[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-TheQmaks-crowdcast-en":3,"guides-for-TheQmaks-crowdcast":380,"similar-k17brwh7tfw6mws70s3qjj8z4n86m5w9-en":381},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":257,"isFallback":243,"parentExtension":262,"providers":314,"relations":318,"repo":319,"tags":377,"workflow":378},1778698083702.1265,"k17brwh7tfw6mws70s3qjj8z4n86m5w9",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Run multi-agent social simulations for prediction and creative exploration. Use when the user wants to simulate group behavior, predict public reactions, explore fictional scenarios, or analyze how agents would interact. Trigger words - \"simulate\", \"prediction\", \"multi-agent\", \"what would happen if\", \"social simulation\", \"crowdcast\".",{"claudeCode":12},"TheQmaks/crowdcast","Crowdcast","https://github.com/TheQmaks/crowdcast",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":241,"workflow":255},1778698138126.487,"kn77jq9bjj3bp00wj7n1e0aqqx86mg46","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":201,"practices":205,"prerequisites":209,"promptVersionExtension":212,"promptVersionScoring":213,"purpose":214,"rationale":215,"score":216,"summary":217,"tags":218,"targetMarket":225,"tier":226,"useCases":227,"workflow":232},[21,26,29,32,36,39,43,47,50,53,57,61,65,69,72,75,78,81,84,87,91,95,99,103,107,110,114,117,121,124,127,130,133,136,139,143,147,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of running multi-agent social simulations and identifies specific use cases like predicting group behavior and exploring fictional scenarios.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers significant value over a simple prompt by orchestrating complex multi-agent simulations, managing state across files, handling multiple phases, and enabling agent interviews, which is beyond default LLM capabilities.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill covers the complete lifecycle of a simulation from document analysis to reporting, stores state in a structured manner, and is designed to be resumable, indicating it's ready for production use.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill is focused on multi-agent social simulations, with commands for simulating, analyzing, resuming, reporting, and interviewing agents related to this core purpose.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The description accurately reflects the skill's functionality, clearly stating its purpose and providing useful trigger words.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill utilizes narrow, verb-noun specific tools like `simulate`, `analyze`, `resume`, `report`, and `interview`, avoiding generalist execution commands.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The SKILL.md file clearly documents all phases, commands, optional flags, and parameters used throughout the simulation workflow, including file locations and data schemas.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","All commands (`simulate`, `analyze`, `resume`, `report`, `interview`) are descriptive, verb-noun based, and relevant to the skill's domain.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Tools and subagents operate on well-defined file paths and JSON structures, requesting only necessary data and producing focused outputs.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under MIT, a permissive open-source license.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The last commit was on May 8, 2026, which is within the last 3 months.",{"category":58,"check":62,"severity":63,"summary":64},"Dependency Management","not_applicable","The skill does not appear to use any third-party dependencies beyond standard Python libraries and the Claude Code environment itself.",{"category":66,"check":67,"severity":24,"summary":68},"Security","Secret Management","The skill does not appear to handle or expose any secrets. All state is managed locally within the simulation directory.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The skill operates on user-provided files and prompts, but the design isolates these as data inputs for specific subagent tasks, and it does not execute arbitrary code fetched from remote sources.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The skill relies on bundled reference files and local file operations, with no runtime fetching of code or data from external URLs.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","All operations are confined to the `.crowdcast/simulations/` directory, ensuring no unintended modifications outside the skill's scope.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No detached process spawns (`nohup`, `&`) or deny-retry loops were found in the scripts.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The skill manages data locally and does not make any undocumented outbound calls or reference confidential data.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled content appears free of hidden-steering tricks, with clean prose and expected Unicode characters.",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Opaque code execution","The bundled scripts are written in plain Python and Bash, with no obfuscation like base64 payloads or runtime code fetching.",{"category":92,"check":93,"severity":24,"summary":94},"Portability","Structural Assumption","File paths are relative to the simulation directory (`.crowdcast/simulations/{sim_id}/`), avoiding assumptions about the user's project structure.",{"category":96,"check":97,"severity":24,"summary":98},"Trust","Issues Attention","There are 0 open issues and 0 closed issues in the last 90 days, indicating low current activity but no outstanding unresolved issues.",{"category":100,"check":101,"severity":24,"summary":102},"Versioning","Release Management","A meaningful version (`0.1.0`) is declared in the SKILL.md frontmatter.",{"category":104,"check":105,"severity":24,"summary":106},"Execution","Validation","The simulation logic relies on structured JSON schemas and file paths, and subagent prompts are carefully constructed, implying robust internal validation.",{"category":66,"check":108,"severity":24,"summary":109},"Unguarded Destructive Operations","The skill primarily performs file operations within its own simulation directory and does not involve inherently destructive operations like deletion or system modification.",{"category":111,"check":112,"severity":24,"summary":113},"Code Execution","Error Handling","The SKILL.md provides detailed error handling strategies for subagent failures, file not found errors, and interview agent not found scenarios, guiding the orchestrator to report clearly and suggest resume options.",{"category":111,"check":115,"severity":63,"summary":116},"Logging","The skill orchestrates subagents that perform actions, but the primary output is file-based state and reports; explicit audit logging within the orchestrator itself is not a stated requirement.",{"category":118,"check":119,"severity":63,"summary":120},"Compliance","GDPR","The skill operates on user-provided documents for simulation purposes and does not handle personal data submission to third parties or persistent storage outside the simulation context.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","The skill operates on local files and internal simulation state, with no regional or jurisdictional logic, making it globally applicable.",{"category":92,"check":125,"severity":24,"summary":126},"Runtime stability","The skill uses standard Bash and Python commands and relies on the Claude Code environment, ensuring cross-platform compatibility.",{"category":44,"check":128,"severity":24,"summary":129},"README","A README.md file exists, clearly explaining the skill's purpose, advantages over alternatives, installation, and usage.",{"category":33,"check":131,"severity":24,"summary":132},"Tool surface size","The skill exposes 5 primary commands (`simulate`, `analyze`, `resume`, `report`, `interview`), falling within the target range.",{"category":40,"check":134,"severity":24,"summary":135},"Overlapping near-synonym tools","The commands (`simulate`, `analyze`, `resume`, `report`, `interview`) are distinct and cover unique aspects of the simulation lifecycle without significant overlap.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom features","All advertised features, such as multi-agent simulation, mode detection, resuming interrupted simulations, and agent interviews, are implemented and documented in the SKILL.md.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","The README provides clear installation instructions via git clone and includes copy-pasteable usage examples for all commands.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","The SKILL.md details specific error handling for various scenarios (subagent failure, simulation not found, agent not found) and provides clear remediation steps or instructions.",{"category":104,"check":148,"severity":63,"summary":149},"Pinned dependencies","The skill does not rely on external package installations; it uses built-in Python and Bash tooling within the Claude Code environment.",{"category":33,"check":151,"severity":63,"summary":152},"Dry-run preview","The skill's operations involve file management and agent orchestration within a defined simulation directory, not external state-changing commands that would typically require a dry-run.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The simulation design emphasizes file-based state management, and the `resume` command allows for retries after failures. Subagent timeouts are implicitly handled by the Claude Code environment.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The skill operates locally and does not emit any telemetry, thus not requiring opt-in/out mechanisms.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The purpose is clearly defined as running multi-agent social simulations for prediction and exploration, with explicit triggers and modes (forecast/creative).",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The SKILL.md frontmatter is concise, containing the name, description, and version, followed by commands and examples, facilitating quick understanding.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is well-structured and delegates detailed procedures to reference files, keeping the main instruction concise and manageable.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The SKILL.md outlines the simulation phases and links to detailed instructions in separate `references/` files, employing progressive disclosure.",{"category":170,"check":174,"severity":63,"summary":175},"Forked exploration","The skill is an orchestrator and does not perform deep exploration or code review that would necessitate `context: fork`.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","Sufficient end-to-end examples are provided in both the README and SKILL.md, demonstrating how to use each command and its options.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The SKILL.md addresses potential failure modes like subagent failure, simulation not found, agent not found for interview, and seed file not found, with clear recovery steps.",{"category":111,"check":183,"severity":63,"summary":184},"Tool Fallback","The skill does not rely on external tools like MCP servers; it operates entirely within the Claude Code environment.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The simulation logic handles unexpected states by reconstructing data or reporting errors clearly, ensuring the workflow halts gracefully when necessary.",{"category":92,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and does not implicitly rely on other skills, ensuring standalone functionality.",1778698137672,"This Claude Code skill orchestrates multi-agent social simulations, allowing users to input documents and prompts to predict societal reactions or explore fictional narratives. It manages the entire simulation lifecycle, including document analysis, persona generation, agent interaction, and reporting, all within the Claude Code environment without external dependencies.",[195,196,197,198,199,200],"Run multi-agent social simulations","Analyze documents to extract knowledge graphs","Generate AI agent personas","Simulate agent interactions in forecast or creative modes","Resume interrupted simulations","Interview simulated agents in character",[202,203,204],"Requiring external APIs or infrastructure","Running simulations at massive scale (millions of agents)","Providing real-time web-based interfaces",[206,207,208],"Simulation design","Agent orchestration","Narrative generation",[210,211],"Claude Code environment","Claude subscription (Pro, Team, or Enterprise)","3.0.0","4.4.0","To enable users to run complex multi-agent social simulations for predictive analysis or creative storytelling without requiring any external setup or APIs.","The skill exhibits excellent documentation, security, and implementation quality, with only minor findings in dependency management and logging not being applicable. High score reflects comprehensive coverage and robust design.",98,"A robust and well-documented skill for multi-agent social simulations with excellent safety and usability features.",[219,220,221,222,223,224],"simulation","multi-agent","social-dynamics","prediction","creative-writing","ai-orchestration","global","verified",[228,229,230,231],"Predicting public reactions to policy changes or news events","Exploring fictional scenarios and character interactions","Analyzing group behavior dynamics","Generating narrative content based on complex prompts",[233,234,235,236,237,238,239,240],"User invokes `/crowdcast simulate \u003Cfiles> \"prompt\"`","Skill generates simulation ID and directory structure","Skill copies seed files and writes initial metadata","Phase 1: Subagent analyzes documents to create knowledge graph and config","Phase 2: Subagents generate key agent personas and crowd group profiles in parallel","Phase 3: Subagents simulate rounds sequentially, updating platform/world state and agent memories","Phase 4: Subagent generates a final report and structured data","User receives report and can optionally interview agents",{"codeQuality":242,"collectedAt":244,"documentation":245,"maintenance":248,"security":253,"testCoverage":254},{"hasLockfile":243},false,1778698118806,{"descriptionLength":246,"readmeSize":247},335,6141,{"closedIssues90d":8,"forks":249,"hasChangelog":243,"manifestVersion":250,"openIssues90d":8,"pushedAt":251,"stars":252},1,"0.1.0",1778251877000,4,{"hasNpmPackage":243,"smitheryVerified":243},{"hasCi":243,"hasTests":243},{"updatedAt":256},1778698138126,{"basePath":258,"githubOwner":259,"githubRepo":260,"locale":18,"slug":260,"type":261},"skills/crowdcast","TheQmaks","crowdcast","skill",{"_creationTime":263,"_id":264,"community":265,"display":266,"identity":269,"parentExtension":272,"providers":302,"relations":309,"tags":310,"workflow":311},1778698083702.1262,"k171tdhndgm24735r23nyaccz586ncrj",{"reviewCount":8},{"description":267,"installMethods":268,"name":260,"sourceUrl":14},"Multi-agent social simulation for prediction and creative exploration. Zero external dependencies — runs entirely within Claude Code.",{"claudeCode":260},{"basePath":270,"githubOwner":259,"githubRepo":260,"locale":18,"slug":260,"type":271},"","plugin",{"_creationTime":273,"_id":274,"community":275,"display":276,"identity":279,"providers":281,"relations":295,"tags":297,"workflow":298},1778698083702.126,"k1777shvk6f4zhzpnan7nm5ma586m6kf",{"reviewCount":8},{"description":277,"installMethods":278,"name":13,"sourceUrl":14},"TheQmaks Claude Code marketplace — hosts the Crowdcast multi-agent social simulation plugin.",{"claudeCode":12},{"basePath":270,"githubOwner":259,"githubRepo":260,"locale":18,"slug":260,"type":280},"marketplace",{"evaluate":282,"extract":288},{"promptVersionExtension":283,"promptVersionScoring":213,"score":284,"tags":285,"targetMarket":225,"tier":226},"3.1.0",99,[219,220,222,286,261,287],"creative","zero-dependency",{"commitSha":289,"license":290,"marketplace":291,"plugin":293},"HEAD","MIT",{"name":292,"pluginCount":249},"theqmaks",{"mcpCount":8,"provider":294,"skillCount":8},"classify",{"repoId":296},"kd760z3j82spgwn0tjtqghm9q586nsyy",[286,220,222,219,261,287],{"evaluatedAt":299,"extractAt":300,"updatedAt":301},1778698098814,1778698083702,1778698177285,{"evaluate":303,"extract":307},{"promptVersionExtension":212,"promptVersionScoring":213,"score":216,"tags":304,"targetMarket":225,"tier":226},[219,220,305,306,222,223],"ai-agents","social-simulation",{"commitSha":289,"plugin":308},{"mcpCount":8,"provider":294,"skillCount":249},{"parentExtensionId":274,"repoId":296},[305,223,220,222,219,306],{"evaluatedAt":312,"extractAt":300,"updatedAt":313},1778698118412,1778698177457,{"evaluate":315,"extract":317},{"promptVersionExtension":212,"promptVersionScoring":213,"score":216,"tags":316,"targetMarket":225,"tier":226},[219,220,221,222,223,224],{"commitSha":289,"license":290},{"parentExtensionId":264,"repoId":296},{"_creationTime":320,"_id":296,"identity":321,"providers":322,"workflow":373},1778698079325.3296,{"githubOwner":259,"githubRepo":260,"sourceUrl":14},{"classify":323,"discover":363,"github":366},{"commitSha":289,"extensions":324},[325,335,345],{"basePath":270,"description":277,"displayName":292,"installMethods":326,"rationale":327,"selectedPaths":328,"source":334,"sourceLanguage":18,"type":280},{"claudeCode":12},"marketplace.json at .claude-plugin/marketplace.json",[329,332],{"path":330,"priority":331},".claude-plugin/marketplace.json","mandatory",{"path":333,"priority":331},"README.md","rule",{"basePath":270,"description":267,"displayName":260,"installMethods":336,"rationale":337,"selectedPaths":338,"source":334,"sourceLanguage":18,"type":271},{"claudeCode":260},"plugin manifest at .claude-plugin/plugin.json",[339,341,342],{"path":340,"priority":331},".claude-plugin/plugin.json",{"path":333,"priority":331},{"path":343,"priority":344},"skills/crowdcast/SKILL.md","medium",{"basePath":258,"description":10,"displayName":260,"installMethods":346,"rationale":347,"selectedPaths":348,"source":334,"sourceLanguage":18,"type":261},{"claudeCode":12},"SKILL.md frontmatter at skills/crowdcast/SKILL.md",[349,351,353,355,357,359,361],{"path":350,"priority":331},"SKILL.md",{"path":352,"priority":344},"references/data-schemas.md",{"path":354,"priority":344},"references/phase1-analyzer.md",{"path":356,"priority":344},"references/phase2-profiler.md",{"path":358,"priority":344},"references/phase3-simulator-creative.md",{"path":360,"priority":344},"references/phase3-simulator-forecast.md",{"path":362,"priority":344},"references/phase4-reporter.md",{"sources":364},[365],"manual",{"closedIssues90d":8,"description":367,"forks":249,"openIssues90d":8,"pushedAt":251,"readmeSize":247,"stars":252,"topics":368},"Multi-agent social simulation as a Claude Code skill. Zero dependencies — predict anything with /crowdcast simulate.",[369,305,370,371,372,220,222,219,306,260],"agent-skills","claude","claude-code","claude-skill",{"classifiedAt":374,"discoverAt":375,"extractAt":376,"githubAt":376,"updatedAt":374},1778698083527,1778698079325,1778698081672,[224,223,220,222,219,221],{"evaluatedAt":256,"extractAt":300,"updatedAt":379},1778698177653,[],[382,410,440,462,494,524],{"_creationTime":383,"_id":384,"community":385,"display":386,"identity":392,"providers":396,"relations":404,"tags":406,"workflow":407},1778696691708.3022,"k178f9kby1bngrepw8bsaxb06s86n9z2",{"reviewCount":8},{"description":387,"installMethods":388,"name":390,"sourceUrl":391},"Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory\n",{"claudeCode":389},"ruvnet/ruflo","hive-mind-advanced","https://github.com/ruvnet/ruflo",{"basePath":393,"githubOwner":394,"githubRepo":395,"locale":18,"slug":390,"type":261},".claude/skills/hive-mind-advanced","ruvnet","ruflo",{"evaluate":397,"extract":403},{"promptVersionExtension":212,"promptVersionScoring":213,"score":216,"tags":398,"targetMarket":225,"tier":226},[220,399,224,400,401,402],"coordination","collective-intelligence","memory-systems","consensus-mechanisms",{"commitSha":289},{"repoId":405},"kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[224,400,402,399,401,220],{"evaluatedAt":408,"extractAt":409,"updatedAt":408},1778699115084,1778696691708,{"_creationTime":411,"_id":412,"community":413,"display":414,"identity":420,"providers":424,"relations":433,"tags":436,"workflow":437},1778699234184.6135,"k175frmf44tn80mcd6gvw1c1th86ngq9",{"reviewCount":8},{"description":415,"installMethods":416,"name":418,"sourceUrl":419},"Invoke parallel document-specialist agents for external web searches and documentation lookup",{"claudeCode":417},"Yeachan-Heo/oh-my-claudecode","external-context","https://github.com/Yeachan-Heo/oh-my-claudecode",{"basePath":421,"githubOwner":422,"githubRepo":423,"locale":18,"slug":418,"type":261},"skills/external-context","Yeachan-Heo","oh-my-claudecode",{"evaluate":425,"extract":432},{"promptVersionExtension":212,"promptVersionScoring":213,"score":426,"tags":427,"targetMarket":225,"tier":226},100,[428,429,430,431,220],"search","documentation","research","information-retrieval",{"commitSha":289},{"parentExtensionId":434,"repoId":435},"k17brg5egdw1jbncj1j4wfv3fh86n639","kd74zv63fryf9prygtq7gf4es986n22y",[429,431,220,430,428],{"evaluatedAt":438,"extractAt":439,"updatedAt":438},1778699449790,1778699234184,{"_creationTime":441,"_id":442,"community":443,"display":444,"identity":448,"providers":450,"relations":458,"tags":459,"workflow":460},1778696691708.3054,"k17by7bzagajqkcvcetdw10cz586nxbj",{"reviewCount":8},{"description":445,"installMethods":446,"name":447,"sourceUrl":391},"Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.",{"claudeCode":389},"swarm-orchestration",{"basePath":449,"githubOwner":394,"githubRepo":395,"locale":18,"slug":447,"type":261},".claude/skills/swarm-orchestration",{"evaluate":451,"extract":457},{"promptVersionExtension":212,"promptVersionScoring":213,"score":426,"tags":452,"targetMarket":225,"tier":226},[453,220,454,455,399,456],"agent-orchestration","swarm","distributed-systems","ai-workflow",{"commitSha":289},{"repoId":405},[453,456,399,455,220,454],{"evaluatedAt":461,"extractAt":409,"updatedAt":461},1778699363559,{"_creationTime":463,"_id":464,"community":465,"display":466,"identity":472,"providers":477,"relations":487,"tags":489,"workflow":490},1778698837670.8,"k17a19x757qjaehqa5jah8k7y986n55p",{"reviewCount":8},{"description":467,"installMethods":468,"name":470,"sourceUrl":471},"Time series forecasting for AI agents. ARIMA and Holt-Winters predictions with confidence intervals. Predict revenue, traffic, prices, or any sequential data. Sub-5ms inference.",{"claudeCode":469},"Whatsonyourmind/oraclaw","OraClaw Forecast","https://github.com/Whatsonyourmind/oraclaw",{"basePath":473,"githubOwner":474,"githubRepo":475,"locale":18,"slug":476,"type":261},"mission-control/packages/clawhub-skills/oraclaw-forecast","Whatsonyourmind","oraclaw","oraclaw-forecast",{"evaluate":478,"extract":486},{"promptVersionExtension":212,"promptVersionScoring":213,"score":426,"tags":479,"targetMarket":225,"tier":226},[480,481,222,482,483,484,485],"forecasting","time-series","arima","holt-winters","analytics","data-science",{"commitSha":289,"license":290},{"repoId":488},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg",[484,482,485,480,483,222,481],{"evaluatedAt":491,"extractAt":492,"updatedAt":493},1778698975269,1778698837670,1778699187952,{"_creationTime":495,"_id":496,"community":497,"display":498,"identity":504,"providers":508,"relations":517,"tags":520,"workflow":521},1778695548458.4019,"k178tdf3aqzyah8h8w58dxt8g186mtns",{"reviewCount":8},{"description":499,"installMethods":500,"name":502,"sourceUrl":503},"Design and simulate a minimal CPU from scratch: define an instruction set architecture (ISA), build the datapath (ALU, register file, program counter, memory interface), design the control unit (hardwired or microprogrammed), implement the fetch-decode-execute cycle, and verify by tracing a small program clock cycle by clock cycle. The capstone \"computer inside a computer\" exercise that composes combinational and sequential building blocks into a complete processor.\n",{"claudeCode":501},"pjt222/agent-almanac","simulate-cpu-architecture","https://github.com/pjt222/agent-almanac",{"basePath":505,"githubOwner":506,"githubRepo":507,"locale":18,"slug":502,"type":261},"skills/simulate-cpu-architecture","pjt222","agent-almanac",{"evaluate":509,"extract":516},{"promptVersionExtension":212,"promptVersionScoring":213,"score":426,"tags":510,"targetMarket":225,"tier":226},[511,512,513,514,219,515],"cpu-architecture","digital-logic","computer-science","instruction-set","hardware-design",{"commitSha":289},{"parentExtensionId":518,"repoId":519},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[513,511,512,515,514,219],{"evaluatedAt":522,"extractAt":523,"updatedAt":522},1778701650465,1778695548458,{"_creationTime":525,"_id":526,"community":527,"display":528,"identity":534,"providers":539,"relations":548,"tags":551,"workflow":552},1778683100520.3008,"k17ey9mx0h8mhbdfzz37c63c0s86m409",{"reviewCount":8},{"description":529,"installMethods":530,"name":532,"sourceUrl":533},"Writes or reviews lyrics with professional prosody, rhyme craft, and quality checks. Use when writing new lyrics, revising existing lyrics, or when the user says 'let's work on a track.'",{"claudeCode":531},"bitwize-music-studio/claude-ai-music-skills","Lyric Writer","https://github.com/bitwize-music-studio/claude-ai-music-skills",{"basePath":535,"githubOwner":536,"githubRepo":537,"locale":18,"slug":538,"type":261},"skills/lyric-writer","bitwize-music-studio","claude-ai-music-skills","lyric-writer",{"evaluate":540,"extract":546},{"promptVersionExtension":212,"promptVersionScoring":213,"score":284,"tags":541,"targetMarket":225,"tier":226},[542,543,544,223,545],"lyrics","music","songwriting","suno",{"commitSha":289,"license":547},"CC0-1.0",{"parentExtensionId":549,"repoId":550},"k1754vkdjckrkqvz9x7tjrvhzn86n1gc","kd70cgrajsrnk5gmq60rhq30zd86nyc0",[223,542,543,544,545],{"evaluatedAt":553,"extractAt":554,"updatedAt":553},1778683512322,1778683100520]