Plugins

3 plugins

All AEM as a Cloud Service skills: component development and Dispatcher, workflows

16 skills · plugin

All AEM 6.5 LTS skills: Dispatcher, workflows for AEM 6.5 LTS and AMS environment

21 skills · plugin

The shared foundation of the Han suite: the specialist agent roster the other plugins dispatch, the project-discovery skill with its project-scanner agent, and the canonical evidence and YAGNI rule files. The documentation skills live in han-documentation, the pre-planning research skills in han-research, the planning skills in han-planning, and the coding skills in han-coding; each depends on han

2 skills · plugin

Results for “skill-dispatch”

8 skills

Keyword Research Pipeline

Master orchestrator for the keyword research pipeline. Chains topic-discovery → seed/modifier ideation → competitor + AI-search gap analysis → question mining → BID method → AIO cannibalization check → adversarial redteam → final ranked queue. Same anti-context-bloat pattern as /blog-pipeline (every layer is an Agent dispatch, never a Skill fork).

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A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.

Project Management

Coordinates sprint planning, delivery tracking, experiment management, knowledge management, and changelog governance across project management workflows.

A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.

Creates new Claude skills with proper structure and metadata, then automatically packages and shares them on Slack for team collaboration.

Install and route through OpenSpace, the skill management layer for AI agents, so a host agent can retrieve/rank/load the right SKILL.md out of this jeo-skills catalog (~150 installed skills), then evaluate skill quality from real execution evidence and evolve skills via FIX/DERIVED/CAPTURED updates. Covers install-as-skill-finder, retrieve-a-skill, evaluate-quality, evolve-skills, and local-first hub share/import. Triggers on: openspace, skill finder, skill retrieval, find the right skill, rank skills, skill discovery, skill quality, evolve skill, FIX DERIVED CAPTURED, skill hub, openspace-mcp, DiscoverSkills, skill-discovery, delegate-task.

Scans a project for stack signals and pain points, searches the skills-hub registry and local skills, scores candidates, and presents a ranked shortlist with install commands.

brycewang-stanford

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 mu