Supports problem identification, the first phase of performance management. Runs a broad scan for potential issues a state or city agency might focus on, drawing on the jurisdiction's performance data through an OpenContext MCP connector, local media coverage, public testimony, and social media where a tool can reach it. Covers state labor and workforce, state health and human services, city 311, emergency dispatch (CAD), and city operations. Produces a ranked list of candidate problems for agency leaders and performance officers, each tagged by evidence source, shape (acute/chronic), horizon, degree of control, and dominant cost, with a verified/partial/unverified confidence flag, plus a second list of adjacent problems where the agency has leverage but no formal remit. Use when someone asks what a state or city agency should prioritize, where to target process improvement, what data or local coverage shows about emerging problems, or for a priority scan or focus areas.
Plugin sgarcese-problem-identification-scan from marketplace civic-skill-exchange in AI-Lab-for-Cities-at-Harvard/civic-skill-exchange. Commands read from the repo's manifest — 22 days ago.
problem-identification-scanSupports problem identification, the first phase of performance management. Runs a broad scan for potential issues a state or city agency might focus on, drawing on the jurisdiction's performance data through an OpenContext MCP connector, local media coverage, public testimony, and social media wher
Housing dashboards, refreshes and briefs for any U.S. city or county (a state works too, with fewer comparisons) from Census ACS, BLS and HUD open data: demand and tenure, construction costs and labor, and federally assisted housing, compared with the surrounding region, state and nation.
3 skills · 5 MCP servers
Separates a skill written for one organization into two things a portable generalized skill, and a context file holding every value that was specific to that place. Records how far the skill was generalized to travel, since that is what decides which values are local at all. Reports what it was unsure about rather than guessing. Produces drafts for the author to check; decides nothing and publishes nothing.
1 skill
Fits a generalized skill to one organization writes its local values back in and produces an ordinary, self-contained skill that runs without a context file. Works out what it can from the organization's own data and public sources, and asks a person only what no source can answer, at the precision the value actually needs. Confirms every machine-consumed value before shipping, because a plausible wrong value is worse than a blank one.
1 skill
Rewrites a government notice template into plain language at a target reading level, preserving every legally required element. Works on templates, not on filled notices containing a specific person's information. Produces a draft for human review; it does not approve or send anything.
1 skill
This is the original civic analytics workflow. Orchestrator for local government policy analysis and civic innovation. ALWAYS use for any request involving open government data, city, county or town services, neighborhood or ward equity, public policy, government performance, 311 and service request analysis, housing, safety or transportation — even if the user hasn't asked for a 'full analysis'. Routes between five sub-skills: problem framing (Bloomberg-inspired), policy analysis (J-PAL-inspired), communication (GovLab/InnovateUS-inspired), peer benchmarking, and performance management (Results for America / PerformanceStat). Triggers include: 'full analysis', 'policy brief', 'data-driven recommendation', 'service improvement project', 'investigate [issue]', 'compare us to peer cities', 'peer counties', 'similar towns', 'what does the data show', 'help me write a memo about', or any request combining problem definition, data analysis and communication for government or civic purposes.
1 skill
Plan de arborización urbana en tres fases a partir del portal de datos abiertos de una ciudad y su conector OpenContext, priorizando lo que el conector entrega directamente. Produce un inventario de los datos del portal útiles para una gestión basada en la naturaleza, con sus vacíos; un ranking de oportunidad de siembra por distrito, comuna o barrio que combina déficit de árboles, cobertura de copa y vulnerabilidad; y una propuesta de especies por barrio basada en el censo arbóreo de la propia ciudad. Úsalo cuando un equipo de medio ambiente o planeación pregunte qué datos abiertos sirven para enverdecer la ciudad, dónde sembrar primero, qué barrios tienen poca sombra y muchas personas mayores, o qué especies sembrar en un barrio, aunque no mencione el conector.
1 skill
Security default for organizations: seated outermost, it keeps the organization's classic hooks, prompt content, settings, tool policy and pinned environment out of reach of the plugins a person installs, and adds no policy of its own; one managed option, allowManagedModsOnly, limits mods to the organization's.
Security review, dependency scanning, policy gates, and CVE monitoring
2 skills · 1 command · 1 agent
Build, deploy, and operate AI agents on AWS. Skills for scaffolding agents with Amazon Bedrock AgentCore, connecting tools, memory, policies, evaluation, debugging, and production hardening.
8 skills · 1 MCP server
Cedar policy enforcement + Ed25519 signed receipts for every Claude Code tool call. First cryptographic governance plugin — decisions are policy-gated before they run and every decision produces a tamper-evident receipt verifiable offline.
1 skill · 2 commands · 2 agents · hooks
Streamline people operations — recruiting, onboarding, performance reviews, compensation analysis, and policy guidance. Maintain compliance and keep your team running smoothly.
9 skills · 5 MCP servers
Per-deal-and-packaging Commercial domain. v2.8.0 ships 8 skills: orchestrator + pricing-strategist (model picker + Van Westendorp WTP + packaging) + deal-desk (deal scorer + discount approval routing + redline) + partnerships-architect (5-tier classifier + joint GTM + revshare modeler) + channel-economics (cost-to-serve + ROI + mix optimizer) + commercial-policy (data-backed discount matrix + exception flow + linter) + rfp-responder (Shipley structured RFP/RFI/RFQ + win-theme + winrate predictor, context: fork) + commercial-forecaster (4Q-weighted bookings + cohort NRR/GRR + funnel-confidence with mandatory assumption disclosure). Hard rules: pricing outputs model+range (never a single number), deal outputs route to named human approver (never auto-approve), forecast outputs surface conversion assumption, RFP never invents claims for GAP requirements. 21 stdlib Python tools, 28+ reference docs. Distinct from business-growth (sales execution), c-level-advisor/cro-advisor (strategic), finance (close+report).
8 skills · 9 commands · 1 agent
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