description: Analyze data with pandas and charts.
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---
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# Data Analysis
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Use pandas. ALWAYS_USE_PANDAS_SENTINEL
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"""
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def _ai_read(tool_call_id: str, path: str, name: str = "read_file") -> AIMessage:
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return AIMessage(
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content="",
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tool_calls=[{"name": name, "args": {"path": path}, "id": tool_call_id, "type": "tool_call"}],
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)
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class TestExtractSkills:
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def test_captures_skill_reference_with_description(self):
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msgs = [
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HumanMessage(content="use the analysis skill"),
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_ai_read("r1", "/mnt/skills/public/data-analysis/SKILL.md"),
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ToolMessage(content=_SKILL_BODY, tool_call_id="r1", id="tm1"),
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]
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out = extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ)
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assert len(out) == 1
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assert out[0]["name"] == "data-analysis"
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assert out[0]["path"] == "/mnt/skills/public/data-analysis/SKILL.md"
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assert out[0]["description"] == "Analyze data with pandas and charts."
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assert "content" not in out[0]
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assert "ALWAYS_USE_PANDAS_SENTINEL" not in repr(out[0])
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assert isinstance(out[0]["loaded_at"], int)
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def test_description_is_capped_at_capture_time(self):
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body = "---\nname: huge\ndescription: " + ("x" * 2000) + "\n---\nBODY_SENTINEL"
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msgs = [
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_ai_read("r1", "/mnt/skills/public/huge/SKILL.md"),
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ToolMessage(content=body, tool_call_id="r1", id="tm1"),
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]
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out = extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ)
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assert out
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assert len(out[0]["description"]) <= 500
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assert "BODY_SENTINEL" not in repr(out[0])
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def test_missing_frontmatter_yields_empty_description(self):
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msgs = [
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_ai_read("r1", "/mnt/skills/public/x/SKILL.md"),
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ToolMessage(content="# X\nno frontmatter here", tool_call_id="r1", id="tm1"),
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]
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out = extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ)
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assert out and out[0]["description"] == ""
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def test_malformed_frontmatter_yields_empty_description(self):
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msgs = [
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_ai_read("r1", "/mnt/skills/public/x/SKILL.md"),
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ToolMessage(content="---\n: : not valid yaml\n---\nbody", tool_call_id="r1", id="tm1"),
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]
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out = extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ)
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assert out and out[0]["description"] == ""
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def test_normalizes_dot_segments_under_skills_root(self):
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msgs = [
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_ai_read("r1", "/mnt/skills/public/./data-analysis/SKILL.md"),
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ToolMessage(content="body", tool_call_id="r1", id="tm1"),
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]
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out = extract_skills(msgs, skills_root="/mnt/skills/", read_tool_names=_READ)
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assert out and out[0]["path"] == "/mnt/skills/public/data-analysis/SKILL.md"
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assert out[0]["name"] == "data-analysis"
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def test_rejects_traversal_that_escapes_skills_root(self):
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msgs = [
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_ai_read("r1", "/mnt/skills/../workspace/secrets.txt"),
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ToolMessage(content="secret", tool_call_id="r1", id="tm1"),
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]
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assert extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ) == []
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def test_ignores_supporting_resources_under_skill_directory(self):
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msgs = [
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_ai_read("r1", "/mnt/skills/public/data-analysis/scripts/analyze.py"),
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ToolMessage(content="large script body", tool_call_id="r1", id="tm1"),
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]
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assert extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ) == []
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def test_ignores_error_tool_messages(self):
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msgs = [
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_ai_read("r1", "/mnt/skills/public/data-analysis/SKILL.md"),
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ToolMessage(content="Error: File not found", tool_call_id="r1", id="tm1", status="error"),
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]
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assert extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ) == []
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def test_ignores_read_file_error_text_even_when_tool_status_is_success(self):
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msgs = [
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_ai_read("r1", "/mnt/skills/public/missing/SKILL.md"),
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ToolMessage(content="Error: File not found: /mnt/skills/public/missing/SKILL.md", tool_call_id="r1", id="tm1"),
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]
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assert extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ) == []
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def test_ignores_reads_outside_skills_root(self):
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msgs = [
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_ai_read("r1", "/workspace/notes.md"),
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ToolMessage(content="notes", tool_call_id="r1", id="tm1"),
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]
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assert extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ) == []
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def test_ignores_non_read_tool_names(self):
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msgs = [
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_ai_read("r1", "/mnt/skills/a/SKILL.md", name="write_file"),
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ToolMessage(content="x", tool_call_id="r1", id="tm1"),
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]
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assert extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ) == []
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def test_read_without_result_is_skipped(self):
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msgs = [_ai_read("r1", "/mnt/skills/a/SKILL.md")]
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assert extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ) == []
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def test_trailing_slash_root_normalized(self):
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msgs = [
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_ai_read("r1", "/mnt/skills/public/a/SKILL.md"),
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ToolMessage(content="body", tool_call_id="r1", id="tm1"),
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]
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out = extract_skills(msgs, skills_root="/mnt/skills/", read_tool_names=_READ)
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assert out and out[0]["name"] == "a"
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def test_multiple_skills_each_captured(self):
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msgs = [
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_ai_read("r1", "/mnt/skills/public/a/SKILL.md"),
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ToolMessage(content="A", tool_call_id="r1", id="tm1"),
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_ai_read("r2", "/mnt/skills/custom/b/SKILL.md"),
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ToolMessage(content="B", tool_call_id="r2", id="tm2"),
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]
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out = extract_skills(msgs, skills_root=_ROOT, read_tool_names=_READ)
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assert [e["name"] for e in out] == ["a", "b"]
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class TestRenderSkillContext:
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def test_empty_returns_empty_string(self):
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assert render_skill_context([]) == ""
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def test_renders_reference_reminder_not_body(self):
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entries = [
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{
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"name": "data-analysis",
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"path": "/mnt/skills/public/data-analysis/SKILL.md",
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"description": "Analyze data with pandas.",
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"loaded_at": 2,
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}
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]
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out = render_skill_context(entries)
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assert "Active skills" in out
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assert "re-read" in out.lower()
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assert "data-analysis" in out
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assert "Analyze data with pandas." in out
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assert "/mnt/skills/public/data-analysis/SKILL.md" in out
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assert "###" not in out
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def test_entry_without_description_still_renders_name_and_path(self):
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entries = [{"name": "x", "path": "/mnt/skills/public/x/SKILL.md", "description": "", "loaded_at": 0}]
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out = render_skill_context(entries)
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assert "- x" in out
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assert "/mnt/skills/public/x/SKILL.md" in out
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def test_render_caps_legacy_large_description(self):
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entries = [{"name": "x", "path": "/mnt/skills/public/x/SKILL.md", "description": "x" * 2000, "loaded_at": 0}]
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out = render_skill_context(entries)
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assert len(out) < 800
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