القائمة
Select and use temporary domain specialists with bounded JIT context and concrete task-scoped skills.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/marcmarti9/agentit --skill specialist-agent-routingيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Discover Agentit skills through bounded semantic packs, then let the primary AI choose only the concrete bodies that materially help the current stage or worker.
Prepare board and investor decisions, narratives, pre-reads and governance communications around material metrics, variance, risks and explicit asks without spin or fabricated confidence.
Triage company signals, maintain a decision/action queue, coordinate priorities and surface blockers with minimal noise while respecting authority and ownership.
عرض SKILL.md
| name | specialist-agent-routing |
| description | Select and use temporary domain specialists with bounded JIT context and concrete task-scoped skills. |
Specialists are temporary capabilities, not a permanent org chart. Spawn one only when specialization, independent judgment, context isolation, or real parallelism materially improves the reviewed plan.
Use agents/catalog.yaml as a model-readable specialist catalog and skills/using-agent-skills/references/packs.md as the semantic skill map. Catalog triggers are discovery hints for the model, never executable keyword-routing rules.
Profiles determine what can be installed/discovered. Packs expose possible expertise. Workers receive selected skill bodies and selected references, never whole profiles or packs.
For every delegated worker define:
role / objective
scope
relevant_packs (discovery labels only)
selected_skills
selected_references
allowed tools / permissions
read/write ownership
expected output / handoff
risk / safety
verification
stop / escalation
The parent decides how much work the task deserves through the semantic TASK_DECISION — including complexity, plan, topology, review, and verification. There is no pack-depth vocabulary, fixed skill count, or required worker count.
Good reasons include:
Avoid job-title theater, duplicate writers, identical contexts, or delegation that creates more integration work than it removes.
The parent keeps decomposition, semantic decisions, ownership, integration, user communication, and final verification. If a worker discovers a material scope/risk change or missing capability, it reports the gap rather than silently expanding context or permissions.
If the host cannot spawn the preferred specialist, ordinary delegation may fall back to a host-native worker or the parent with the same bounded skill set. When genuine independence is required for safety/review, degrade visibly and escalate instead of pretending same-context review is independent.
Independent concept workers can be useful for ambitious design or architecture when real alternatives would improve judgment. The model decides whether that exploration is worth the cost; there is no named quality tier that automatically triggers it.
Worker claims are never sufficient evidence by themselves. Completion still follows Agentit's verification and Loop/Graph contracts.