{"id":"854404b3-4339-484f-bfbe-62633c44d4d9","shortId":"9J5cVA","kind":"skill","title":"temporal-python-pro","tagline":"Master Temporal workflow orchestration with Python SDK. Implements durable workflows, saga patterns, and distributed transactions. Covers async/await, testing strategies, and production deployment.","description":"## Use this skill when\n\n- Working on temporal python pro tasks or workflows\n- Needing guidance, best practices, or checklists for temporal python pro\n\n## Do not use this skill when\n\n- The task is unrelated to temporal python pro\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources/implementation-playbook.md`.\n\nYou are an expert Temporal workflow developer specializing in Python SDK implementation, durable workflow design, and production-ready distributed systems.\n\n## Purpose\n\nExpert Temporal developer focused on building reliable, scalable workflow orchestration systems using the Python SDK. Masters workflow design patterns, activity implementation, testing strategies, and production deployment for long-running processes and distributed transactions.\n\n## Capabilities\n\n### Python SDK Implementation\n\n**Worker Configuration and Startup**\n\n- Worker initialization with proper task queue configuration\n- Workflow and activity registration patterns\n- Concurrent worker deployment strategies\n- Graceful shutdown and resource cleanup\n- Connection pooling and retry configuration\n\n**Workflow Implementation Patterns**\n\n- Workflow definition with `@workflow.defn` decorator\n- Async/await workflow entry points with `@workflow.run`\n- Workflow-safe time operations with `workflow.now()`\n- Deterministic workflow code patterns\n- Signal and query handler implementation\n- Child workflow orchestration\n- Workflow continuation and completion strategies\n\n**Activity Implementation**\n\n- Activity definition with `@activity.defn` decorator\n- Sync vs async activity execution models\n- ThreadPoolExecutor for blocking I/O operations\n- ProcessPoolExecutor for CPU-intensive tasks\n- Activity context and cancellation handling\n- Heartbeat reporting for long-running activities\n- Activity-specific error handling\n\n### Async/Await and Execution Models\n\n**Three Execution Patterns** (Source: docs.temporal.io):\n\n1. **Async Activities** (asyncio)\n   - Non-blocking I/O operations\n   - Concurrent execution within worker\n   - Use for: API calls, async database queries, async libraries\n\n2. **Sync Multithreaded** (ThreadPoolExecutor)\n   - Blocking I/O operations\n   - Thread pool manages concurrency\n   - Use for: sync database clients, file operations, legacy libraries\n\n3. **Sync Multiprocess** (ProcessPoolExecutor)\n   - CPU-intensive computations\n   - Process isolation for parallel processing\n   - Use for: data processing, heavy calculations, ML inference\n\n**Critical Anti-Pattern**: Blocking the async event loop turns async programs into serial execution. Always use sync activities for blocking operations.\n\n### Error Handling and Retry Policies\n\n**ApplicationError Usage**\n\n- Non-retryable errors with `non_retryable=True`\n- Custom error types for business logic\n- Dynamic retry delay with `next_retry_delay`\n- Error message and context preservation\n\n**RetryPolicy Configuration**\n\n- Initial retry interval and backoff coefficient\n- Maximum retry interval (cap exponential backoff)\n- Maximum attempts (eventual failure)\n- Non-retryable error types classification\n\n**Activity Error Handling**\n\n- Catching `ActivityError` in workflows\n- Extracting error details and context\n- Implementing compensation logic\n- Distinguishing transient vs permanent failures\n\n**Timeout Configuration**\n\n- `schedule_to_close_timeout`: Total activity duration limit\n- `start_to_close_timeout`: Single attempt duration\n- `heartbeat_timeout`: Detect stalled activities\n- `schedule_to_start_timeout`: Queuing time limit\n\n### Signal and Query Patterns\n\n**Signals** (External Events)\n\n- Signal handler implementation with `@workflow.signal`\n- Async signal processing within workflow\n- Signal validation and idempotency\n- Multiple signal handlers per workflow\n- External workflow interaction patterns\n\n**Queries** (State Inspection)\n\n- Query handler implementation with `@workflow.query`\n- Read-only workflow state access\n- Query performance optimization\n- Consistent snapshot guarantees\n- External monitoring and debugging\n\n**Dynamic Handlers**\n\n- Runtime signal/query registration\n- Generic handler patterns\n- Workflow introspection capabilities\n\n### State Management and Determinism\n\n**Deterministic Coding Requirements**\n\n- Use `workflow.now()` instead of `datetime.now()`\n- Use `workflow.random()` instead of `random.random()`\n- No threading, locks, or global state\n- No direct external calls (use activities)\n- Pure functions and deterministic logic only\n\n**State Persistence**\n\n- Automatic workflow state preservation\n- Event history replay mechanism\n- Workflow versioning with `workflow.get_version()`\n- Safe code evolution strategies\n- Backward compatibility patterns\n\n**Workflow Variables**\n\n- Workflow-scoped variable persistence\n- Signal-based state updates\n- Query-based state inspection\n- Mutable state handling patterns\n\n### Type Hints and Data Classes\n\n**Python Type Annotations**\n\n- Workflow input/output type hints\n- Activity parameter and return types\n- Data classes for structured data\n- Pydantic models for validation\n- Type-safe signal and query handlers\n\n**Serialization Patterns**\n\n- JSON serialization (default)\n- Custom data converters\n- Protobuf integration\n- Payload encryption\n- Size limit management (2MB per argument)\n\n### Testing Strategies\n\n**WorkflowEnvironment Testing**\n\n- Time-skipping test environment setup\n- Instant execution of `workflow.sleep()`\n- Fast testing of month-long workflows\n- Workflow execution validation\n- Mock activity injection\n\n**Activity Testing**\n\n- ActivityEnvironment for unit tests\n- Heartbeat validation\n- Timeout simulation\n- Error injection testing\n- Idempotency verification\n\n**Integration Testing**\n\n- Full workflow with real activities\n- Local Temporal server with Docker\n- End-to-end workflow validation\n- Multi-workflow coordination testing\n\n**Replay Testing**\n\n- Determinism validation against production histories\n- Code change compatibility verification\n- Continuous integration replay testing\n\n### Production Deployment\n\n**Worker Deployment Patterns**\n\n- Containerized worker deployment (Docker/Kubernetes)\n- Horizontal scaling strategies\n- Task queue partitioning\n- Worker versioning and gradual rollout\n- Blue-green deployment for workers\n\n**Monitoring and Observability**\n\n- Workflow execution metrics\n- Activity success/failure rates\n- Worker health monitoring\n- Queue depth and lag metrics\n- Custom metric emission\n- Distributed tracing integration\n\n**Performance Optimization**\n\n- Worker concurrency tuning\n- Connection pool sizing\n- Activity batching strategies\n- Workflow decomposition for scalability\n- Memory and CPU optimization\n\n**Operational Patterns**\n\n- Graceful worker shutdown\n- Workflow execution queries\n- Manual workflow intervention\n- Workflow history export\n- Namespace configuration and isolation\n\n## When to Use Temporal Python\n\n**Ideal Scenarios**:\n\n- Distributed transactions across microservices\n- Long-running business processes (hours to years)\n- Saga pattern implementation with compensation\n- Entity workflow management (carts, accounts, inventory)\n- Human-in-the-loop approval workflows\n- Multi-step data processing pipelines\n- Infrastructure automation and orchestration\n\n**Key Benefits**:\n\n- Automatic state persistence and recovery\n- Built-in retry and timeout handling\n- Deterministic execution guarantees\n- Time-travel debugging with replay\n- Horizontal scalability with workers\n- Language-agnostic interoperability\n\n## Common Pitfalls\n\n**Determinism Violations**:\n\n- Using `datetime.now()` instead of `workflow.now()`\n- Random number generation with `random.random()`\n- Threading or global state in workflows\n- Direct API calls from workflows\n\n**Activity Implementation Errors**:\n\n- Non-idempotent activities (unsafe retries)\n- Missing timeout configuration\n- Blocking async event loop with sync code\n- Exceeding payload size limits (2MB)\n\n**Testing Mistakes**:\n\n- Not using time-skipping environment\n- Testing workflows without mocking activities\n- Ignoring replay testing in CI/CD\n- Inadequate error injection testing\n\n**Deployment Issues**:\n\n- Unregistered workflows/activities on workers\n- Mismatched task queue configuration\n- Missing graceful shutdown handling\n- Insufficient worker concurrency\n\n## Integration Patterns\n\n**Microservices Orchestration**\n\n- Cross-service transaction coordination\n- Saga pattern with compensation\n- Event-driven workflow triggers\n- Service dependency management\n\n**Data Processing Pipelines**\n\n- Multi-stage data transformation\n- Parallel batch processing\n- Error handling and retry logic\n- Progress tracking and reporting\n\n**Business Process Automation**\n\n- Order fulfillment workflows\n- Payment processing with compensation\n- Multi-party approval processes\n- SLA enforcement and escalation\n\n## Best Practices\n\n**Workflow Design**:\n\n1. Keep workflows focused and single-purpose\n2. Use child workflows for scalability\n3. Implement idempotent activities\n4. Configure appropriate timeouts\n5. Design for failure and recovery\n\n**Testing**:\n\n1. Use time-skipping for fast feedback\n2. Mock activities in workflow tests\n3. Validate replay with production histories\n4. Test error scenarios and compensation\n5. Achieve high coverage (≥80% target)\n\n**Production**:\n\n1. Deploy workers with graceful shutdown\n2. Monitor workflow and activity metrics\n3. Implement distributed tracing\n4. Version workflows carefully\n5. Use workflow queries for debugging\n\n## Resources\n\n**Official Documentation**:\n\n- Python SDK: python.temporal.io\n- Core Concepts: docs.temporal.io/workflows\n- Testing Guide: docs.temporal.io/develop/python/testing-suite\n- Best Practices: docs.temporal.io/develop/best-practices\n\n**Architecture**:\n\n- Temporal Architecture: github.com/temporalio/temporal/blob/main/docs/architecture/README.md\n- Testing Patterns: github.com/temporalio/temporal/blob/main/docs/development/testing.md\n\n**Key Takeaways**:\n\n1. Workflows = orchestration, Activities = external calls\n2. Determinism is mandatory for workflows\n3. Idempotency is critical for activities\n4. Test with time-skipping for fast feedback\n5. Monitor and observe in production\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.","tags":["temporal","python","pro","antigravity","awesome","skills","sickn33","agent-skills","agentic-skills","ai-agent-skills","ai-agents","ai-coding"],"capabilities":["skill","source-sickn33","skill-temporal-python-pro","topic-agent-skills","topic-agentic-skills","topic-ai-agent-skills","topic-ai-agents","topic-ai-coding","topic-ai-workflows","topic-antigravity","topic-antigravity-skills","topic-claude-code","topic-claude-code-skills","topic-codex-cli","topic-codex-skills"],"categories":["antigravity-awesome-skills"],"synonyms":[],"warnings":[],"endpointUrl":"https://skills.sh/sickn33/antigravity-awesome-skills/temporal-python-pro","protocol":"skill","transport":"skills-sh","auth":{"type":"none","details":{"cli":"npx skills add sickn33/antigravity-awesome-skills","source_repo":"https://github.com/sickn33/antigravity-awesome-skills","install_from":"skills.sh"}},"qualityScore":"0.700","qualityRationale":"deterministic score 0.70 from registry signals: · indexed on github topic:agent-skills · 34460 github stars · SKILL.md body (10,589 chars)","verified":false,"liveness":"unknown","lastLivenessCheck":null,"agentReviews":{"count":0,"score_avg":null,"cost_usd_avg":null,"success_rate":null,"latency_p50_ms":null,"narrative_summary":null,"summary_updated_at":null},"enrichmentModel":"deterministic:skill-github:v1","enrichmentVersion":1,"enrichedAt":"2026-04-22T06:51:59.827Z","embedding":null,"createdAt":"2026-04-18T21:46:04.353Z","updatedAt":"2026-04-22T06:51:59.827Z","lastSeenAt":"2026-04-22T06:51:59.827Z","tsv":"'/develop/best-practices':1166 '/develop/python/testing-suite':1161 '/temporalio/temporal/blob/main/docs/architecture/readme.md':1172 '/temporalio/temporal/blob/main/docs/development/testing.md':1177 '/workflows':1156 '1':277,1058,1087,1120,1180 '2':299,1066,1095,1126,1186 '2mb':659,954 '3':319,1072,1101,1132,1192 '4':1076,1107,1136,1198 '5':1080,1113,1140,1207 '80':1117 'access':511 'account':856 'achiev':1114 'across':837 'action':88 'activ':140,172,227,229,237,251,262,264,279,358,419,446,460,561,623,687,689,710,774,799,931,937,967,1075,1097,1130,1183,1197 'activity-specif':263 'activity.defn':232 'activityenviron':691 'activityerror':423 'agnost':904 'alway':355 'annot':618 'anti':342 'anti-pattern':341 'api':292,927 'appli':80 'applicationerror':367 'appropri':1078 'approv':863,1048 'architectur':1167,1169 'argument':661 'ask':1246 'async':236,278,294,297,346,350,480,944 'async/await':21,197,268 'asyncio':280 'attempt':410,454 'autom':872,1037 'automat':570,877 'backoff':401,408 'backward':587 'base':599,604 'batch':800,1024 'benefit':876 'best':41,82,1054,1162 'block':242,283,303,344,360,943 'blue':763 'blue-green':762 'boundari':1254 'build':126 'built':883 'built-in':882 'busi':381,842,1035 'calcul':337 'call':293,559,928,1185 'cancel':254 'cap':406 'capabl':155,532 'care':1139 'cart':855 'catch':422 'chang':735 'checklist':44 'child':219,1068 'ci/cd':972 'clarif':1248 'clarifi':74 'class':615,629 'classif':418 'cleanup':183 'clear':1221 'client':314 'close':443,451 'code':212,538,584,734,949 'coeffici':402 'common':906 'compat':588,736 'compens':432,851,1006,1044,1112 'complet':225 'comput':326 'concept':1153 'concurr':175,286,309,794,993 'configur':160,169,188,396,440,825,942,986,1077 'connect':184,796 'consist':515 'constraint':76 'container':747 'context':252,393,430 'continu':223,738 'convert':651 'coordin':725,1002 'core':1152 'cover':20 'coverag':1116 'cpu':248,324,808 'cpu-intens':247,323 'criteria':1257 'critic':340,1195 'cross':999 'cross-servic':998 'custom':377,649,785 'data':334,614,628,632,650,868,1015,1021 'databas':295,313 'datetime.now':544,911 'debug':521,895,1145 'decomposit':803 'decor':196,233 'default':648 'definit':193,230 'delay':385,389 'depend':1013 'deploy':26,146,177,743,745,749,765,977,1121 'depth':781 'describ':1225 'design':113,138,1057,1081 'detail':93,428 'detect':458 'determin':536,729,908,1187 'determinist':210,537,565,889 'develop':105,123 'differ':66 'direct':557,926 'distinguish':434 'distribut':18,118,153,788,835,1134 'docker':715 'docker/kubernetes':750 'docs.temporal.io':276,1155,1160,1165 'docs.temporal.io/develop/best-practices':1164 'docs.temporal.io/develop/python/testing-suite':1159 'docs.temporal.io/workflows':1154 'document':1148 'domain':67 'driven':1009 'durabl':13,111 'durat':447,455 'dynam':383,522 'emiss':787 'encrypt':655 'end':717,719 'end-to-end':716 'enforc':1051 'entiti':852 'entri':199 'environ':670,962,1237 'environment-specif':1236 'error':266,362,372,378,390,416,420,427,699,933,974,1026,1109 'escal':1053 'event':347,474,574,945,1008 'event-driven':1007 'eventu':411 'evolut':585 'exampl':94 'exceed':950 'execut':238,270,273,287,354,673,684,772,816,890 'expert':102,121,1242 'exponenti':407 'export':823 'extern':473,494,518,558,1184 'extract':426 'failur':412,438,1083 'fast':676,1093,1205 'feedback':1094,1206 'file':315 'focus':124,1061 'fulfil':1039 'full':706 'function':563 'generat':917 'generic':527 'github.com':1171,1176 'github.com/temporalio/temporal/blob/main/docs/architecture/readme.md':1170 'github.com/temporalio/temporal/blob/main/docs/development/testing.md':1175 'global':554,922 'goal':75 'grace':179,812,988,1124 'gradual':760 'green':764 'guarante':517,891 'guid':1158 'guidanc':40 'handl':255,267,363,421,609,888,990,1027 'handler':217,476,491,502,523,528,643 'health':778 'heartbeat':256,456,695 'heavi':336 'high':1115 'hint':612,622 'histori':575,733,822,1106 'horizont':751,898 'hour':844 'human':859 'human-in-the-loop':858 'i/o':243,284,304 'ideal':833 'idempot':488,702,936,1074,1193 'ignor':968 'implement':12,110,141,158,190,218,228,431,477,503,849,932,1073,1133 'inadequ':973 'infer':339 'infrastructur':871 'initi':164,397 'inject':688,700,975 'input':79,1251 'input/output':620 'inspect':500,606 'instant':672 'instead':542,547,912 'instruct':73 'insuffici':991 'integr':653,704,739,790,994 'intens':249,325 'interact':496 'interoper':905 'interv':399,405 'intervent':820 'introspect':531 'inventori':857 'isol':328,827 'issu':978 'json':646 'keep':1059 'key':875,1178 'lag':783 'languag':903 'language-agnost':902 'legaci':317 'librari':298,318 'limit':448,467,657,953,1213 'local':711 'lock':552 'logic':382,433,566,1030 'long':149,260,681,840 'long-run':148,259,839 'loop':348,862,946 'manag':308,534,658,854,1014 'mandatori':1189 'manual':818 'master':5,136 'match':1222 'maximum':403,409 'mechan':577 'memori':806 'messag':391 'metric':773,784,786,1131 'microservic':838,996 'mismatch':983 'miss':940,987,1259 'mistak':956 'ml':338 'mock':686,966,1096 'model':239,271,634 'monitor':519,768,779,1127,1208 'month':680 'month-long':679 'multi':723,866,1019,1046 'multi-parti':1045 'multi-stag':1018 'multi-step':865 'multi-workflow':722 'multipl':489 'multiprocess':321 'multithread':301 'mutabl':607 'namespac':824 'need':39,64 'next':387 'non':282,370,374,414,935 'non-block':281 'non-idempot':934 'non-retry':369,413 'number':916 'observ':770,1210 'offici':1147 'open':97 'oper':207,244,285,305,316,361,810 'optim':514,792,809 'orchestr':8,130,221,874,997,1182 'order':1038 'outcom':86 'output':1231 'outsid':70 'parallel':330,1023 'paramet':624 'parti':1047 'partit':756 'pattern':16,139,174,191,213,274,343,471,497,529,589,610,645,746,811,848,995,1004,1174 'payload':654,951 'payment':1041 'per':492,660 'perform':513,791 'perman':437 'permiss':1252 'persist':569,596,879 'pipelin':870,1017 'pitfal':907 'point':200 'polici':366 'pool':185,307,797 'practic':42,83,1055,1163 'preserv':394,573 'pro':4,35,48,62 'process':151,327,331,335,482,843,869,1016,1025,1036,1042,1049 'processpoolexecutor':245,322 'product':25,116,145,732,742,1105,1119,1212 'production-readi':115 'program':351 'progress':1031 'proper':166 'protobuf':652 'provid':87 'pure':562 'purpos':120,1065 'pydant':633 'python':3,10,34,47,61,108,134,156,616,832,1149 'python.temporal.io':1151 'queri':216,296,470,498,501,512,603,642,817,1143 'query-bas':602 'queu':465 'queue':168,755,780,985 'random':915 'random.random':549,919 'rate':776 'read':507 'read-on':506 'readi':117 'real':709 'recoveri':881,1085 'registr':173,526 'relev':81 'reliabl':127 'replay':576,727,740,897,969,1103 'report':257,1034 'requir':78,96,539,1250 'resourc':182,1146 'resources/implementation-playbook.md':98 'retri':187,365,384,388,398,404,885,939,1029 'retryabl':371,375,415 'retrypolici':395 'return':626 'review':1243 'rollout':761 'run':150,261,841 'runtim':524 'safe':205,583,639 'safeti':1253 'saga':15,847,1003 'scalabl':128,805,899,1071 'scale':752 'scenario':834,1110 'schedul':441,461 'scope':72,594,1224 'sdk':11,109,135,157,1150 'serial':353,644,647 'server':713 'servic':1000,1012 'setup':671 'shutdown':180,814,989,1125 'signal':214,468,472,475,481,485,490,598,640 'signal-bas':597 'signal/query':525 'simul':698 'singl':453,1064 'single-purpos':1063 'size':656,798,952 'skill':29,53,1216 'skill-temporal-python-pro' 'skip':668,961,1091,1203 'sla':1050 'snapshot':516 'sourc':275 'source-sickn33' 'special':106 'specif':265,1238 'stage':1020 'stall':459 'start':449,463 'startup':162 'state':499,510,533,555,568,572,600,605,608,878,923 'step':89,867 'stop':1244 'strategi':23,143,178,226,586,663,753,801 'structur':631 'substitut':1234 'success':1256 'success/failure':775 'sync':234,300,312,320,357,948 'system':119,131 'takeaway':1179 'target':1118 'task':36,56,167,250,754,984,1220 'tempor':2,6,33,46,60,103,122,712,831,1168 'temporal-python-pro':1 'test':22,142,662,665,669,677,690,694,701,705,726,728,741,955,963,970,976,1086,1100,1108,1157,1173,1199,1240 'thread':306,551,920 'threadpoolexecutor':240,302 'three':272 'time':206,466,667,893,960,1090,1202 'time-skip':666,959,1089,1201 'time-travel':892 'timeout':439,444,452,457,464,697,887,941,1079 'tool':69 'topic-agent-skills' 'topic-agentic-skills' 'topic-ai-agent-skills' 'topic-ai-agents' 'topic-ai-coding' 'topic-ai-workflows' 'topic-antigravity' 'topic-antigravity-skills' 'topic-claude-code' 'topic-claude-code-skills' 'topic-codex-cli' 'topic-codex-skills' 'total':445 'trace':789,1135 'track':1032 'transact':19,154,836,1001 'transform':1022 'transient':435 'travel':894 'treat':1229 'trigger':1011 'true':376 'tune':795 'turn':349 'type':379,417,611,617,621,627,638 'type-saf':637 'unit':693 'unregist':979 'unrel':58 'unsaf':938 'updat':601 'usag':368 'use':27,51,132,290,310,332,356,540,545,560,830,910,958,1067,1088,1141,1214 'valid':85,486,636,685,696,721,730,1102,1239 'variabl':591,595 'verif':91,703,737 'version':579,582,758,1137 'violat':909 'vs':235,436 'within':288,483 'without':965 'work':31 'worker':159,163,176,289,744,748,757,767,777,793,813,901,982,992,1122 'workflow':7,14,38,104,112,129,137,170,189,192,198,204,211,220,222,425,484,493,495,509,530,571,578,590,593,619,682,683,707,720,724,771,802,815,819,821,853,864,925,930,964,1010,1040,1056,1060,1069,1099,1128,1138,1142,1181,1191 'workflow-saf':203 'workflow-scop':592 'workflow.defn':195 'workflow.get':581 'workflow.now':209,541,914 'workflow.query':505 'workflow.random':546 'workflow.run':202 'workflow.signal':479 'workflow.sleep':675 'workflowenviron':664 'workflows/activities':980 'year':846","prices":[{"id":"8943f507-3d12-425d-a40e-94989e473424","listingId":"854404b3-4339-484f-bfbe-62633c44d4d9","amountUsd":"0","unit":"free","nativeCurrency":null,"nativeAmount":null,"chain":null,"payTo":null,"paymentMethod":"skill-free","isPrimary":true,"details":{"org":"sickn33","category":"antigravity-awesome-skills","install_from":"skills.sh"},"createdAt":"2026-04-18T21:46:04.353Z"}],"sources":[{"listingId":"854404b3-4339-484f-bfbe-62633c44d4d9","source":"github","sourceId":"sickn33/antigravity-awesome-skills/temporal-python-pro","sourceUrl":"https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/temporal-python-pro","isPrimary":false,"firstSeenAt":"2026-04-18T21:46:04.353Z","lastSeenAt":"2026-04-22T06:51:59.827Z"}],"details":{"listingId":"854404b3-4339-484f-bfbe-62633c44d4d9","quickStartSnippet":null,"exampleRequest":null,"exampleResponse":null,"schema":null,"openapiUrl":null,"agentsTxtUrl":null,"citations":[],"useCases":[],"bestFor":[],"notFor":[],"kindDetails":{"org":"sickn33","slug":"temporal-python-pro","github":{"repo":"sickn33/antigravity-awesome-skills","stars":34460,"topics":["agent-skills","agentic-skills","ai-agent-skills","ai-agents","ai-coding","ai-workflows","antigravity","antigravity-skills","claude-code","claude-code-skills","codex-cli","codex-skills","cursor","cursor-skills","developer-tools","gemini-cli","gemini-skills","kiro","mcp","skill-library"],"license":"mit","html_url":"https://github.com/sickn33/antigravity-awesome-skills","pushed_at":"2026-04-22T06:40:00Z","description":"Installable GitHub library of 1,400+ agentic skills for Claude Code, Cursor, Codex CLI, Gemini CLI, Antigravity, and more. Includes installer CLI, bundles, workflows, and official/community skill collections.","skill_md_sha":"95447461dda843aa79b77be7f6aa83def9cb9052","skill_md_path":"skills/temporal-python-pro/SKILL.md","default_branch":"main","skill_tree_url":"https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/temporal-python-pro"},"layout":"multi","source":"github","category":"antigravity-awesome-skills","frontmatter":{"name":"temporal-python-pro","description":"Master Temporal workflow orchestration with Python SDK. Implements durable workflows, saga patterns, and distributed transactions. Covers async/await, testing strategies, and production deployment."},"skills_sh_url":"https://skills.sh/sickn33/antigravity-awesome-skills/temporal-python-pro"},"updatedAt":"2026-04-22T06:51:59.827Z"}}