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* feat(gateway): skill-aware slash commands, paginated /commands, Telegram 100-cap Map active skills to Telegram's slash command menu so users can discover and invoke skills directly. Three changes: 1. Telegram menu now includes active skill commands alongside built-in commands, capped at 100 entries (Telegram Bot API limit). Overflow commands remain callable but hidden from the picker. Logged at startup when cap is hit. 2. New /commands [page] gateway command for paginated browsing of all commands + skills. /help now shows first 10 skill commands and points to /commands for the full list. 3. When a user types a slash command that matches a disabled or uninstalled skill, they get actionable guidance: - Disabled: 'Enable it with: hermes skills config' - Optional (not installed): 'Install with: hermes skills install official/<path>' Built on ideas from PR #3921 by @kshitijk4poor. * chore: move 21 niche skills to optional-skills Move specialized/niche skills from built-in (skills/) to optional (optional-skills/) to reduce the default skill count. Users can install them with: hermes skills install official/<category>/<name> Moved skills (21): - mlops: accelerate, chroma, faiss, flash-attention, hermes-atropos-environments, huggingface-tokenizers, instructor, lambda-labs, llava, nemo-curator, pinecone, pytorch-lightning, qdrant, saelens, simpo, slime, tensorrt-llm, torchtitan - research: domain-intel, duckduckgo-search - devops: inference-sh cli Built-in skills: 96 → 75 Optional skills: 22 → 43 * fix: only include repo built-in skills in Telegram menu, not user-installed User-installed skills (from hub or manually added) stay accessible via /skills and by typing the command directly, but don't get registered in the Telegram slash command picker. Only skills whose SKILL.md is under the repo's skills/ directory are included in the menu. This keeps the Telegram menu focused on the curated built-in set while user-installed skills remain discoverable through /skills and /commands.
59 lines
2 KiB
Markdown
59 lines
2 KiB
Markdown
# AgentResult Fields Reference
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`AgentResult` is defined in `environments/agent_loop.py` as a dataclass.
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## Fields
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| Field | Type | Description |
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|-------|------|-------------|
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| `messages` | `List[Dict[str, Any]]` | Full conversation history in OpenAI message format |
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| `managed_state` | `Optional[Dict]` | ManagedServer.get_state() if Phase 2, else None |
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| `turns_used` | `int` | Number of LLM calls made during the loop |
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| `finished_naturally` | `bool` | True if model stopped calling tools on its own |
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| `reasoning_per_turn` | `List[Optional[str]]` | Extracted reasoning content per turn |
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| `tool_errors` | `List[ToolError]` | Tool errors encountered during the loop |
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## ToolError Fields
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| Field | Type | Description |
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|-------|------|-------------|
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| `turn` | `int` | Which turn the error occurred |
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| `tool_name` | `str` | Name of the tool that failed |
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| `arguments` | `str` | Arguments passed to the tool |
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| `error` | `str` | Error message |
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| `tool_result` | `str` | The result returned to the model |
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## Extracting Data from Messages
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Messages follow OpenAI format. Common patterns:
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```python
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# Get final assistant response
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for msg in reversed(result.messages):
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if msg.get("role") == "assistant" and msg.get("content"):
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final_response = msg["content"]
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break
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# Get all tool names used
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tools = []
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for msg in result.messages:
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if msg.get("role") == "assistant" and msg.get("tool_calls"):
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for tc in msg["tool_calls"]:
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fn = tc.get("function", {}) if isinstance(tc, dict) else {}
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tools.append(fn.get("name", ""))
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# Get tool results
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for msg in result.messages:
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if msg.get("role") == "tool":
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tool_output = msg.get("content", "")
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call_id = msg.get("tool_call_id", "")
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```
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## Fields that DO NOT EXIST
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These are common mistakes — AgentResult does NOT have:
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- `final_response` — extract from messages
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- `tool_calls` — extract from messages
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- `tools_used` — extract from messages
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- `output` — extract from messages
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- `response` — extract from messages
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