gtylee
9 verified skills
modelgas/skills/methodology
# Skill: Methodology ## Purpose Extract and formalize the model methodology as implemented in code, bridging the gap between mathematical intent, numerical implementation, and practical usage. This is the canonical description of “what the model actually does.” ## Inputs Required IR fields: - symbols (functions, classes) - code evidence snippets - commentary_md - imports and dependencies Skill data inputs: - sections.yaml (expected methodology sections and ordering) ## Outputs A complete me
development
modelgas/skills/prod_controls
# Skill: Production Control ## Purpose Define technical controls that ensure safe, observable, and auditable operation of the model in batch and service environments. This skill turns governance into runtime behavior. ## Inputs Required IR fields: - model interfaces - deployment assumptions - risk tier output Skill data inputs: - monitors.yaml (control patterns and snippets) ## Outputs - Logging and lineage requirements - Monitoring hooks (input/output, drift, failures) - Audit artifacts -
development
modelgas/skills/alw
# Skill: ALW (Assumptions, Limitations, Weaknesses) ## Purpose Surface and formalize the assumptions embedded in the model, the limitations of its design and scope, and the weaknesses that could lead to model failure or misuse. This skill makes implicit risk explicit. ## Inputs Required IR fields: - methodology outputs - code evidence snippets - commentary_md Skill data inputs: - alw_taxonomy.yaml (common assumption/limitation categories) ## Outputs Structured lists of: - Assumptions (model
development
modelgas/skills/documentation
# Skill: Documentation Assembly ## Purpose Assemble the final model documentation in a way that preserves the original intent of the model description while incorporating the validated findings from Skills 1–7. This skill is responsible for producing the human-facing narrative that will be read by reviewers, auditors, and downstream consumers, so it must be complete, internally consistent, and grounded in evidence. The goal is not to rewrite the model description from scratch, but to use it as
development
modelgas/skills/iteration
# Skill: Iteration (Human Responsibility + V2 Plan) ## Purpose Generate: - the Human Responsibility file (what must be explicitly declared/owned by humans), and - a practical V2 implementation plan (including deltas vs prior plans where present). ## Inputs Required inputs: - Skill outputs 1–8 (and 9 if re-running) - Unioned unknowns from prior skills - Templates: - `data/responsibility_template.md` - `data/planning_template.md` - Optional `human_declarations` (only if provided; do not inve
testing
modelgas/skills/opm
# Skill: OPM Tailoring ## Purpose Define ongoing performance monitoring (OPM) metrics and thresholds that are proportional to the model’s risk tier and usage. This skill operationalizes “model performance” in production. ## Inputs Required IR fields: - risk tier output - test outputs (especially metrics) - model usage characteristics Skill data inputs: - thresholds.yaml (default metrics and bands per tier) ## Outputs - Selected monitoring metrics - Thresholds (green/amber/red) - Breach defi
development
modelgas/skills/remediation_pack
# Skill: Remediation Pack ## Purpose Convert CodexGAS findings into implementable remediation artifacts (patches, tests, config/control updates) that close evidenced weaknesses and governance gaps. ## Inputs Required inputs: - Prior skill outputs (1–7 at minimum; include 8/9 if present) - remediation rules (`data/remediation_rules.yaml`) - Optional `human_declarations` (only if provided; do not invent) - IR evidence index (for evidence ids) ## Outputs Produce a remediation pack containing: -
development
modelgas/skills/risk_tiering
# Skill: Risk Tiering ## Purpose Determine the governance risk tier of the model by assessing its financial impact, operational reliance, usage pattern, implementation complexity, and strength of existing risk mitigations. This skill establishes the downstream control requirements for all other skills. ## Inputs Required IR fields: - project metadata - symbols and public interfaces - imports and dependencies - commentary_md - evidence_index Skill data inputs: - rubric.yaml (axis definitions,
development
modelgas/skills/tests
# Skill: Test Writing ## Purpose Design and generate a validation test suite that assesses conceptual soundness, implementation correctness, numerical stability, and outcome reasonableness. This skill converts model risk into executable tests. ## Inputs Required IR fields: - methodology outputs - ALW outputs - code evidence snippets Skill data inputs: - test_matrix.yaml (required test categories and patterns) ## Outputs - A test plan matrix (test name, purpose, category) - Generated pytest
development