---

name: nupic-legacy description: "Use NuPIC legacy HTM algorithms, OPF prediction, Network API pipelines, swarming search definitions, and data/config validation in Python 2.7-era runtimes." disable-model-invocation: true metadata: disco-role: operating license: MIT

NuPIC Legacy

Use this repo skill when a task involves NuPIC legacy, Hierarchical Temporal Memory (HTM), streaming anomaly/prediction workflows, SpatialPooler, TemporalMemory, SDRClassifier, OPF ModelFactory, Network API regions, run_swarm, or NuPIC three-header-row CSV/model-parameter files.

NuPIC legacy is maintenance-mode, Python 2.7-era software. Start by checking the runtime before debugging workflow code.

Start here

1. Read references/repo-provenance.md when checking whether this skill matches a checkout or deciding whether to refresh it.
2. Read references/installation-and-runtime.md before installing or preparing a NuPIC legacy environment.
3. Run scripts/check_nupic_legacy_env.py inside the candidate Python environment to verify imports and tiny API construction.
4. Use references/troubleshooting.md when failures mention Python version, nupic, nupic.bindings, numpy, capnp/pycapnp, dependency conflicts, or broad runtime setup.

Minimal package smoke command from this skill root:

python scripts/check_nupic_legacy_env.py

Expected runtime for execution tasks: Python 2.7, installed nupic, compiled nupic.bindings, legacy-compatible NumPy, and pycapnp/Cap'n Proto where serialization is involved. Do not treat Python 3 import or syntax failures as surprising unless the user is explicitly porting NuPIC.

Route by task

User task or signal	Go to	Why
Direct encoders, SpatialPooler, TemporalMemory, SDRClassifier, anomaly score, anomaly likelihood, active columns/cells	sub-skills/htm-algorithms/	Covers direct HTM algorithm APIs, signatures, array shapes, and tiny smoke checks.
CSV stream headers, FileRecordStream, field metadata, model parameter YAML/JSON, aggregation, configuration overrides	sub-skills/data-and-configuration/	Validates and explains input/config artifacts shared by OPF, Network, and swarming.
OPF ModelFactory, HTMPredictionModel, model.run, result.inferences, experiment directories, checkpoints	sub-skills/opf-prediction/	Covers high-level prediction workflows, inference keys, experiment runner guidance, and checkpoint use.
Network, RecordSensor, SPRegion, TMRegion, classifier regions, region links, custom PyRegion	sub-skills/network-api/	Covers Network API construction, linking, region specs, output extraction, and custom-region skeletons.
run_swarm, permutations_runner, search_def.json, generated model_0, MySQL/ClientJobs errors	sub-skills/swarming/	Covers safe search-definition linting, CLI/action options, service requirements, and using generated OPF models.

Common workflows

Direct HTM algorithm pipeline

Use htm-algorithms when the user wants to encode values, run SP/TM, classify active cells, or compute anomaly likelihood without OPF. The bundled smoke is:

python sub-skills/htm-algorithms/scripts/algorithm_smoke.py --mode all --records 20

OPF prediction over a CSV stream

Use data-and-configuration first to validate the NuPIC CSV and model parameters, then opf-prediction to create the model and extract multiStepBestPredictions / multiStepPredictions. Useful checks:

python sub-skills/data-and-configuration/scripts/validate_nupic_csv.py data.csv --predicted-field consumption
python sub-skills/opf-prediction/scripts/opf_prediction_smoke.py

Network API graph

Use network-api when the user asks for explicit regions and links rather than OPF. Start with:

python sub-skills/network-api/scripts/network_smoke.py --inspect-region-types

Swarming/search-definition workflow

Use swarming for search_def.json authoring and hypersearch run planning. Lint before running anything service-backed:

python sub-skills/swarming/scripts/swarm_config_lint.py search_def.json --summary

Full swarming can require MySQL-compatible service configuration and may be expensive; never use it as a first install smoke.

Boundaries

• This skill teaches using NuPIC legacy as a package. For source-code edits, release engineering, or Python 3 porting, treat the task as repository maintenance and do not use package workflow assumptions blindly.
• This skill does not cover modern deep-learning time-series forecasting unless the task explicitly uses NuPIC/HTM APIs.
• Do not install developer requirements, visualization extras, Docker/Vagrant tooling, profiling scripts, or full benchmark suites unless the user asks for those surfaces.
• Runtime instructions here are self-contained; bundled references/scripts replace the relevant example logic instead of requiring future agents to read or run original checkout files.