[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MB5KRE5WD10CS4E17D6FR9":3,"skill-same:01M6MB5KRE5WD10CS4E17D6FR9":144},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":61,"ownerHtmlUrl":62,"ownerBlogUrl":63,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6MB5KRE5WD10CS4E17D6FR9","simpy",null,"Builds, tests, and analyzes bounded process-based discrete-event simulations in Python with SimPy 4.1.2: Environment, Timeout, Process, AnyOf\u002FAllOf conditions, interrupts, Resource, PriorityResource, PreemptiveResource, Container, Store, time-weighted monitoring, and replication-based output analysis, plus bundled safe CLIs. Use when modeling queues, production lines, logistics, or service operations with generator processes; when contending entities need shared resources or preemption; when instrumenting queue length or utilization correctly; when running independent replications with seeds and confidence intervals; when debugging event ordering or run(until=) boundaries. Not for continuous-time ODE or agent-based modeling frameworks.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1571,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"edit","when","operate","data-science","resources","confidence intervals","shared","local","time","uv","only","safe","write","read","Bash","simulation","analysis","monitoring","run","debugging","2026-11-10 10:12:30",false,"---\nname: simpy\ndescription: 'Builds, tests, and analyzes bounded process-based discrete-event simulations in Python with SimPy 4.1.2: Environment, Timeout, Process, AnyOf\u002FAllOf conditions, interrupts, Resource, PriorityResource, PreemptiveResource, Container, Store, time-weighted monitoring, and replication-based output analysis, plus bundled safe CLIs. Use when modeling queues, production lines, logistics, or service operations with generator processes; when contending entities need shared resources or preemption; when instrumenting queue length or utilization correctly; when running independent replications with seeds and confidence intervals; when debugging event ordering or run(until=) boundaries. Not for continuous-time ODE or agent-based modeling frameworks.'\nlicense: MIT\ncompatibility: Upstream SimPy 4.1.2 supports Python 3.8+; bundled CLIs require Python 3.10+, uv, and SimPy 4.1.2. They use only SimPy and the standard library, operate on local bounded inputs, and make no network calls.\nallowed-tools: Read Write Edit Bash Glob\nmetadata:\n  version: '1.4'\n  category: data-science-and-ml\n  maintainer: Kalaris Labs\n  tags: discrete-event simulation, simulation, queueing, operations research\n---\n\n# SimPy\n\n## Scope\n\nUse this skill for process-based discrete-event models where active entities yield\nevents and contend for resources: queues, production systems, logistics, networks,\nservice operations, inventory, and other event-driven systems.\n\nSimPy supplies an event scheduler and modeling primitives. It does **not** choose a\nscientifically valid conceptual model, input distribution, warm-up, run length,\nreplication count, estimand, or causal interpretation. Treat those as simulation-study\nmethodology, not SimPy API behavior.\n\n## Current release and installation\n\nVerified **2026-07-23**:\n\n- Latest stable: **SimPy 4.1.2**, released on PyPI 2026-05-24; source tag\n  `4.1.2` points to commit `f4381649`.\n- Package metadata requires Python **>=3.8** and classifies CPython 3.8-3.14\n  plus PyPy. SimPy has no runtime dependencies.\n- 4.1.2 adds Python 3.13\u002F3.14 support and modern-interpreter test fixes.\n- Upstream and this skill are MIT-licensed.\n\nCreate a reproducible environment:\n\n```bash\nuv venv --python 3.13\nsource .venv\u002Fbin\u002Factivate\nuv pip install \"simpy==4.1.2\"\npython -c \"import importlib.metadata; print(importlib.metadata.version('simpy'))\"\n```\n\nDo not silently substitute the `latest` documentation build: it may describe an\nunreleased development revision. Use the versioned 4.1.2 links in\n`references\u002Fsources.md`.\n\n## Model workflow\n\n1. **Define purpose and estimands.** State the decision\u002Fquestion, system boundary,\n   entities, resources, state, outputs, time units, and terminating event or\n   steady-state target.\n2. **Write a conceptual model first.** Record assumptions, distributions,\n   routing, priorities, initial conditions, and omitted mechanisms.\n3. **Implement generators.** A SimPy process is an event-yielding Python generator.\n   Register the generator object with `env.process(...)`.\n4. **Bound execution.** Give every production run explicit time, entity, event, and\n   replication caps. Never call `env.run()` on a model containing an endless process.\n5. **Separate random streams.** Use local RNG instances for logically distinct\n   stochastic sources; retain a seed manifest.\n6. **Instrument deliberately.** Observe state after the transition of interest,\n   close time-weighted intervals at the horizon, and test that monitoring does not\n   alter event order.\n7. **Verify and validate.** Test deterministic edge cases, conservation identities,\n   traces, queue discipline, and analytical benchmarks; compare against system or\n   expert evidence for the stated purpose.\n8. **Run independent replications.** Make intervals from replication-level\n   estimates, not correlated entities within one run.\n9. **Report limitations.** Include initialization, unfinished entities, run length,\n   seeds\u002Fstreams, precision, sensitivity, and validation evidence. Never convert\n   simulation association into a causal claim.\n\nRead `references\u002Fsimulation-methodology.md` before making inferential claims.\n\n## Minimal bounded model\n\n```python\nimport random\nimport simpy\n\nHORIZON = 480.0\narrival_rng = random.Random(101)\nservice_rng = random.Random(202)\nenv = simpy.Environment()\nserver = simpy.Resource(env, capacity=2)\ncompleted = []\n\ndef customer(arrival):\n    with server.request() as request:\n        yield request\n        wait = env.now - arrival\n        yield env.timeout(service_rng.expovariate(1 \u002F 6.0))\n    completed.append((env.now, wait))\n\ndef arrivals():\n    for _ in range(10_000):  # Entity cap.\n        delay = arrival_rng.expovariate(1 \u002F 4.0)\n        if env.now + delay >= HORIZON:\n            return\n        yield env.timeout(delay)\n        env.process(customer(env.now))\n\nenv.process(arrivals())\nenv.run(until=HORIZON)\n```\n\nThe numeric horizon is half-open: normal events scheduled exactly at `480.0` are\nnot processed. Report unfinished entities rather than silently treating them as\ncompleted observations.\n\n## Core semantics\n\n### Environment and deterministic ordering\n\n`Environment` is single-threaded. The queue is ordered by simulation time, event\npriority, then a strictly increasing event ID. Same-time, same-priority events are\ntherefore processed FIFO in scheduling order. Model processes may represent\nconcurrency, but callbacks execute sequentially and deterministically.\n\n- `env.now`: unitless simulation clock; choose and document one unit.\n- `env.peek()`: next event time or infinity.\n- `env.step()`: process one event; raises `EmptySchedule` when empty.\n- `env.active_process`: currently executing process, otherwise `None`.\n- `env.run()`: drain the queue; unsafe with recurring or endless processes.\n\n`env.run(until=number)` and `env.run(until=event)` are not interchangeable at\nboundaries:\n\n- A numeric value schedules an urgent stop event and excludes ordinary events at\n  that exact time.\n- An Event criterion returns that event's value when its stop callback fires.\n  Other same-time ordering depends on priority and scheduling order.\n- In 4.1.2, `Environment.step()` preserves callbacks remaining after\n  `StopSimulation` by rescheduling the target. Consequently, after\n  `env.run(until=target)`, `target.processed` can remain `False` until one more\n  `step()`\u002F`run()` even though its value was returned. Do not use `processed` as the\n  sole post-run completion test.\n\nSee `references\u002Fevents.md` and `references\u002Fmonitoring.md`.\n\n### Event, Timeout, Process, and Condition\n\n- An `Event` moves once through not-triggered -> triggered\u002Fscheduled -> processed.\n  `succeed(value)` or `fail(exception)` triggers it once.\n- A `Timeout` triggers when created, is scheduled for `now + delay`, and cannot be\n  manually succeeded again.\n- `env.process(generator)` creates a `Process`; the generator resumes with the\n  yielded event value. Returning from the generator succeeds the Process with that\n  return value. Uncaught exceptions fail it.\n- `AnyOf` \u002F `a | b` and `AllOf` \u002F `a & b` yield a `ConditionValue`: an ordered,\n  dict-like mapping from **event objects** to their values. Test membership using\n  the original event objects; do not assume a scalar result.\n- `AnyOf` does not cancel losing events. Explicitly cancel pending resource\n  requests when abandoning them; ordinary timeouts remain scheduled.\n\n### Interrupts\n\n`process.interrupt(cause)` schedules an urgent interruption that throws\n`simpy.Interrupt` into the target generator. Catch it around the yielded work that\nmay be interrupted, inspect `interrupt.cause`, update remaining work, then either\nresume, re-yield the original event, or terminate.\n\nInterrupting a process removes its resume callback from its current target; it does\nnot cancel that target event. A process cannot interrupt itself or a terminated\nprocess. See `references\u002Fprocess-interaction.md`.\n\n## Shared resources\n\n| Type | Semantics |\n|---|---|\n| `Resource` | FIFO semaphore-like usage slots |\n| `PriorityResource` | Queued requests sorted by lower numeric priority first |\n| `PreemptiveResource` | Priority queue plus optional preemption of a current user |\n| `Container` | Homogeneous numeric level; `put`\u002F`get` wait for capacity\u002Fmaterial |\n| `Store` | FIFO Python objects |\n| `FilterStore` | First available item satisfying the request's predicate |\n| `PriorityStore` | Comparable items returned in priority order |\n\nUse a request context manager:\n\n```python\ndef job(env, resource):\n    with resource.request() as request:\n        yield request\n        yield env.timeout(3)\n```\n\nOn exit it releases an acquired request or cancels a still-pending one, including\nduring exception unwinding. For a manually retained pending `put`\u002F`get`\u002Frequest,\ncall `cancel()` if an interrupt or timeout makes the process abandon it.\n\n`PreemptiveResource.request(priority=..., preempt=True)` uses lower numbers as\nhigher priority. The preempted process receives an `Interrupt` whose cause is a\n`Preempted` object: `cause.by` is the preempting Process,\n`cause.usage_since` is when use began, and `cause.resource` is the resource.\nQueued priority takes precedence over the `preempt` flag; mixing preempting and\nnon-preempting requests needs explicit tests.\n\nRead `references\u002Fresources.md` for blocked operations, queue rules, and examples.\n\n## Monitoring and stepping\n\nPrefer explicit domain observations at state transitions. For generic resource\nmonitoring, wrappers or subclasses can inspect `count`, `queue`, `level`, `items`,\n`put_queue`, and `get_queue`. For event tracing, `schedule()` and `step()` are the\ncentral hooks.\n\nQueue measurements are timing-sensitive:\n\n- A request method's pre-state, post-call state, grant callback, and release\n  callback can all differ at the same simulation timestamp.\n- Sample averages weight event observations, not time. Compute area under the\n  left-continuous state path and divide by elapsed time.\n- Add initial and final samples; close the last interval at the analysis horizon.\n- `env._queue`, resource `_env`, and monkey-patching are implementation details.\n  Pin SimPy, isolate the instrumentation, and regression-test after upgrades.\n- Tracing every event changes runtime and memory use; cap trace records.\n\nUse `scripts\u002Fresource_monitor.py` and `references\u002Fmonitoring.md`.\n\n## Real-time execution\n\n`simpy.rt.RealtimeEnvironment(initial_time=0, factor=1.0, strict=True)` maps one\nsimulation unit to `factor` wall-clock seconds. In strict mode, `step()`\u002F`run()`\nraises `RuntimeError` when computation falls behind. `strict=False` tolerates lag;\nit does not restore timing accuracy. Develop logic with `Environment`, then run\nseparate timing tests with generous platform-aware tolerances. See\n`references\u002Freal-time.md`.\n\n## Bundled safe CLIs\n\nAll CLIs use a fixed built-in queue model or summarize local artifacts. They reject\nunknown JSON keys, URLs, symlinks, non-finite numbers, oversized inputs, and\nunbounded time\u002Fevents\u002Fentities\u002Freplications. They never evaluate config text,\nexecute user Python, import plugins, or call a network service.\n\n```bash\n# Inspect all options.\npython skills\u002Fsimpy\u002Fscripts\u002Fbounded_queue_scenario.py --help\npython skills\u002Fsimpy\u002Fscripts\u002Freplication_runner.py --help\npython skills\u002Fsimpy\u002Fscripts\u002Fevent_trace_summary.py --help\npython skills\u002Fsimpy\u002Fscripts\u002Fvalidate_simulation_config.py --help\n\n# Deterministic built-in scenario.\npython skills\u002Fsimpy\u002Fscripts\u002Fbounded_queue_scenario.py\n\n# Independent replications with replication-level Student-t intervals.\npython skills\u002Fsimpy\u002Fscripts\u002Freplication_runner.py\n\n# Validate only; no simulation runs.\npython skills\u002Fsimpy\u002Fscripts\u002Fvalidate_simulation_config.py config.json\n```\n\nThe replication runner refuses one-replication intervals. Its intervals quantify\nMonte Carlo uncertainty under the configured model; they neither validate the model\nnor identify causal effects. See `references\u002Fcli-guide.md`.\n\n## Testing\n\nUse deterministic unit tests for ordering, boundary times, conditions, interrupts,\nall resource disciplines, conservation, event\u002Fentity limits, seed reproducibility,\nand monitor non-interference. Add stochastic tests only as broad distributional\nchecks with fixed seeds; avoid brittle exact sample estimates.\n\nRun the skill's suite in the exact pinned environment without bytecode artifacts:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 uv run --isolated --no-project \\\n  --python 3.13 --with \"simpy==4.1.2\" \\\n  python -m unittest discover -s tests\u002Fsimpy -v\n```\n\n## References\n\n- `references\u002Fevents.md` — scheduler, lifecycle, run boundaries, conditions\n- `references\u002Fprocess-interaction.md` — generators, shared events, interrupts\n- `references\u002Fresources.md` — all Resource, Container, and Store variants\n- `references\u002Fmonitoring.md` — time weighting, queue timing, tracing, stepping\n- `references\u002Freal-time.md` — factor, strict mode, drift, timing tests\n- `references\u002Fsimulation-methodology.md` — replications, warm-up, validation, CI\n- `references\u002Fcli-guide.md` — schemas, bounds, outputs, and safe CLI examples\n- `references\u002Fsources.md` — dated official and primary-method sources\n","# SimPy\n\n## Scope\n\nUse this skill for process-based discrete-event models where active entities yield\nevents and contend for resources: queues, production systems, logistics, networks,\nservice operations, inventory, and other event-driven systems.\n\nSimPy supplies an event scheduler and modeling primitives. It does **not** choose a\nscientifically valid conceptual model, input distribution, warm-up, run length,\nreplication count, estimand, or causal interpretation. Treat those as simulation-study\nmethodology, not SimPy API behavior.\n\n## Current release and installation\n\nVerified **2026-07-23**:\n\n- Latest stable: **SimPy 4.1.2**, released on PyPI 2026-05-24; source tag\n  `4.1.2` points to commit `f4381649`.\n- Package metadata requires Python **>=3.8** and classifies CPython 3.8-3.14\n  plus PyPy. SimPy has no runtime dependencies.\n- 4.1.2 adds Python 3.13\u002F3.14 support and modern-interpreter test fixes.\n- Upstream and this skill are MIT-licensed.\n\nCreate a reproducible environment:\n\n```bash\nuv venv --python 3.13\nsource .venv\u002Fbin\u002Factivate\nuv pip install \"simpy==4.1.2\"\npython -c \"import importlib.metadata; print(importlib.metadata.version('simpy'))\"\n```\n\nDo not silently substitute the `latest` documentation build: it may describe an\nunreleased development revision. Use the versioned 4.1.2 links in\n`references\u002Fsources.md`.\n\n## Model workflow\n\n1. **Define purpose and estimands.** State the decision\u002Fquestion, system boundary,\n   entities, resources, state, outputs, time units, and terminating event or\n   steady-state target.\n2. **Write a conceptual model first.** Record assumptions, distributions,\n   routing, priorities, initial conditions, and omitted mechanisms.\n3. **Implement generators.** A SimPy process is an event-yielding Python generator.\n   Register the generator object with `env.process(...)`.\n4. **Bound execution.** Give every production run explicit time, entity, event, and\n   replication caps. Never call `env.run()` on a model containing an endless process.\n5. **Separate random streams.** Use local RNG instances for logically distinct\n   stochastic sources; retain a seed manifest.\n6. **Instrument deliberately.** Observe state after the transition of interest,\n   close time-weighted intervals at the horizon, and test that monitoring does not\n   alter event order.\n7. **Verify and validate.** Test deterministic edge cases, conservation identities,\n   traces, queue discipline, and analytical benchmarks; compare against system or\n   expert evidence for the stated purpose.\n8. **Run independent replications.** Make intervals from replication-level\n   estimates, not correlated entities within one run.\n9. **Report limitations.** Include initialization, unfinished entities, run length,\n   seeds\u002Fstreams, precision, sensitivity, and validation evidence. Never convert\n   simulation association into a causal claim.\n\nRead `references\u002Fsimulation-methodology.md` before making inferential claims.\n\n## Minimal bounded model\n\n```python\nimport random\nimport simpy\n\nHORIZON = 480.0\narrival_rng = random.Random(101)\nservice_rng = random.Random(202)\nenv = simpy.Environment()\nserver = simpy.Resource(env, capacity=2)\ncompleted = []\n\ndef customer(arrival):\n    with server.request() as request:\n        yield request\n        wait = env.now - arrival\n        yield env.timeout(service_rng.expovariate(1 \u002F 6.0))\n    completed.append((env.now, wait))\n\ndef arrivals():\n    for _ in range(10_000):  # Entity cap.\n        delay = arrival_rng.expovariate(1 \u002F 4.0)\n        if env.now + delay >= HORIZON:\n            return\n        yield env.timeout(delay)\n        env.process(customer(env.now))\n\nenv.process(arrivals())\nenv.run(until=HORIZON)\n```\n\nThe numeric horizon is half-open: normal events scheduled exactly at `480.0` are\nnot processed. Report unfinished entities rather than silently treating them as\ncompleted observations.\n\n## Core semantics\n\n### Environment and deterministic ordering\n\n`Environment` is single-threaded. The queue is ordered by simulation time, event\npriority, then a strictly increasing event ID. Same-time, same-priority events are\ntherefore processed FIFO in scheduling order. Model processes may represent\nconcurrency, but callbacks execute sequentially and deterministically.\n\n- `env.now`: unitless simulation clock; choose and document one unit.\n- `env.peek()`: next event time or infinity.\n- `env.step()`: process one event; raises `EmptySchedule` when empty.\n- `env.active_process`: currently executing process, otherwise `None`.\n- `env.run()`: drain the queue; unsafe with recurring or endless processes.\n\n`env.run(until=number)` and `env.run(until=event)` are not interchangeable at\nboundaries:\n\n- A numeric value schedules an urgent stop event and excludes ordinary events at\n  that exact time.\n- An Event criterion returns that event's value when its stop callback fires.\n  Other same-time ordering depends on priority and scheduling order.\n- In 4.1.2, `Environment.step()` preserves callbacks remaining after\n  `StopSimulation` by rescheduling the target. Consequently, after\n  `env.run(until=target)`, `target.processed` can remain `False` until one more\n  `step()`\u002F`run()` even though its value was returned. Do not use `processed` as the\n  sole post-run completion test.\n\nSee `references\u002Fevents.md` and `references\u002Fmonitoring.md`.\n\n### Event, Timeout, Process, and Condition\n\n- An `Event` moves once through not-triggered -> triggered\u002Fscheduled -> processed.\n  `succeed(value)` or `fail(exception)` triggers it once.\n- A `Timeout` triggers when created, is scheduled for `now + delay`, and cannot be\n  manually succeeded again.\n- `env.process(generator)` creates a `Process`; the generator resumes with the\n  yielded event value. Returning from the generator succeeds the Process with that\n  return value. Uncaught exceptions fail it.\n- `AnyOf` \u002F `a | b` and `AllOf` \u002F `a & b` yield a `ConditionValue`: an ordered,\n  dict-like mapping from **event objects** to their values. Test membership using\n  the original event objects; do not assume a scalar result.\n- `AnyOf` does not cancel losing events. Explicitly cancel pending resource\n  requests when abandoning them; ordinary timeouts remain scheduled.\n\n### Interrupts\n\n`process.interrupt(cause)` schedules an urgent interruption that throws\n`simpy.Interrupt` into the target generator. Catch it around the yielded work that\nmay be interrupted, inspect `interrupt.cause`, update remaining work, then either\nresume, re-yield the original event, or terminate.\n\nInterrupting a process removes its resume callback from its current target; it does\nnot cancel that target event. A process cannot interrupt itself or a terminated\nprocess. See `references\u002Fprocess-interaction.md`.\n\n## Shared resources\n\n| Type | Semantics |\n|---|---|\n| `Resource` | FIFO semaphore-like usage slots |\n| `PriorityResource` | Queued requests sorted by lower numeric priority first |\n| `PreemptiveResource` | Priority queue plus optional preemption of a current user |\n| `Container` | Homogeneous numeric level; `put`\u002F`get` wait for capacity\u002Fmaterial |\n| `Store` | FIFO Python objects |\n| `FilterStore` | First available item satisfying the request's predicate |\n| `PriorityStore` | Comparable items returned in priority order |\n\nUse a request context manager:\n\n```python\ndef job(env, resource):\n    with resource.request() as request:\n        yield request\n        yield env.timeout(3)\n```\n\nOn exit it releases an acquired request or cancels a still-pending one, including\nduring exception unwinding. For a manually retained pending `put`\u002F`get`\u002Frequest,\ncall `cancel()` if an interrupt or timeout makes the process abandon it.\n\n`PreemptiveResource.request(priority=..., preempt=True)` uses lower numbers as\nhigher priority. The preempted process receives an `Interrupt` whose cause is a\n`Preempted` object: `cause.by` is the preempting Process,\n`cause.usage_since` is when use began, and `cause.resource` is the resource.\nQueued priority takes precedence over the `preempt` flag; mixing preempting and\nnon-preempting requests needs explicit tests.\n\nRead `references\u002Fresources.md` for blocked operations, queue rules, and examples.\n\n## Monitoring and stepping\n\nPrefer explicit domain observations at state transitions. For generic resource\nmonitoring, wrappers or subclasses can inspect `count`, `queue`, `level`, `items`,\n`put_queue`, and `get_queue`. For event tracing, `schedule()` and `step()` are the\ncentral hooks.\n\nQueue measurements are timing-sensitive:\n\n- A request method's pre-state, post-call state, grant callback, and release\n  callback can all differ at the same simulation timestamp.\n- Sample averages weight event observations, not time. Compute area under the\n  left-continuous state path and divide by elapsed time.\n- Add initial and final samples; close the last interval at the analysis horizon.\n- `env._queue`, resource `_env`, and monkey-patching are implementation details.\n  Pin SimPy, isolate the instrumentation, and regression-test after upgrades.\n- Tracing every event changes runtime and memory use; cap trace records.\n\nUse `scripts\u002Fresource_monitor.py` and `references\u002Fmonitoring.md`.\n\n## Real-time execution\n\n`simpy.rt.RealtimeEnvironment(initial_time=0, factor=1.0, strict=True)` maps one\nsimulation unit to `factor` wall-clock seconds. In strict mode, `step()`\u002F`run()`\nraises `RuntimeError` when computation falls behind. `strict=False` tolerates lag;\nit does not restore timing accuracy. Develop logic with `Environment`, then run\nseparate timing tests with generous platform-aware tolerances. See\n`references\u002Freal-time.md`.\n\n## Bundled safe CLIs\n\nAll CLIs use a fixed built-in queue model or summarize local artifacts. They reject\nunknown JSON keys, URLs, symlinks, non-finite numbers, oversized inputs, and\nunbounded time\u002Fevents\u002Fentities\u002Freplications. They never evaluate config text,\nexecute user Python, import plugins, or call a network service.\n\n```bash\n# Inspect all options.\npython skills\u002Fsimpy\u002Fscripts\u002Fbounded_queue_scenario.py --help\npython skills\u002Fsimpy\u002Fscripts\u002Freplication_runner.py --help\npython skills\u002Fsimpy\u002Fscripts\u002Fevent_trace_summary.py --help\npython skills\u002Fsimpy\u002Fscripts\u002Fvalidate_simulation_config.py --help\n\n# Deterministic built-in scenario.\npython skills\u002Fsimpy\u002Fscripts\u002Fbounded_queue_scenario.py\n\n# Independent replications with replication-level Student-t intervals.\npython skills\u002Fsimpy\u002Fscripts\u002Freplication_runner.py\n\n# Validate only; no simulation runs.\npython skills\u002Fsimpy\u002Fscripts\u002Fvalidate_simulation_config.py config.json\n```\n\nThe replication runner refuses one-replication intervals. Its intervals quantify\nMonte Carlo uncertainty under the configured model; they neither validate the model\nnor identify causal effects. See `references\u002Fcli-guide.md`.\n\n## Testing\n\nUse deterministic unit tests for ordering, boundary times, conditions, interrupts,\nall resource disciplines, conservation, event\u002Fentity limits, seed reproducibility,\nand monitor non-interference. Add stochastic tests only as broad distributional\nchecks with fixed seeds; avoid brittle exact sample estimates.\n\nRun the skill's suite in the exact pinned environment without bytecode artifacts:\n\n```bash\nPYTHONDONTWRITEBYTECODE=1 uv run --isolated --no-project \\\n  --python 3.13 --with \"simpy==4.1.2\" \\\n  python -m unittest discover -s tests\u002Fsimpy -v\n```\n\n## References\n\n- `references\u002Fevents.md` — scheduler, lifecycle, run boundaries, conditions\n- `references\u002Fprocess-interaction.md` — generators, shared events, interrupts\n- `references\u002Fresources.md` — all Resource, Container, and Store variants\n- `references\u002Fmonitoring.md` — time weighting, queue timing, tracing, stepping\n- `references\u002Freal-time.md` — factor, strict mode, drift, timing tests\n- `references\u002Fsimulation-methodology.md` — replications, warm-up, validation, CI\n- `references\u002Fcli-guide.md` — schemas, bounds, outputs, and safe CLI examples\n- `references\u002Fsources.md` — dated official and primary-method sources\n",{"name":5,"license":15,"metadata":49,"description":7,"allowed-tools":54,"compatibility":55},{"tags":50,"version":51,"category":52,"maintainer":53},"discrete-event simulation, simulation, queueing, operations research","1.4","data-science-and-ml","Kalaris Labs","Read Write Edit Bash Glob","Upstream SimPy 4.1.2 supports Python 3.8+; bundled CLIs require Python 3.10+, uv, and SimPy 4.1.2. They use only SimPy and the standard library, operate on local bounded inputs, and make no network calls.","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy",490,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-14 02:12:02",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill simpy",{"tool":72,"command":73},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill simpy",{"tool":75,"command":76},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill simpy","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002FSKILL.md",true,[80,86,90,94,98,102,106,110,114,120,124,128,132,136,140],{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fcli-guide.md","REFERENCE","参考文档",7882,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Freferences\u002Fcli-guide.md",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fevents.md",7294,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Freferences\u002Fevents.md",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fmonitoring.md",8344,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Freferences\u002Fmonitoring.md",{"path":95,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Fprocess-interaction.md",7703,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Freferences\u002Fprocess-interaction.md",{"path":99,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":100,"sourceHtmlUrl":101},"references\u002Freal-time.md",5159,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Freferences\u002Freal-time.md",{"path":103,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fresources.md",8489,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Freferences\u002Fresources.md",{"path":107,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":108,"sourceHtmlUrl":109},"references\u002Fsimulation-methodology.md",11724,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Freferences\u002Fsimulation-methodology.md",{"path":111,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":112,"sourceHtmlUrl":113},"references\u002Fsources.md",9785,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Freferences\u002Fsources.md",{"path":115,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":118,"sourceHtmlUrl":119},"scripts\u002F_common.py","SCRIPT","脚本",15134,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Fscripts\u002F_common.py",{"path":121,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":122,"sourceHtmlUrl":123},"scripts\u002Fbasic_simulation_template.py",13197,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Fscripts\u002Fbasic_simulation_template.py",{"path":125,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":126,"sourceHtmlUrl":127},"scripts\u002Fbounded_queue_scenario.py",3609,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Fscripts\u002Fbounded_queue_scenario.py",{"path":129,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":130,"sourceHtmlUrl":131},"scripts\u002Fevent_trace_summary.py",10769,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Fscripts\u002Fevent_trace_summary.py",{"path":133,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":134,"sourceHtmlUrl":135},"scripts\u002Freplication_runner.py",6234,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Fscripts\u002Freplication_runner.py",{"path":137,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":138,"sourceHtmlUrl":139},"scripts\u002Fresource_monitor.py",15945,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Fscripts\u002Fresource_monitor.py",{"path":141,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":142,"sourceHtmlUrl":143},"scripts\u002Fvalidate_simulation_config.py",3679,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fsimpy\u002Fscripts\u002Fvalidate_simulation_config.py",[145,154,161,168,193,218],{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":149,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"publishedAt":153},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":149,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":160,"publishedAt":153},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":149,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":167,"publishedAt":153},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":149,"licenseFileUrl":6,"sourceHtmlUrl":172,"categories":173,"keywords":174,"publishedAt":44},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[175,176,177,178,179,180,181,182,183,184,43,25,185,186,187,188,189,190,191,192],"search","deja search","never","error","ready","memory","re","decide","fix","design","you","done","reviewer","io","change","cli","merge","past",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":149,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":199,"publishedAt":44},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[200,201,202,188,203,204,205,206,207,208,209,210,211,212,180,213,214,215,216,217],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":149,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":231,"publishedAt":44},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[224,227,228],{"categoryNo":225,"categoryName":226,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":229,"categoryName":230,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[220,232,233,234,235,236,237,214,238,181,239,240,241,242,25,243,244,245,246,247],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]