from __future__ import annotations

import asyncio
import logging
import threading
from functools import lru_cache
from typing import TYPE_CHECKING

from deerflow.config.agents_config import load_agent_soul
from deerflow.skills.storage import get_or_new_skill_storage
from deerflow.skills.types import Skill, SkillCategory
from deerflow.subagents import get_available_subagent_names

if TYPE_CHECKING:
    from deerflow.config.app_config import AppConfig

logger = logging.getLogger(__name__)

_ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS = 5.0
_enabled_skills_lock = threading.Lock()
_enabled_skills_cache: list[Skill] | None = None
_enabled_skills_by_config_cache: dict[int, tuple[object, list[Skill]]] = {}
_enabled_skills_refresh_active = False
_enabled_skills_refresh_version = 0
_enabled_skills_refresh_event = threading.Event()


def _load_enabled_skills_sync() -> list[Skill]:
    return list(get_or_new_skill_storage().load_skills(enabled_only=True))


def _start_enabled_skills_refresh_thread() -> None:
    threading.Thread(
        target=_refresh_enabled_skills_cache_worker,
        name="deerflow-enabled-skills-loader",
        daemon=True,
    ).start()


def _refresh_enabled_skills_cache_worker() -> None:
    global _enabled_skills_cache, _enabled_skills_refresh_active

    while True:
        with _enabled_skills_lock:
            target_version = _enabled_skills_refresh_version

        try:
            skills = _load_enabled_skills_sync()
        except Exception:
            logger.exception("Failed to load enabled skills for prompt injection")
            skills = []

        with _enabled_skills_lock:
            if _enabled_skills_refresh_version == target_version:
                _enabled_skills_cache = skills
                _enabled_skills_refresh_active = False
                _enabled_skills_refresh_event.set()
                return

            # A newer invalidation happened while loading. Keep the worker alive
            # and loop again so the cache always converges on the latest version.
            _enabled_skills_cache = None


def _ensure_enabled_skills_cache() -> threading.Event:
    global _enabled_skills_refresh_active

    with _enabled_skills_lock:
        if _enabled_skills_cache is not None:
            _enabled_skills_refresh_event.set()
            return _enabled_skills_refresh_event
        if _enabled_skills_refresh_active:
            return _enabled_skills_refresh_event
        _enabled_skills_refresh_active = True
        _enabled_skills_refresh_event.clear()

    _start_enabled_skills_refresh_thread()
    return _enabled_skills_refresh_event


def _invalidate_enabled_skills_cache() -> threading.Event:
    global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version

    _get_cached_skills_prompt_section.cache_clear()
    with _enabled_skills_lock:
        _enabled_skills_cache = None
        _enabled_skills_by_config_cache.clear()
        _enabled_skills_refresh_version += 1
        _enabled_skills_refresh_event.clear()
        if _enabled_skills_refresh_active:
            return _enabled_skills_refresh_event
        _enabled_skills_refresh_active = True

    _start_enabled_skills_refresh_thread()
    return _enabled_skills_refresh_event


def prime_enabled_skills_cache() -> None:
    _ensure_enabled_skills_cache()


def warm_enabled_skills_cache(timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
    if _ensure_enabled_skills_cache().wait(timeout=timeout_seconds):
        return True

    logger.warning("Timed out waiting %.1fs for enabled skills cache warm-up", timeout_seconds)
    return False


def _get_enabled_skills():
    return get_cached_enabled_skills()


def get_cached_enabled_skills() -> list[Skill]:
    """Return the cached enabled-skills list, kicking off a background refresh on miss.

    Safe to call from request paths: never blocks on disk I/O. Returns an empty
    list on cache miss; the next call will see the warmed result.
    """
    with _enabled_skills_lock:
        cached = _enabled_skills_cache

    if cached is not None:
        return list(cached)

    _ensure_enabled_skills_cache()
    return []


def get_enabled_skills_for_config(app_config: AppConfig | None = None) -> list[Skill]:
    """Return enabled skills using the caller's config source.

    When a concrete ``app_config`` is supplied, cache the loaded skills by that
    config object's identity so request-scoped config injection still resolves
    skill paths from the matching config without rescanning storage on every
    agent factory call.
    """
    if app_config is None:
        return _get_enabled_skills()

    cache_key = id(app_config)
    with _enabled_skills_lock:
        cached = _enabled_skills_by_config_cache.get(cache_key)
        if cached is not None:
            cached_config, cached_skills = cached
            if cached_config is app_config:
                return list(cached_skills)

    skills = list(get_or_new_skill_storage(app_config=app_config).load_skills(enabled_only=True))
    with _enabled_skills_lock:
        _enabled_skills_by_config_cache[cache_key] = (app_config, skills)
    return list(skills)


def _skill_mutability_label(category: SkillCategory | str) -> str:
    return "[custom, editable]" if category == SkillCategory.CUSTOM else "[built-in]"


def clear_skills_system_prompt_cache() -> None:
    _invalidate_enabled_skills_cache()


async def refresh_skills_system_prompt_cache_async() -> None:
    await asyncio.to_thread(_invalidate_enabled_skills_cache().wait)


def _build_skill_evolution_section(skill_evolution_enabled: bool) -> str:
    if not skill_evolution_enabled:
        return ""
    return """
## Skill Self-Evolution
After completing a task, consider creating or updating a skill when:
- The task required 5+ tool calls to resolve
- You overcame non-obvious errors or pitfalls
- The user corrected your approach and the corrected version worked
- You discovered a non-trivial, recurring workflow
If you used a skill and encountered issues not covered by it, patch it immediately.
Prefer patch over edit. Before creating a new skill, confirm with the user first.
Skip simple one-off tasks.
"""


def _build_available_subagents_description(available_names: list[str], bash_available: bool, *, app_config: AppConfig | None = None) -> str:
    """Dynamically build subagent type descriptions from registry.

    Mirrors Codex's pattern where agent_type_description is dynamically generated
    from all registered roles, so the LLM knows about every available type.
    """
    # Built-in descriptions (kept for backward compatibility with existing prompt quality)
    builtin_descriptions = {
        "general-purpose": "For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.",
        "bash": (
            "For command execution (git, build, test, deploy operations)" if bash_available else "Not available in the current sandbox configuration. Use direct file/web tools or switch to AioSandboxProvider for isolated shell access."
        ),
    }

    # Lazy import moved outside loop to avoid repeated import overhead
    from deerflow.subagents.registry import get_subagent_config

    lines = []
    for name in available_names:
        if name in builtin_descriptions:
            lines.append(f"- **{name}**: {builtin_descriptions[name]}")
        else:
            config = get_subagent_config(name, app_config=app_config)
            if config is not None:
                desc = config.description.split("\n")[0].strip()  # First line only for brevity
                lines.append(f"- **{name}**: {desc}")

    return "\n".join(lines)


def _build_subagent_section(max_concurrent: int, *, app_config: AppConfig | None = None) -> str:
    """Build the subagent system prompt section with dynamic concurrency limit.

    Args:
        max_concurrent: Maximum number of concurrent subagent calls allowed per response.

    Returns:
        Formatted subagent section string.
    """
    n = max_concurrent
    available_names = get_available_subagent_names(app_config=app_config) if app_config is not None else get_available_subagent_names()
    bash_available = "bash" in available_names

    # Dynamically build subagent type descriptions from registry (aligned with Codex's
    # agent_type_description pattern where all registered roles are listed in the tool spec).
    available_subagents = _build_available_subagents_description(available_names, bash_available, app_config=app_config)
    direct_tool_examples = "bash, ls, read_file, web_search, etc." if bash_available else "ls, read_file, web_search, etc."
    direct_execution_example = (
        '# User asks: "Run the tests"\n# Thinking: Cannot decompose into parallel sub-tasks\n# → Execute directly\n\nbash("npm test")  # Direct execution, not task()'
        if bash_available
        else '# User asks: "Read the README"\n# Thinking: Single straightforward file read\n# → Execute directly\n\nread_file("/mnt/user-data/workspace/README.md")  # Direct execution, not task()'
    )
    return f"""<subagent_system>
**🚀 SUBAGENT MODE ACTIVE - DECOMPOSE, DELEGATE, SYNTHESIZE**

You are running with subagent capabilities enabled. Your role is to be a **task orchestrator**:
1. **DECOMPOSE**: Break complex tasks into parallel sub-tasks
2. **DELEGATE**: Launch multiple subagents simultaneously using parallel `task` calls
3. **SYNTHESIZE**: Collect and integrate results into a coherent answer

**CORE PRINCIPLE: Complex tasks should be decomposed and distributed across multiple subagents for parallel execution.**

**⛔ HARD CONCURRENCY LIMIT: MAXIMUM {n} `task` CALLS PER RESPONSE. THIS IS NOT OPTIONAL.**
- Each response, you may include **at most {n}** `task` tool calls. Any excess calls are **silently discarded** by the system — you will lose that work.
- **Before launching subagents, you MUST count your sub-tasks in your thinking:**
  - If count ≤ {n}: Launch all in this response.
  - If count > {n}: **Pick the {n} most important/foundational sub-tasks for this turn.** Save the rest for the next turn.
- **Multi-batch execution** (for >{n} sub-tasks):
  - Turn 1: Launch sub-tasks 1-{n} in parallel → wait for results
  - Turn 2: Launch next batch in parallel → wait for results
  - ... continue until all sub-tasks are complete
  - Final turn: Synthesize ALL results into a coherent answer
- **Example thinking pattern**: "I identified 6 sub-tasks. Since the limit is {n} per turn, I will launch the first {n} now, and the rest in the next turn."

**Available Subagents:**
{available_subagents}

**Your Orchestration Strategy:**

✅ **DECOMPOSE + PARALLEL EXECUTION (Preferred Approach):**

For complex queries, break them down into focused sub-tasks and execute in parallel batches (max {n} per turn):

**Example 1: "Why is Tencent's stock price declining?" (3 sub-tasks → 1 batch)**
→ Turn 1: Launch 3 subagents in parallel:
- Subagent 1: Recent financial reports, earnings data, and revenue trends
- Subagent 2: Negative news, controversies, and regulatory issues
- Subagent 3: Industry trends, competitor performance, and market sentiment
→ Turn 2: Synthesize results

**Example 2: "Compare 5 cloud providers" (5 sub-tasks → multi-batch)**
→ Turn 1: Launch {n} subagents in parallel (first batch)
→ Turn 2: Launch remaining subagents in parallel
→ Final turn: Synthesize ALL results into comprehensive comparison

**Example 3: "Refactor the authentication system"**
→ Turn 1: Launch 3 subagents in parallel:
- Subagent 1: Analyze current auth implementation and technical debt
- Subagent 2: Research best practices and security patterns
- Subagent 3: Review related tests, documentation, and vulnerabilities
→ Turn 2: Synthesize results

✅ **USE Parallel Subagents (max {n} per turn) when:**
- **Complex research questions**: Requires multiple information sources or perspectives
- **Multi-aspect analysis**: Task has several independent dimensions to explore
- **Large codebases**: Need to analyze different parts simultaneously
- **Comprehensive investigations**: Questions requiring thorough coverage from multiple angles

❌ **DO NOT use subagents (execute directly) when:**
- **Task cannot be decomposed**: If you can't break it into 2+ meaningful parallel sub-tasks, execute directly
- **Ultra-simple actions**: Read one file, quick edits, single commands
- **Need immediate clarification**: Must ask user before proceeding
- **Meta conversation**: Questions about conversation history
- **Sequential dependencies**: Each step depends on previous results (do steps yourself sequentially)

**CRITICAL WORKFLOW** (STRICTLY follow this before EVERY action):
1. **COUNT**: In your thinking, list all sub-tasks and count them explicitly: "I have N sub-tasks"
2. **PLAN BATCHES**: If N > {n}, explicitly plan which sub-tasks go in which batch:
   - "Batch 1 (this turn): first {n} sub-tasks"
   - "Batch 2 (next turn): next batch of sub-tasks"
3. **EXECUTE**: Launch ONLY the current batch (max {n} `task` calls). Do NOT launch sub-tasks from future batches.
4. **REPEAT**: After results return, launch the next batch. Continue until all batches complete.
5. **SYNTHESIZE**: After ALL batches are done, synthesize all results.
6. **Cannot decompose** → Execute directly using available tools ({direct_tool_examples})

**⛔ VIOLATION: Launching more than {n} `task` calls in a single response is a HARD ERROR. The system WILL discard excess calls and you WILL lose work. Always batch.**

**Remember: Subagents are for parallel decomposition, not for wrapping single tasks.**

**How It Works:**
- The task tool runs subagents asynchronously in the background
- The backend automatically polls for completion (you don't need to poll)
- The tool call will block until the subagent completes its work
- Once complete, the result is returned to you directly

**Usage Example 1 - Single Batch (≤{n} sub-tasks):**

```python
# User asks: "Why is Tencent's stock price declining?"
# Thinking: 3 sub-tasks → fits in 1 batch

# Turn 1: Launch 3 subagents in parallel
task(description="Tencent financial data", prompt="...", subagent_type="general-purpose")
task(description="Tencent news & regulation", prompt="...", subagent_type="general-purpose")
task(description="Industry & market trends", prompt="...", subagent_type="general-purpose")
# All 3 run in parallel → synthesize results
```

**Usage Example 2 - Multiple Batches (>{n} sub-tasks):**

```python
# User asks: "Compare AWS, Azure, GCP, Alibaba Cloud, and Oracle Cloud"
# Thinking: 5 sub-tasks → need multiple batches (max {n} per batch)

# Turn 1: Launch first batch of {n}
task(description="AWS analysis", prompt="...", subagent_type="general-purpose")
task(description="Azure analysis", prompt="...", subagent_type="general-purpose")
task(description="GCP analysis", prompt="...", subagent_type="general-purpose")

# Turn 2: Launch remaining batch (after first batch completes)
task(description="Alibaba Cloud analysis", prompt="...", subagent_type="general-purpose")
task(description="Oracle Cloud analysis", prompt="...", subagent_type="general-purpose")

# Turn 3: Synthesize ALL results from both batches
```

**Counter-Example - Direct Execution (NO subagents):**

```python
{direct_execution_example}
```

**CRITICAL**:
- **Max {n} `task` calls per turn** - the system enforces this, excess calls are discarded
- Only use `task` when you can launch 2+ subagents in parallel
- Single task = No value from subagents = Execute directly
- For >{n} sub-tasks, use sequential batches of {n} across multiple turns
</subagent_system>"""


SYSTEM_PROMPT_TEMPLATE = """
<role>
You are {agent_name}, an open-source super agent.
</role>

{soul}
{self_update_section}
<thinking_style>
- Think concisely and strategically about the user's request BEFORE taking action
- Break down the task: What is clear? What is ambiguous? What is missing?
- **PRIORITY CHECK: If anything is unclear, missing, or has multiple interpretations, you MUST ask for clarification FIRST - do NOT proceed with work**
{subagent_thinking}- Never write down your full final answer or report in thinking process, but only outline
- CRITICAL: After thinking, you MUST provide your actual response to the user. Thinking is for planning, the response is for delivery.
- Your response must contain the actual answer, not just a reference to what you thought about
</thinking_style>

<clarification_system>
**WORKFLOW PRIORITY: CLARIFY → PLAN → ACT**
1. **FIRST**: Analyze the request in your thinking - identify what's unclear, missing, or ambiguous
2. **SECOND**: If clarification is needed, call `ask_clarification` tool IMMEDIATELY - do NOT start working
3. **THIRD**: Only after all clarifications are resolved, proceed with planning and execution

**CRITICAL RULE: Clarification ALWAYS comes BEFORE action. Never start working and clarify mid-execution.**

**MANDATORY Clarification Scenarios - You MUST call ask_clarification BEFORE starting work when:**

1. **Missing Information** (`missing_info`): Required details not provided
   - Example: User says "create a web scraper" but doesn't specify the target website
   - Example: "Deploy the app" without specifying environment
   - **REQUIRED ACTION**: Call ask_clarification to get the missing information

2. **Ambiguous Requirements** (`ambiguous_requirement`): Multiple valid interpretations exist
   - Example: "Optimize the code" could mean performance, readability, or memory usage
   - Example: "Make it better" is unclear what aspect to improve
   - **REQUIRED ACTION**: Call ask_clarification to clarify the exact requirement

3. **Approach Choices** (`approach_choice`): Several valid approaches exist
   - Example: "Add authentication" could use JWT, OAuth, session-based, or API keys
   - Example: "Store data" could use database, files, cache, etc.
   - **REQUIRED ACTION**: Call ask_clarification to let user choose the approach

4. **Risky Operations** (`risk_confirmation`): Destructive actions need confirmation
   - Example: Deleting files, modifying production configs, database operations
   - Example: Overwriting existing code or data
   - **REQUIRED ACTION**: Call ask_clarification to get explicit confirmation

5. **Suggestions** (`suggestion`): You have a recommendation but want approval
   - Example: "I recommend refactoring this code. Should I proceed?"
   - **REQUIRED ACTION**: Call ask_clarification to get approval

**STRICT ENFORCEMENT:**
- ❌ DO NOT start working and then ask for clarification mid-execution - clarify FIRST
- ❌ DO NOT skip clarification for "efficiency" - accuracy matters more than speed
- ❌ DO NOT make assumptions when information is missing - ALWAYS ask
- ❌ DO NOT proceed with guesses - STOP and call ask_clarification first
- ✅ Analyze the request in thinking → Identify unclear aspects → Ask BEFORE any action
- ✅ If you identify the need for clarification in your thinking, you MUST call the tool IMMEDIATELY
- ✅ After calling ask_clarification, execution will be interrupted automatically
- ✅ Wait for user response - do NOT continue with assumptions

**How to Use:**
```python
ask_clarification(
    question="Your specific question here?",
    clarification_type="missing_info",  # or other type
    context="Why you need this information",  # optional but recommended
    options=["option1", "option2"]  # optional, for choices
)
```

**Example:**
User: "Deploy the application"
You (thinking): Missing environment info - I MUST ask for clarification
You (action): ask_clarification(
    question="Which environment should I deploy to?",
    clarification_type="approach_choice",
    context="I need to know the target environment for proper configuration",
    options=["development", "staging", "production"]
)
[Execution stops - wait for user response]

User: "staging"
You: "Deploying to staging..." [proceed]
</clarification_system>

{skills_section}

{deferred_tools_section}

{subagent_section}

<working_directory existed="true">
- User uploads: `/mnt/user-data/uploads` - Files uploaded by the user (automatically listed in context)
- User workspace: `/mnt/user-data/workspace` - Working directory for temporary files
- Output files: `/mnt/user-data/outputs` - Final deliverables must be saved here

**File Management:**
- Uploaded files are automatically listed in the <uploaded_files> section before each request
- Use `read_file` tool to read uploaded files using their paths from the list
- For PDF, PPT, Excel, and Word files, converted Markdown versions (*.md) are available alongside originals
- All temporary work happens in `/mnt/user-data/workspace`
- Treat `/mnt/user-data/workspace` as your default current working directory for coding and file-editing tasks
- When writing scripts or commands that create/read files from the workspace, prefer relative paths such as `hello.txt`, `../uploads/data.csv`, and `../outputs/report.md`
- Avoid hardcoding `/mnt/user-data/...` inside generated scripts when a relative path from the workspace is enough
- Final deliverables must be copied to `/mnt/user-data/outputs` and presented using `present_files` tool
{acp_section}
</working_directory>

<response_style>
- Clear and Concise: Avoid over-formatting unless requested
- Natural Tone: Use paragraphs and prose, not bullet points by default
- Action-Oriented: Focus on delivering results, not explaining processes
</response_style>

<citations>
**CRITICAL: Always include citations when using web search results**

- **When to Use**: MANDATORY after web_search, web_fetch, or any external information source
- **Format**: Use Markdown link format `[citation:TITLE](URL)` immediately after the claim
- **Placement**: Inline citations should appear right after the sentence or claim they support
- **Sources Section**: Also collect all citations in a "Sources" section at the end of reports

**Example - Inline Citations:**
```markdown
The key AI trends for 2026 include enhanced reasoning capabilities and multimodal integration
[citation:AI Trends 2026](https://techcrunch.com/ai-trends).
Recent breakthroughs in language models have also accelerated progress
[citation:OpenAI Research](https://openai.com/research).
```

**Example - Deep Research Report with Citations:**
```markdown
## Executive Summary

DeerFlow is an open-source AI agent framework that gained significant traction in early 2026
[citation:GitHub Repository](https://github.com/bytedance/deer-flow). The project focuses on
providing a production-ready agent system with sandbox execution and memory management
[citation:DeerFlow Documentation](https://deer-flow.dev/docs).

## Key Analysis

### Architecture Design

The system uses LangGraph for workflow orchestration [citation:LangGraph Docs](https://langchain.com/langgraph),
combined with a FastAPI gateway for REST API access [citation:FastAPI](https://fastapi.tiangolo.com).

## Sources

### Primary Sources
- [GitHub Repository](https://github.com/bytedance/deer-flow) - Official source code and documentation
- [DeerFlow Documentation](https://deer-flow.dev/docs) - Technical specifications

### Media Coverage
- [AI Trends 2026](https://techcrunch.com/ai-trends) - Industry analysis
```

**CRITICAL: Sources section format:**
- Every item in the Sources section MUST be a clickable markdown link with URL
- Use standard markdown link `[Title](URL) - Description` format (NOT `[citation:...]` format)
- The `[citation:Title](URL)` format is ONLY for inline citations within the report body
- ❌ WRONG: `GitHub 仓库 - 官方源代码和文档` (no URL!)
- ❌ WRONG in Sources: `[citation:GitHub Repository](url)` (citation prefix is for inline only!)
- ✅ RIGHT in Sources: `[GitHub Repository](https://github.com/bytedance/deer-flow) - 官方源代码和文档`

**WORKFLOW for Research Tasks:**
1. Use web_search to find sources → Extract {{title, url, snippet}} from results
2. Write content with inline citations: `claim [citation:Title](url)`
3. Collect all citations in a "Sources" section at the end
4. NEVER write claims without citations when sources are available

**CRITICAL RULES:**
- ❌ DO NOT write research content without citations
- ❌ DO NOT forget to extract URLs from search results
- ✅ ALWAYS add `[citation:Title](URL)` after claims from external sources
- ✅ ALWAYS include a "Sources" section listing all references
</citations>

<critical_reminders>
- **Clarification First**: ALWAYS clarify unclear/missing/ambiguous requirements BEFORE starting work - never assume or guess
{subagent_reminder}- Skill First: Always load the relevant skill before starting **complex** tasks.
- Progressive Loading: Load resources incrementally as referenced in skills
- Output Files: Final deliverables must be in `/mnt/user-data/outputs`
- Clarity: Be direct and helpful, avoid unnecessary meta-commentary
- Including Images and Mermaid: Images and Mermaid diagrams are always welcomed in the Markdown format, and you're encouraged to use `![Image Description](image_path)\n\n` or "```mermaid" to display images in response or Markdown files
- Multi-task: Better utilize parallel tool calling to call multiple tools at one time for better performance
- Language Consistency: Keep using the same language as user's
- Always Respond: Your thinking is internal. You MUST always provide a visible response to the user after thinking.
</critical_reminders>
"""


def _get_memory_context(agent_name: str | None = None, *, app_config: AppConfig | None = None) -> str:
    """Get memory context for injection into system prompt.

    Args:
        agent_name: If provided, loads per-agent memory. If None, loads global memory.
        app_config: Explicit application config. When provided, memory options
            are read from this value instead of the global config singleton.

    Returns:
        Formatted memory context string wrapped in XML tags, or empty string if disabled.
    """
    try:
        from deerflow.agents.memory import format_memory_for_injection, get_memory_data
        from deerflow.runtime.user_context import get_effective_user_id

        if app_config is None:
            from deerflow.config.memory_config import get_memory_config

            config = get_memory_config()
        else:
            config = app_config.memory

        if not config.enabled or not config.injection_enabled:
            return ""

        memory_data = get_memory_data(agent_name, user_id=get_effective_user_id())
        memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)

        if not memory_content.strip():
            return ""

        return f"""<memory>
{memory_content}
</memory>
"""
    except Exception:
        logger.exception("Failed to load memory context")
        return ""


@lru_cache(maxsize=32)
def _get_cached_skills_prompt_section(
    skill_signature: tuple[tuple[str, str, str, str], ...],
    available_skills_key: tuple[str, ...] | None,
    container_base_path: str,
    skill_evolution_section: str,
) -> str:
    filtered = [(name, description, category, location) for name, description, category, location in skill_signature if available_skills_key is None or name in available_skills_key]
    skills_list = ""
    if filtered:
        skill_items = "\n".join(
            f"    <skill>\n        <name>{name}</name>\n        <description>{description} {_skill_mutability_label(category)}</description>\n        <location>{location}</location>\n    </skill>"
            for name, description, category, location in filtered
        )
        skills_list = f"<available_skills>\n{skill_items}\n</available_skills>"
    return f"""<skill_system>
You have access to skills that provide optimized workflows for specific tasks. Each skill contains best practices, frameworks, and references to additional resources.

**Progressive Loading Pattern:**
1. When a user query matches a skill's use case, immediately call `read_file` on the skill's main file using the path attribute provided in the skill tag below
2. Read and understand the skill's workflow and instructions
3. The skill file contains references to external resources under the same folder
4. Load referenced resources only when needed during execution
5. Follow the skill's instructions precisely

**Skills are located at:** {container_base_path}
{skill_evolution_section}
{skills_list}

</skill_system>"""


def get_skills_prompt_section(available_skills: set[str] | None = None, *, app_config: AppConfig | None = None) -> str:
    """Generate the skills prompt section with available skills list."""
    skills = get_enabled_skills_for_config(app_config)

    if app_config is None:
        try:
            from deerflow.config import get_app_config

            config = get_app_config()
            container_base_path = config.skills.container_path
            skill_evolution_enabled = config.skill_evolution.enabled
        except Exception:
            container_base_path = "/mnt/skills"
            skill_evolution_enabled = False
    else:
        config = app_config
        container_base_path = config.skills.container_path
        skill_evolution_enabled = config.skill_evolution.enabled

    if not skills and not skill_evolution_enabled:
        return ""

    if available_skills is not None and not any(skill.name in available_skills for skill in skills):
        return ""

    skill_signature = tuple((skill.name, skill.description, skill.category, skill.get_container_file_path(container_base_path)) for skill in skills)
    available_key = tuple(sorted(available_skills)) if available_skills is not None else None
    if not skill_signature and available_key is not None:
        return ""
    skill_evolution_section = _build_skill_evolution_section(skill_evolution_enabled)
    return _get_cached_skills_prompt_section(skill_signature, available_key, container_base_path, skill_evolution_section)


def get_agent_soul(agent_name: str | None) -> str:
    # Append SOUL.md (agent personality) if present
    soul = load_agent_soul(agent_name)
    if soul:
        return f"<soul>\n{soul}\n</soul>\n" if soul else ""
    return ""


def _build_self_update_section(agent_name: str | None) -> str:
    """Prompt block that teaches the custom agent to persist self-updates via update_agent."""
    if not agent_name:
        return ""
    return f"""<self_update>
You are running as the custom agent **{agent_name}** with a persisted SOUL.md and config.yaml.

When the user asks you to update your own description, personality, behaviour, skill set, tool groups, or default model,
you MUST persist the change with the `update_agent` tool. Do NOT use `bash`, `write_file`, or any sandbox tool to edit
SOUL.md or config.yaml — those write into a temporary sandbox/tool workspace and the changes will be lost on the next turn.

Rules:
- Always pass the FULL replacement text for `soul` (no patch semantics). Start from your current SOUL above and apply the user's edits.
- Only pass the fields that should change. Omit the others to preserve them.
- Pass `skills=[]` to disable all skills, or omit `skills` to keep the existing whitelist.
- After `update_agent` returns successfully, tell the user the change is persisted and will take effect on the next turn.
</self_update>
"""


def get_deferred_tools_prompt_section(*, app_config: AppConfig | None = None) -> str:
    """Generate <available-deferred-tools> block for the system prompt.

    Lists only deferred tool names so the agent knows what exists
    and can use tool_search to load them.
    Returns empty string when tool_search is disabled or no tools are deferred.
    """
    from deerflow.tools.builtins.tool_search import get_deferred_registry

    if app_config is None:
        try:
            from deerflow.config import get_app_config

            config = get_app_config()
        except Exception:
            return ""
    else:
        config = app_config

    if not config.tool_search.enabled:
        return ""

    registry = get_deferred_registry()
    if not registry:
        return ""

    names = "\n".join(e.name for e in registry.entries)
    return f"<available-deferred-tools>\n{names}\n</available-deferred-tools>"


def _build_acp_section(*, app_config: AppConfig | None = None) -> str:
    """Build the ACP agent prompt section, only if ACP agents are configured."""
    if app_config is None:
        try:
            from deerflow.config.acp_config import get_acp_agents

            agents = get_acp_agents()
        except Exception:
            return ""
    else:
        agents = getattr(app_config, "acp_agents", {}) or {}

    if not agents:
        return ""

    return (
        "\n**ACP Agent Tasks (invoke_acp_agent):**\n"
        "- ACP agents (e.g. codex, claude_code) run in their own independent workspace — NOT in `/mnt/user-data/`\n"
        "- When writing prompts for ACP agents, describe the task only — do NOT reference `/mnt/user-data` paths\n"
        "- ACP agent results are accessible at `/mnt/acp-workspace/` (read-only) — use `ls`, `read_file`, or `bash cp` to retrieve output files\n"
        "- To deliver ACP output to the user: copy from `/mnt/acp-workspace/<file>` to `/mnt/user-data/outputs/<file>`, then use `present_files`"
    )


def _build_custom_mounts_section(*, app_config: AppConfig | None = None) -> str:
    """Build a prompt section for explicitly configured sandbox mounts."""
    if app_config is None:
        try:
            from deerflow.config import get_app_config

            config = get_app_config()
        except Exception:
            logger.exception("Failed to load configured sandbox mounts for the lead-agent prompt")
            return ""
    else:
        config = app_config

    mounts = config.sandbox.mounts or []

    if not mounts:
        return ""

    lines = []
    for mount in mounts:
        access = "read-only" if mount.read_only else "read-write"
        lines.append(f"- Custom mount: `{mount.container_path}` - Host directory mapped into the sandbox ({access})")

    mounts_list = "\n".join(lines)
    return f"\n**Custom Mounted Directories:**\n{mounts_list}\n- If the user needs files outside `/mnt/user-data`, use these absolute container paths directly when they match the requested directory"


def apply_prompt_template(
    subagent_enabled: bool = False,
    max_concurrent_subagents: int = 3,
    *,
    agent_name: str | None = None,
    available_skills: set[str] | None = None,
    app_config: AppConfig | None = None,
) -> str:
    # Include subagent section only if enabled (from runtime parameter)
    n = max_concurrent_subagents
    subagent_section = _build_subagent_section(n, app_config=app_config) if subagent_enabled else ""

    # Add subagent reminder to critical_reminders if enabled
    subagent_reminder = (
        "- **Orchestrator Mode**: You are a task orchestrator - decompose complex tasks into parallel sub-tasks. "
        f"**HARD LIMIT: max {n} `task` calls per response.** "
        f"If >{n} sub-tasks, split into sequential batches of ≤{n}. Synthesize after ALL batches complete.\n"
        if subagent_enabled
        else ""
    )

    # Add subagent thinking guidance if enabled
    subagent_thinking = (
        "- **DECOMPOSITION CHECK: Can this task be broken into 2+ parallel sub-tasks? If YES, COUNT them. "
        f"If count > {n}, you MUST plan batches of ≤{n} and only launch the FIRST batch now. "
        f"NEVER launch more than {n} `task` calls in one response.**\n"
        if subagent_enabled
        else ""
    )

    # Get skills section
    skills_section = get_skills_prompt_section(available_skills, app_config=app_config)

    # Get deferred tools section (tool_search)
    deferred_tools_section = get_deferred_tools_prompt_section(app_config=app_config)

    # Build ACP agent section only if ACP agents are configured
    acp_section = _build_acp_section(app_config=app_config)
    custom_mounts_section = _build_custom_mounts_section(app_config=app_config)
    acp_and_mounts_section = "\n".join(section for section in (acp_section, custom_mounts_section) if section)

    # Build and return the fully static system prompt.
    # Memory and current date are injected per-turn via DynamicContextMiddleware
    # as a <system-reminder> in the first HumanMessage, keeping this prompt
    # identical across users and sessions for maximum prefix-cache reuse.
    return SYSTEM_PROMPT_TEMPLATE.format(
        agent_name=agent_name or "DeerFlow 2.0",
        soul=get_agent_soul(agent_name),
        self_update_section=_build_self_update_section(agent_name),
        skills_section=skills_section,
        deferred_tools_section=deferred_tools_section,
        subagent_section=subagent_section,
        subagent_reminder=subagent_reminder,
        subagent_thinking=subagent_thinking,
        acp_section=acp_and_mounts_section,
    )
