Yige
caccaee6ef
## 摘要 / Summary ### 中文 在去除机械数字上限（#58）后，进一步删掉技能里追问/展示数量的**软性倾向措辞**——create 的「一次不要问太多以免用户负担」、discover 的「避免一次刷屏过多」。数量与节奏完全交由执行 Agent 自主判断，不带任何倾向暗示。create 2.5.4 / discover 2.6.4，manifest 1.2.15，中英双份同步、重算 source_hash（validate-i18n 通过）。 ### English After removing the mechanical numeric caps (#58), further drop the soft directional hints on ask/display count — create's "don't ask too many so as not to burden the user" and discover's "avoid flooding the screen". Count and pacing are left entirely to the executing Agent's judgment with no bias. create 2.5.4 / discover 2.6.4, manifest 1.2.15, 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。 | 2.6.4 | 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 ones — you decide how many by relevance.
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.