القائمة
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/majiayu000/claude-skill-registry --skill researchيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
LLM token logprobs and calibration. Per-decision confidence, ECE, Brier, reliability diagrams, low-confidence triage.
Analyze LLM token logprobs and calibration. Use for per-decision confidence, ECE, Brier scores, reliability diagrams, and low-confidence triage.
回顾最近 N 天的 Claude Code 使用记录——扫描原始会话数据，按主题分组汇总"我都做了什么"，并从个人操作系统视角输出模式、风险与增删建议。当用户说 /recap、"看看我这几天做了什么"、"回顾一下我最近的会话"、"这两天我用 claude 干了啥"、"活动回顾" 时使用。
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | research |
| description | Multi-source research across code, discourse, and academic channels. |
| alwaysApply | false |
| category | orchestration |
| tags | ["research","synthesis","multi-source"] |
| tools | [] |
| estimated_tokens | 600 |
| progressive_loading | true |
| orchestrates | ["tome:code-search","tome:discourse","tome:papers","tome:triz","tome:synthesize"] |
| model_hint | standard |
Run a full multi-source research session: classify the domain, dispatch parallel agents, synthesize findings, and output a formatted report.
Run the domain classifier on the topic:
from tome.scripts.domain_classifier import classify
result = classify(topic)
# result.domain, result.triz_depth, result.channel_weights
If confidence < 0.6, ask the user to confirm or override the domain classification before proceeding.
from tome.scripts.research_planner import plan
research_plan = plan(result)
# research_plan.channels, research_plan.weights, research_plan.triz_depth
from tome.session import SessionManager
mgr = SessionManager(Path.cwd())
session = mgr.create(topic, result.domain, result.triz_depth, research_plan.channels)
Launch research agents in parallel using the Agent tool. Use this mapping:
| Channel | Agent Type | Prompt Includes |
|---|---|---|
| code | tome:code-searcher | topic |
| discourse | tome:discourse-scanner | topic, domain, subreddits |
| academic | tome:literature-reviewer | topic, domain |
| triz | tome:triz-analyst | topic, domain, triz_depth |
Rules:
Each agent prompt must include:
After all agents return:
tome.synthesis.merger.merge_findings()tome.synthesis.ranker.rank_findings()from tome.output.report import format_report, format_brief, format_transcript
# Default to report format
output = format_report(session)
# Save to docs/research/
output_path = f"docs/research/{session.id}-{slug}.md"
Save the session state:
mgr.save(session)
Display a brief summary to the user:
Then offer interactive refinement:
"Use /tome:dig \"subtopic\" to explore specific areas."
| Flag | Format | Function |
|---|---|---|
| (default) | report | format_report() |
--format brief | brief | format_brief() |
--format transcript | transcript | format_transcript() |