Yige
14b6a02c48
## 摘要 / Summary ### 中文 去除技能里束缚执行 Agent 自主判断的**机械数字策略**：create 需求收集的「每轮最多问 2 个」、discover 排序的「最多展示 5 个候选」。改为交由 Agent 按情况自主把握追问节奏与展示数量，保留「别让用户负担 / 别一次刷屏」的原则意图——把控制权交还给判断力更强的执行者，而非用固定上限约束。create 2.5.3 / discover 2.6.3，manifest 1.2.14，中英双份同步、重算 source_hash（validate-i18n 通过）。 ### English Remove mechanical numeric limits that constrain the executing Agent's judgment: create's "at most 2 questions per turn" and discover's "show at most 5 candidates". Both become "the Agent decides the pacing/count by situation", keeping the principle intent (don't burden the user / don't flood the screen) while handing control back to the more capable executor instead of a fixed cap. create 2.5.3 / discover 2.6.3, manifest 1.2.14, both locales synced, hashes recomputed (validate-i18n passes).
6.4 KiB
name, description, version, type, risk_level, status, disable-model-invocation, tags, metadata, market
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| discover-agent | 根据用户需求推荐最匹配的智能体，展示候选列表并引导选择。Use when 用户描述需求但不确定该找哪个智能体帮忙，或想浏览可用的智能体。 | 2.6.3 | procedural | low | enabled | true |
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discover-agent skill
L0: One-Sentence Summary
Match and recommend the most suitable Agent among registered Agents based on the user's need description.
L1: Overview
Procedural skill: understand the need → retrieve with ManageAgent(action='list') → semantic match scoring → rank and present → guide selection; on no match, hand off to create-agent automatically. Applies when the user doesn't know which Agent to pick, wants to browse available Agents, is a new user getting oriented, or is unhappy with the current Agent and wants an alternative. Its value is what the tool can't give: semantic need matching (not keyword search), candidate ranking and presentation, and the create hand-off on no match. list / get are read-only, approval-free.
L2: Detailed Spec
Flow: need understanding → retrieval → match evaluation → ranking → presentation → guided selection.
Stage 1: Need Understanding
Trigger (any): user says "find me a… / is there a… / who can help me…", describes a task without naming an Agent, "which Agents are there", or the system detects a need that mismatches the current Agent. Extract dimensions from the description: domain (legal/finance/tech/education), task_type (consult/review/analyze/create), keywords (contract/report/code/paper…), urgency (routine/urgent).
Stage 2: Retrieval
ManageAgent(action='list') fetches all registered Agents (returns a compact list with name / id / status / description). Filtering: by default show non-offline Agents; offline ones appear only as a fallback when there's no better candidate; exclude internal system Agents (e.g. DesireCore itself) unless the user explicitly asks.
Stage 3: Match Evaluation
Judge match with LLM semantic understanding (not a formula): relevance of description / persona to the need, association of skills to the task type, domain fit, status availability (online preferred). Presentation tiers: strong match → mark "recommended", partial → "possibly relevant", no clear relation → don't show.
Stage 4: Ranking
Descending by overall score; ties broken by online status; show the most relevant few — you decide how many by relevance, avoiding flooding the screen.
Stage 5: Presentation
- With matches: list candidates, each with name, description, key skills, status, and match score; ask the user to choose or refine the need. E.g. "1. Legal Advisor (92%) — contract review and legal risk assessment; skills: contract review / risk assessment / legal research; online".
- No match: report that none were found and offer three options — retry with a more specific description / create a new specialized Agent (hand off to create-agent) / browse all.
- Browse mode (user wants to see all): list name + description grouped by online / offline, and ask whether to view details of any.
Stage 6: Guided Selection
- Chose an Agent → switch to that Agent's conversation, passing the user's need context (source / target / user_intent).
- Wants more detail →
ManageAgent(action='get', id)for details (name / description / status / version / skill count / tool count / Git status); present the key info in natural language or a table and ask whether to chat. - Unhappy with candidates → guide the user to refine the need or suggest creating a new Agent.
- Chose "create new" → invoke the create-agent skill, passing the gathered requirements.
Collaboration and Error Handling
- Collaboration: on no match, hand off to create-agent (pass the need as initial info); on a successful match, optionally create a task and assign it to the target Agent.
- Errors: tool call fails → report error and suggest retry; empty Agent list → guide the user to create the first Agent; overly vague need → ask follow-ups and offer domain options;
getreturns "Agent not found: " → fall back tolistto reconfirm available Agents; recommended Agent in an abnormal state → mark the status and suggest picking an online one. list/getare read-only, approval-free, and risk-free; always done viaManageAgent.