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扩展智能体 Skill

「扩展智能体」分类共 662 个 Skill,按仓库 star 排序。分类自动生成,仅供参考。

StatsPAI_skill
brycewang-stanford/Auto-Empirical-Research-Skills4.6k

StatsPAI_skill

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Table 1 balance", "event study", "first-stage F", "Oster bound", "honest_did", "spec_curve", "callaway_santanna", "dragonnet", "text as treatment", "outreg2 in Python", "regression table to Word/Excel", "sp.regtable", "sp.collect", "sp.paper_tables", "sp.feols", "summary_col", "modelsummary", "AER style table", "QJE style table", "epidemiology pipeline", "target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "TRIPOD", "公共健康", "流行病学", "DML", "double machine learning", "causal forest", "meta-learner", "CATE", "conformal causal", "policy learning", "因果机器学习", "ML causal", "decomposition", "Oaxaca-Blinder", "Kitagawa", "DiNardo-Fortin-Lemieux", "DFL", "Gelbach", "RIF decomposition", "wage gap decomposition", "sp.decompose", "sp.oaxaca".

serenity-skill
muxuuu/serenity-skill4.1k

serenity-skill

Research technology and advanced-manufacturing investments using Serenity-inspired supply-chain bottleneck analysis. Use for theme scans, company thesis challenges, candidate comparisons, or learning this method. Prioritize A-shares unless another market is requested. Return research priorities, dated evidence, profit implications, and conditions that would change the judgment.

agentica-claude-proxy
parcadei/Continuous-Claude-v33.9k

agentica-claude-proxy

Guide for integrating Agentica SDK with Claude Code CLI proxy

agentica-prompts
parcadei/Continuous-Claude-v33.9k

agentica-prompts

Write reliable prompts for Agentica/REPL agents that avoid LLM instruction ambiguity

agentica-sdk
parcadei/Continuous-Claude-v33.9k

agentica-sdk

Build Python agents with Agentica SDK - @agentic decorator, spawn(), persistence, MCP integration

agentica-spawn
parcadei/Continuous-Claude-v33.9k

agentica-spawn

Spawn Agentica multi-agent patterns

agent-orchestration
parcadei/Continuous-Claude-v33.9k

agent-orchestration

Agent Orchestration Rules

braintrust-analyze
parcadei/Continuous-Claude-v33.9k

braintrust-analyze

Analyze Claude Code sessions via Braintrust

braintrust-tracing
parcadei/Continuous-Claude-v33.9k

braintrust-tracing

Braintrust tracing for Claude Code - hook architecture, sub-agent correlation, debugging

cli-reference
parcadei/Continuous-Claude-v33.9k

cli-reference

Claude Code CLI commands, flags, headless mode, and automation patterns

complete-skill
parcadei/Continuous-Claude-v33.9k

complete-skill

A complete skill for E2E testing

compound-learnings
parcadei/Continuous-Claude-v33.9k

compound-learnings

Transform session learnings into permanent capabilities (skills, rules, agents). Use when asked to "improve setup", "learn from sessions", "compound learnings", or "what patterns should become skills".

debug-hooks
parcadei/Continuous-Claude-v33.9k

debug-hooks

Systematic hook debugging workflow. Use when hooks aren't firing, producing wrong output, or behaving unexpectedly.

explore
parcadei/Continuous-Claude-v33.9k

explore

Meta-skill for internal codebase exploration at varying depths (quick/deep/architecture)

firecrawl-scrape
parcadei/Continuous-Claude-v33.9k

firecrawl-scrape

Scrape web pages and extract content via Firecrawl MCP

pinme-email
glitternetwork/pinme3.8k

pinme-email

Use this skill when a PinMe project (Worker TypeScript) needs to integrate email sending (send_email). Guides AI to generate correct Worker TS code.

pinme-llm
glitternetwork/pinme3.8k

pinme-llm

Use this skill when a PinMe project (Worker TypeScript) needs to call OpenRouter-backed LLM APIs, including models, chat/completions, streaming, or OpenRouter web search. Guides AI to generate correct Worker TS code.

cc-sdd-new-agent
gotalab/cc-sdd3.7k

cc-sdd-new-agent

Add or extend coding-agent support in cc-sdd by executing the SOP in docs/cc-sdd/sop-new-agent.md end-to-end. Use when introducing a new agent, adding a subagent-capable variant, or evaluating migration of an existing supported agent to skills-based templates.

kiro-impl
gotalab/cc-sdd3.7k

kiro-impl

Implement approved tasks using TDD with native subagent dispatch. Runs all pending tasks autonomously or selected tasks manually.

kiro-impl
gotalab/cc-sdd3.7k

kiro-impl

Implement approved tasks using TDD with subagent dispatch. Runs all pending tasks autonomously or selected tasks manually.

kiro-steering
gotalab/cc-sdd3.7k

kiro-steering

Maintain {{KIRO_DIR}}/steering/ as persistent project memory (bootstrap/sync). Use when initializing or updating steering documents.

aeon
foryourhealth111-pixel/Vibe-Skills3.6k

aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

cancel-ralph
foryourhealth111-pixel/Vibe-Skills3.6k

cancel-ralph

Codex-compatible cancel command for Ralph loop state, preserving the original command name.