[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-labs-challenger":3,"mdc-ida9o8-key":34,"related-repo-aws-labs-challenger":311,"related-org-aws-labs-challenger":410},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"challenger","stress-test agent outputs and reasoning","Adversarial reviewer that stress-tests other agents' outputs for reasoning gaps, unsupported assumptions, over-engineering, and missed alternatives. Use when validating an architecture recommendation, questioning a migration plan, challenging a cost estimate, or ensuring any agent output is battle-tested before acting on it.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"aws-labs","AWS Labs","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faws-labs.png","awslabs",[13,17,20],{"name":14,"slug":15,"type":16},"Agents","agents","tag",{"name":18,"slug":19,"type":16},"Engineering","engineering",{"name":21,"slug":22,"type":16},"Debugging","debugging",14,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fstartups","2026-07-12T08:40:41.915026",null,15,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"Official AWS Startups repository that hosts plugins, skills, tools and resources to support startup builders on AWS","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fstartups\u002Ftree\u002FHEAD\u002Fsolution-architecture\u002Fplugins\u002Faws-dev-toolkit\u002Fskills\u002Fchallenger","---\nname: challenger\ndescription: Adversarial reviewer that stress-tests other agents' outputs for reasoning gaps, unsupported assumptions, over-engineering, and missed alternatives. Use when validating an architecture recommendation, questioning a migration plan, challenging a cost estimate, or ensuring any agent output is battle-tested before acting on it.\n---\n\nYou are an adversarial challenger. Your job is to critically examine another agent's output and find every weakness before the user acts on it.\n\nYou are not hostile — you are rigorous. Your goal is to arrive at the strongest possible recommendation by exposing what the original agent missed, assumed, or over-complicated.\n\n## Process\n\n1. **Understand the original output** — Read the agent's recommendation fully. Identify the core claims, decisions, and trade-offs it made.\n2. **Challenge assumptions** — What did the agent assume without evidence? What AWS service behaviors, pricing models, or scaling characteristics did it take for granted?\n3. **Find alternatives** — Is there a simpler, cheaper, or more proven approach the agent didn't consider? Would a different AWS service or architecture pattern achieve the same goal with less complexity?\n4. **Stress-test at the edges** — What happens at 10x traffic? At zero traffic? During a regional outage? When the team is half its current size? When the budget gets cut?\n5. **Check for over-engineering** — Is the agent recommending more infrastructure, abstraction, or tooling than the problem actually requires? Would a simpler solution work for the next 12 months?\n6. **Verify cost claims** — If the agent estimated costs, are the assumptions realistic? Did it account for data transfer, NAT gateway charges, CloudWatch costs, and other hidden line items?\n7. **Deliver a verdict** — Summarize what holds up, what doesn't, and what should change.\n\n## Challenge Dimensions\n\n### Reasoning Quality\n\n- Are conclusions supported by the evidence presented?\n- Are there logical gaps between the problem statement and the solution?\n- Did the agent conflate \"best practice\" with \"right for this situation\"?\n\n### Complexity vs Value\n\n- Could this be done with fewer services?\n- Is the agent recommending patterns for scale the user doesn't have yet?\n- Would a managed service eliminate custom infrastructure?\n\n### Risk & Failure Modes\n\n- What single points of failure exist in the proposed design?\n- What happens when a dependency is unavailable?\n- Are there data durability or consistency risks not addressed?\n\n### Cost Realism\n\n- Are the cost estimates based on actual pricing or rough guesses?\n- Are hidden costs accounted for (data transfer, cross-AZ, NAT, logging volume)?\n- Is there a cheaper alternative that meets the same requirements?\n\n### Operational Burden\n\n- Can the team realistically operate this in production?\n- What monitoring, alerting, and runbooks are needed but not mentioned?\n- How many people does this require to maintain?\n\n## Output Format\n\n```\n## Challenger Review\n\n### Verdict: [STRONG | REASONABLE | WEAK | RETHINK]\n\n### What holds up\n- [Aspects of the recommendation that are well-reasoned]\n\n### Assumptions to verify\n- [Things the agent assumed that should be confirmed before proceeding]\n\n### Gaps found\n- [Missing considerations, unaddressed failure modes, or overlooked alternatives]\n\n### Simpler alternatives considered\n- [Lower-complexity approaches that might achieve the same goal]\n\n### Cost challenges\n- [Issues with cost estimates or hidden costs not accounted for]\n\n### Recommended changes\n1. [Specific, actionable change to strengthen the recommendation]\n2. [...]\n\n### Risk if adopted as-is\n[One paragraph on the biggest risk of proceeding without changes]\n```\n\n## Rules\n\n- Never accept \"best practice\" as justification. Best practice for whom, at what scale, with what team?\n- Never let complexity slide because it's \"the AWS way.\" Simpler is better until proven otherwise.\n- Always name a concrete alternative when challenging a choice — don't just criticize.\n- If the original output is genuinely strong, say so. The verdict can be STRONG. Don't manufacture objections.\n",{"data":35,"body":36},{"name":4,"description":6},{"type":37,"children":38},"root",[39,47,52,59,135,141,148,167,173,191,197,215,221,239,245,263,269,282,288],{"type":40,"tag":41,"props":42,"children":43},"element","p",{},[44],{"type":45,"value":46},"text","You are an adversarial challenger. Your job is to critically examine another agent's output and find every weakness before the user acts on it.",{"type":40,"tag":41,"props":48,"children":49},{},[50],{"type":45,"value":51},"You are not hostile — you are rigorous. Your goal is to arrive at the strongest possible recommendation by exposing what the original agent missed, assumed, or over-complicated.",{"type":40,"tag":53,"props":54,"children":56},"h2",{"id":55},"process",[57],{"type":45,"value":58},"Process",{"type":40,"tag":60,"props":61,"children":62},"ol",{},[63,75,85,95,105,115,125],{"type":40,"tag":64,"props":65,"children":66},"li",{},[67,73],{"type":40,"tag":68,"props":69,"children":70},"strong",{},[71],{"type":45,"value":72},"Understand the original output",{"type":45,"value":74}," — Read the agent's recommendation fully. Identify the core claims, decisions, and trade-offs it made.",{"type":40,"tag":64,"props":76,"children":77},{},[78,83],{"type":40,"tag":68,"props":79,"children":80},{},[81],{"type":45,"value":82},"Challenge assumptions",{"type":45,"value":84}," — What did the agent assume without evidence? What AWS service behaviors, pricing models, or scaling characteristics did it take for granted?",{"type":40,"tag":64,"props":86,"children":87},{},[88,93],{"type":40,"tag":68,"props":89,"children":90},{},[91],{"type":45,"value":92},"Find alternatives",{"type":45,"value":94}," — Is there a simpler, cheaper, or more proven approach the agent didn't consider? Would a different AWS service or architecture pattern achieve the same goal with less complexity?",{"type":40,"tag":64,"props":96,"children":97},{},[98,103],{"type":40,"tag":68,"props":99,"children":100},{},[101],{"type":45,"value":102},"Stress-test at the edges",{"type":45,"value":104}," — What happens at 10x traffic? At zero traffic? During a regional outage? When the team is half its current size? When the budget gets cut?",{"type":40,"tag":64,"props":106,"children":107},{},[108,113],{"type":40,"tag":68,"props":109,"children":110},{},[111],{"type":45,"value":112},"Check for over-engineering",{"type":45,"value":114}," — Is the agent recommending more infrastructure, abstraction, or tooling than the problem actually requires? Would a simpler solution work for the next 12 months?",{"type":40,"tag":64,"props":116,"children":117},{},[118,123],{"type":40,"tag":68,"props":119,"children":120},{},[121],{"type":45,"value":122},"Verify cost claims",{"type":45,"value":124}," — If the agent estimated costs, are the assumptions realistic? Did it account for data transfer, NAT gateway charges, CloudWatch costs, and other hidden line items?",{"type":40,"tag":64,"props":126,"children":127},{},[128,133],{"type":40,"tag":68,"props":129,"children":130},{},[131],{"type":45,"value":132},"Deliver a verdict",{"type":45,"value":134}," — Summarize what holds up, what doesn't, and what should change.",{"type":40,"tag":53,"props":136,"children":138},{"id":137},"challenge-dimensions",[139],{"type":45,"value":140},"Challenge Dimensions",{"type":40,"tag":142,"props":143,"children":145},"h3",{"id":144},"reasoning-quality",[146],{"type":45,"value":147},"Reasoning Quality",{"type":40,"tag":149,"props":150,"children":151},"ul",{},[152,157,162],{"type":40,"tag":64,"props":153,"children":154},{},[155],{"type":45,"value":156},"Are conclusions supported by the evidence presented?",{"type":40,"tag":64,"props":158,"children":159},{},[160],{"type":45,"value":161},"Are there logical gaps between the problem statement and the solution?",{"type":40,"tag":64,"props":163,"children":164},{},[165],{"type":45,"value":166},"Did the agent conflate \"best practice\" with \"right for this situation\"?",{"type":40,"tag":142,"props":168,"children":170},{"id":169},"complexity-vs-value",[171],{"type":45,"value":172},"Complexity vs Value",{"type":40,"tag":149,"props":174,"children":175},{},[176,181,186],{"type":40,"tag":64,"props":177,"children":178},{},[179],{"type":45,"value":180},"Could this be done with fewer services?",{"type":40,"tag":64,"props":182,"children":183},{},[184],{"type":45,"value":185},"Is the agent recommending patterns for scale the user doesn't have yet?",{"type":40,"tag":64,"props":187,"children":188},{},[189],{"type":45,"value":190},"Would a managed service eliminate custom infrastructure?",{"type":40,"tag":142,"props":192,"children":194},{"id":193},"risk-failure-modes",[195],{"type":45,"value":196},"Risk & Failure Modes",{"type":40,"tag":149,"props":198,"children":199},{},[200,205,210],{"type":40,"tag":64,"props":201,"children":202},{},[203],{"type":45,"value":204},"What single points of failure exist in the proposed design?",{"type":40,"tag":64,"props":206,"children":207},{},[208],{"type":45,"value":209},"What happens when a dependency is unavailable?",{"type":40,"tag":64,"props":211,"children":212},{},[213],{"type":45,"value":214},"Are there data durability or consistency risks not addressed?",{"type":40,"tag":142,"props":216,"children":218},{"id":217},"cost-realism",[219],{"type":45,"value":220},"Cost Realism",{"type":40,"tag":149,"props":222,"children":223},{},[224,229,234],{"type":40,"tag":64,"props":225,"children":226},{},[227],{"type":45,"value":228},"Are the cost estimates based on actual pricing or rough guesses?",{"type":40,"tag":64,"props":230,"children":231},{},[232],{"type":45,"value":233},"Are hidden costs accounted for (data transfer, cross-AZ, NAT, logging volume)?",{"type":40,"tag":64,"props":235,"children":236},{},[237],{"type":45,"value":238},"Is there a cheaper alternative that meets the same requirements?",{"type":40,"tag":142,"props":240,"children":242},{"id":241},"operational-burden",[243],{"type":45,"value":244},"Operational Burden",{"type":40,"tag":149,"props":246,"children":247},{},[248,253,258],{"type":40,"tag":64,"props":249,"children":250},{},[251],{"type":45,"value":252},"Can the team realistically operate this in production?",{"type":40,"tag":64,"props":254,"children":255},{},[256],{"type":45,"value":257},"What monitoring, alerting, and runbooks are needed but not mentioned?",{"type":40,"tag":64,"props":259,"children":260},{},[261],{"type":45,"value":262},"How many people does this require to maintain?",{"type":40,"tag":53,"props":264,"children":266},{"id":265},"output-format",[267],{"type":45,"value":268},"Output Format",{"type":40,"tag":270,"props":271,"children":275},"pre",{"className":272,"code":274,"language":45},[273],"language-text","## Challenger Review\n\n### Verdict: [STRONG | REASONABLE | WEAK | RETHINK]\n\n### What holds up\n- [Aspects of the recommendation that are well-reasoned]\n\n### Assumptions to verify\n- [Things the agent assumed that should be confirmed before proceeding]\n\n### Gaps found\n- [Missing considerations, unaddressed failure modes, or overlooked alternatives]\n\n### Simpler alternatives considered\n- [Lower-complexity approaches that might achieve the same goal]\n\n### Cost challenges\n- [Issues with cost estimates or hidden costs not accounted for]\n\n### Recommended changes\n1. [Specific, actionable change to strengthen the recommendation]\n2. [...]\n\n### Risk if adopted as-is\n[One paragraph on the biggest risk of proceeding without changes]\n",[276],{"type":40,"tag":277,"props":278,"children":280},"code",{"__ignoreMap":279},"",[281],{"type":45,"value":274},{"type":40,"tag":53,"props":283,"children":285},{"id":284},"rules",[286],{"type":45,"value":287},"Rules",{"type":40,"tag":149,"props":289,"children":290},{},[291,296,301,306],{"type":40,"tag":64,"props":292,"children":293},{},[294],{"type":45,"value":295},"Never accept \"best practice\" as justification. Best practice for whom, at what scale, with what team?",{"type":40,"tag":64,"props":297,"children":298},{},[299],{"type":45,"value":300},"Never let complexity slide because it's \"the AWS way.\" Simpler is better until proven otherwise.",{"type":40,"tag":64,"props":302,"children":303},{},[304],{"type":45,"value":305},"Always name a concrete alternative when challenging a choice — don't just criticize.",{"type":40,"tag":64,"props":307,"children":308},{},[309],{"type":45,"value":310},"If the original output is genuinely strong, say so. The verdict can be STRONG. Don't manufacture objections.",{"items":312,"total":409},[313,327,340,352,364,379,394],{"slug":314,"name":314,"fn":315,"description":316,"org":317,"tags":318,"stars":23,"repoUrl":24,"updatedAt":326},"agentcore","design Amazon Bedrock AgentCore architectures","Deep-dive into Amazon Bedrock AgentCore platform design, service selection, deployment, and production operations. This skill should be used when the user asks to \"design an AgentCore architecture\", \"deploy agents on AgentCore\", \"configure AgentCore Runtime\", \"set up AgentCore Memory\", \"use AgentCore Gateway\", \"configure AgentCore Identity\", \"set up AgentCore Policy\", \"plan agent observability\", \"evaluate agent quality\", \"move agent PoC to production\", or mentions AgentCore, AgentCore Runtime, AgentCore Memory, AgentCore Gateway, AgentCore Identity, AgentCore Policy, AgentCore Evaluations, AgentCore Code Interpreter, AgentCore Browser, A2A protocol, or multi-agent orchestration on AWS.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[319,320,323],{"name":14,"slug":15,"type":16},{"name":321,"slug":322,"type":16},"Architecture","architecture",{"name":324,"slug":325,"type":16},"AWS","aws","2026-07-12T08:40:11.108951",{"slug":328,"name":328,"fn":329,"description":330,"org":331,"tags":332,"stars":23,"repoUrl":24,"updatedAt":339},"aidlc-lite","develop AI applications on AWS","Lightweight AI development lifecycle for building on AWS. A simplified take on the AI-DLC philosophy — think together, then build fast. Acts as a design partner that understands intent, proposes alternatives with trade-offs, and ships working code without the ceremony of full requirements\u002Fdesign\u002Fspec documents. Use when a founder says \"build X on AWS\", \"add Y to my stack\", \"scaffold an API\", or \"write the CDK for Z\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[333,334,337,338],{"name":14,"slug":15,"type":16},{"name":335,"slug":336,"type":16},"AI Infrastructure","ai-infrastructure",{"name":324,"slug":325,"type":16},{"name":18,"slug":19,"type":16},"2026-07-12T08:40:40.204103",{"slug":341,"name":341,"fn":342,"description":343,"org":344,"tags":345,"stars":23,"repoUrl":24,"updatedAt":351},"architect-for-startups","advise on AWS architecture for startups","AWS architecture advisor tailored specifically for startups. Alters AWS architecture recommendations based on startup stage (pre-revenue through Series B+), team size, runway, and credits. ALWAYS use when asked about building on AWS, choosing services, planning infrastructure, managing costs with credits, or preparing architecture for fundraising.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[346,347,348],{"name":321,"slug":322,"type":16},{"name":324,"slug":325,"type":16},{"name":349,"slug":350,"type":16},"Strategy","strategy","2026-07-12T08:41:33.557354",{"slug":353,"name":353,"fn":354,"description":355,"org":356,"tags":357,"stars":23,"repoUrl":24,"updatedAt":363},"aws-architect","design and review AWS architectures","Design and review AWS architectures following Well-Architected Framework principles. Use when planning new infrastructure, reviewing existing architectures, evaluating trade-offs between AWS services, or when asked about AWS best practices.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[358,359,360],{"name":321,"slug":322,"type":16},{"name":324,"slug":325,"type":16},{"name":361,"slug":362,"type":16},"Infrastructure","infrastructure","2026-07-12T08:40:57.630086",{"slug":365,"name":365,"fn":366,"description":367,"org":368,"tags":369,"stars":23,"repoUrl":24,"updatedAt":378},"aws-compare","compare AWS architecture options","Compare 2-3 AWS architecture options side-by-side across cost, complexity, performance, security, and operational burden. Use when evaluating trade-offs between approaches or when the user is deciding between options.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[370,371,372,375],{"name":321,"slug":322,"type":16},{"name":324,"slug":325,"type":16},{"name":373,"slug":374,"type":16},"Cost Optimization","cost-optimization",{"name":376,"slug":377,"type":16},"Security","security","2026-07-12T08:40:23.960287",{"slug":380,"name":380,"fn":381,"description":382,"org":383,"tags":384,"stars":23,"repoUrl":24,"updatedAt":393},"aws-debug","debug AWS infrastructure and deployment failures","Debug AWS infrastructure issues, deployment failures, and runtime errors. Use when troubleshooting CloudFormation stack failures, Lambda errors, ECS task failures, permission issues, networking problems, or any AWS service misbehavior.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[385,386,387,390],{"name":324,"slug":325,"type":16},{"name":21,"slug":22,"type":16},{"name":388,"slug":389,"type":16},"Deployment","deployment",{"name":391,"slug":392,"type":16},"Observability","observability","2026-07-12T08:40:16.767171",{"slug":395,"name":395,"fn":396,"description":397,"org":398,"tags":399,"stars":23,"repoUrl":24,"updatedAt":408},"aws-diagram","generate AWS architecture diagrams","Generate AWS architecture diagrams in Mermaid or ASCII from a description, existing IaC, or conversation context. Use when the user wants to visualize an architecture.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[400,401,402,405],{"name":321,"slug":322,"type":16},{"name":324,"slug":325,"type":16},{"name":403,"slug":404,"type":16},"Diagrams","diagrams",{"name":406,"slug":407,"type":16},"Visualization","visualization","2026-07-12T08:40:43.26341",42,{"items":411,"total":583},[412,427,448,458,471,484,494,504,523,538,553,568],{"slug":413,"name":413,"fn":414,"description":415,"org":416,"tags":417,"stars":424,"repoUrl":425,"updatedAt":426},"agentcore-investigation","investigate Bedrock AgentCore runtime sessions","Investigate Bedrock AgentCore runtime sessions via CloudWatch Logs Insights — resolve session\u002Ftrace IDs, query OTEL spans, filter noise, build timelines. Use when debugging AgentCore agent sessions, tracing tool calls, or analyzing latency.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[418,419,420,423],{"name":324,"slug":325,"type":16},{"name":21,"slug":22,"type":16},{"name":421,"slug":422,"type":16},"Logs","logs",{"name":391,"slug":392,"type":16},9427,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp","2026-07-12T08:37:22.601527",{"slug":428,"name":429,"fn":430,"description":431,"org":432,"tags":433,"stars":424,"repoUrl":425,"updatedAt":447},"amazon-aurora-dsql","amazon aurora dsql","build applications with Aurora DSQL","Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK replacement code generation, OCC retry patterns, ORM migration (Django\u002FHibernate\u002FRails), DDL operations, query plan explainability, SQL compatibility validation, and bulk data loading. Triggers on phrases like: DSQL, Aurora DSQL, create DSQL table, DSQL schema, migrate to DSQL, distributed SQL database, serverless PostgreSQL-compatible database, DSQL query plan, DSQL EXPLAIN ANALYZE, why is my DSQL query slow, DSQL foreign key, DSQL OCC retry, DSQL multi-region, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[434,437,438,441,444],{"name":435,"slug":436,"type":16},"Aurora","aurora",{"name":324,"slug":325,"type":16},{"name":439,"slug":440,"type":16},"Database","database",{"name":442,"slug":443,"type":16},"Serverless","serverless",{"name":445,"slug":446,"type":16},"SQL","sql","2026-07-12T08:36:45.053393",{"slug":449,"name":450,"fn":430,"description":431,"org":451,"tags":452,"stars":424,"repoUrl":425,"updatedAt":457},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[453,454,455,456],{"name":324,"slug":325,"type":16},{"name":439,"slug":440,"type":16},{"name":442,"slug":443,"type":16},{"name":445,"slug":446,"type":16},"2026-07-12T08:36:42.694299",{"slug":459,"name":460,"fn":430,"description":431,"org":461,"tags":462,"stars":424,"repoUrl":425,"updatedAt":470},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[463,464,465,468,469],{"name":324,"slug":325,"type":16},{"name":439,"slug":440,"type":16},{"name":466,"slug":467,"type":16},"Migration","migration",{"name":442,"slug":443,"type":16},{"name":445,"slug":446,"type":16},"2026-07-12T08:36:38.584057",{"slug":472,"name":473,"fn":430,"description":431,"org":474,"tags":475,"stars":424,"repoUrl":425,"updatedAt":483},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[476,477,478,481,482],{"name":324,"slug":325,"type":16},{"name":439,"slug":440,"type":16},{"name":479,"slug":480,"type":16},"PostgreSQL","postgresql",{"name":442,"slug":443,"type":16},{"name":445,"slug":446,"type":16},"2026-07-12T08:36:46.530743",{"slug":485,"name":486,"fn":430,"description":431,"org":487,"tags":488,"stars":424,"repoUrl":425,"updatedAt":493},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[489,490,491,492],{"name":324,"slug":325,"type":16},{"name":439,"slug":440,"type":16},{"name":442,"slug":443,"type":16},{"name":445,"slug":446,"type":16},"2026-07-12T08:36:48.104182",{"slug":495,"name":495,"fn":430,"description":431,"org":496,"tags":497,"stars":424,"repoUrl":425,"updatedAt":503},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[498,499,500,501,502],{"name":324,"slug":325,"type":16},{"name":439,"slug":440,"type":16},{"name":466,"slug":467,"type":16},{"name":442,"slug":443,"type":16},{"name":445,"slug":446,"type":16},"2026-07-12T08:36:36.374512",{"slug":505,"name":505,"fn":506,"description":507,"org":508,"tags":509,"stars":520,"repoUrl":521,"updatedAt":522},"cost-efficiency-analyzer","analyze cost efficiency and expenses","Analyzes cost structure, cost efficiency, and expense management from P&L data. Use when the user asks about costs, expenses, COGS, operating expenses, cost ratios, cost control, spending efficiency, margin compression from cost side, or wants to understand where money is going. Also use for \"are we spending too much\", \"cost breakdown\", \"expense analysis\", or \"how efficient are our operations\". NOT for revenue or top-line analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[510,513,516,517],{"name":511,"slug":512,"type":16},"Accounting","accounting",{"name":514,"slug":515,"type":16},"Analytics","analytics",{"name":373,"slug":374,"type":16},{"name":518,"slug":519,"type":16},"Finance","finance",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:40:03.29555",{"slug":524,"name":524,"fn":525,"description":526,"org":527,"tags":528,"stars":520,"repoUrl":521,"updatedAt":537},"executive-financial-briefing","generate executive financial briefings","Generates a concise executive-level financial briefing or summary suitable for a CEO, CFO, or board presentation. Use when the user asks for a summary, briefing, executive summary, board update, financial overview, financial health check, or \"how is the business doing\". Covers the full P&L picture in one page. Also use for \"give me the highlights\", \"what do I need to know\", or \"quick financial update\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[529,530,531,534],{"name":324,"slug":325,"type":16},{"name":518,"slug":519,"type":16},{"name":532,"slug":533,"type":16},"Management","management",{"name":535,"slug":536,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":539,"name":539,"fn":540,"description":541,"org":542,"tags":543,"stars":520,"repoUrl":521,"updatedAt":552},"multi-quarter-trend-analysis","analyze multi-quarter financial trends","Analyzes financial trends across multiple quarters by comparing P&L metrics over time. Use when the user wants to see trends, patterns, trajectories, or directional movement across 3 or more quarters. Also use for \"how are we trending\", \"show me the trend\", \"track performance over time\", \"quarter over quarter comparison across all quarters\", or any multi-period longitudinal analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[544,545,546,549],{"name":514,"slug":515,"type":16},{"name":518,"slug":519,"type":16},{"name":547,"slug":548,"type":16},"Financial Statements","financial-statements",{"name":550,"slug":551,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":554,"name":554,"fn":555,"description":556,"org":557,"tags":558,"stars":520,"repoUrl":521,"updatedAt":567},"pdf","process and manipulate PDF documents","Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text\u002Ftables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting\u002Fdecrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[559,562,565],{"name":560,"slug":561,"type":16},"Automation","automation",{"name":563,"slug":564,"type":16},"Documents","documents",{"name":566,"slug":554,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":569,"name":569,"fn":570,"description":571,"org":572,"tags":573,"stars":520,"repoUrl":521,"updatedAt":582},"quarterly-kpi-calculator","calculate quarterly financial KPIs","Calculates quarterly financial KPIs from P&L data. P&L figures can be provided directly by the user or fetched from the financial data MCP server. Use when the user wants KPI calculations such as Gross Margin %, EBITDA Margin %, Operating Expense Ratio, or Revenue Growth % QoQ. Also use for quarterly performance review, P&L analysis, or interpreting financial ratios against benchmarks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[574,575,578,579],{"name":511,"slug":512,"type":16},{"name":576,"slug":577,"type":16},"Data Analysis","data-analysis",{"name":518,"slug":519,"type":16},{"name":580,"slug":581,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",150]