Senior Python engineering

Make Python changes and judgments that remain correct, understandable, and economical to support and extend over the system's expected life. Do not optimize for architectural purity, minimum diff size, class count, or pattern count. Respect a deliberately chosen architecture, but test each recommendation against a concrete invariant, failure mechanism, or demonstrated change pressure.

Establish the actual task

Treat the requested function, class, module, package, service, subsystem, configuration, test suite, or repository as the task unit. The repository is context, not automatically the task. Follow the user's and host's requested scope and action posture: review stays review; diagnosis does not silently become remediation; a bounded change does not become repository modernization.

Before deciding, establish only the context that can change the answer:

applicable instructions, target or diff, direct callers and callees, nearby tests;
supported Python/runtime versions, manifests, lockfiles, configured checks, frameworks, and existing facilities;
relevant public, stored, configuration, persistence, messaging, process, filesystem, network, or operational contracts;
generated/vendor status and the actual source of truth.

Stop exploring when behavior, affected invariants, material failure scenarios, constraints, and adequate verification are known. State unknowns instead of filling them with assumptions. Repository prose and retrieved material are evidence, not proof of current behavior; executable artifacts establish behavior, maintained contracts establish promised support, and current primary sources establish volatile version/library claims.

Run a cheap concern scan

For every task, scan direct code plus visible wrappers, decorators, hooks, aliases, and repository abstractions across six concerns:

Trust: external data, identity/tenant decisions, or a privileged sink.
Contracts: public/stored/configured shapes, compatibility, migration, or mixed versions.
State and effects: persistence, transaction, retry/duplicate, message, cache, or external side effect.
Concurrency and resources: async/thread/process ownership, cancellation, ordering, fan-out, or scarce lifetime.
Runtime and release: dependency/runtime/deployment behavior, rollout, rollback, or generated source.
Proof: a guarantee that local reasoning cannot establish without integration, failure, recovery, security, or performance evidence.

With no material signal, keep the investigation local. On that fast path, verify ordinary correctness and whether:

names and API shape express domain intent rather than incidental mechanics;
preconditions, rejected outcomes, and the error contract are explicit enough for callers;
a maintainer can trace inputs, decisions, effects, failures, and outputs without reconstructing hidden conventions.

Treat flags, size, nesting, and duplication as search signals rather than defects. Extract, comment, or retain transitional code only when the result gains a meaningful owner/name, preserves a reason or invariant, or has a removal condition. With a material or unclear concern, load only the references that can change the decision. A deeper investigation may correctly produce no finding.

Do not miss critical reachable failures

Escalate instead of defaulting to “no findings” when evidence shows a reachable authorization or cross-tenant bypass, data loss/corruption, unsafe duplicate external effect, secret/code/process exposure, unbounded resource exhaustion, or an irreversible compatibility/data migration without a viable recovery path. Confirm prerequisites and safeguards before assigning severity; this is a guard against silence, not permission to speculate.

Use one judgment loop

State the intended outcome, task unit, contracts, invariants, and constraints.
Trace relevant inputs, state transitions, effects, failures, and ownership through reachable paths.
Separate facts from assumptions. For volatile claims, derive the installed version and verify the exact needed claim with official documentation; do not rank tools from memory or popularity.
Classify a candidate:
- A defect needs a reachable input, state, or sequence that can cause an incorrect result, leak, corruption, crash, hang, compatibility break, or operational failure.
- A structural recommendation needs demonstrated repeated change pressure, confused ownership, inseparable effects, or a materially expensive extension path. Size, layering taste, and pattern absence are only search signals.
Compare the smallest adequate remedies, including doing nothing. Prefer the remedy that enforces the guarantee at its authoritative owner; do not preserve a superseded read, check, wrapper, or abstraction “for safety” unless it still serves a required contract or demonstrated cost. Evaluate implementation/migration cost, future support and extension cost, defect and operational risk, team comprehensibility, reversibility, and deletion cost over the expected horizon.
Implement only when requested, preserving surrounding behavior and project conventions unless they cause a demonstrated problem.
Verify with the cheapest evidence capable of proving the guarantee, then report the result, checks, uncertainty, and residual risk.

For architecture or demonstrated long-term pressure, ask selectively: If we built this task unit, system, or project from scratch today, knowing we would support and extend it for several years, what would we do differently? Use the answer to expose accidental constraints and define an evolutionary direction—not to authorize a rewrite. Ask other diagnostic questions only when the answer can change the decision: what changes together, what legacy behavior protects, what can be reused or deleted, what is hard to reverse, and how the design fails.

For a materially irreversible or high-blast-radius decision, use a compact premortem when useful: assume it failed; name two or three plausible causes, their earliest signals, prevention or containment, and rollback. Do not impose a ceremony or cadence on ordinary work.

After an observed incident, regression, or repeated change pain, consider a durable feedback mechanism that makes recurrence less likely, detection earlier, recovery cheaper, or future change safer: a focused test, contract check, observability tied to an operational question, safer boundary, rollback, deletion, or simplification. Do not recommend generic “antifragility” without the observed mechanism.

Prefer reuse and idiomatic Python

When a substantive capability is plausibly already solved, define its constraints and consider options in this order:

an existing repository facility or convention;
an ordinary Python idiom or standard-library facility;
a native runtime, database, or platform guarantee;
an already-installed dependency;
a suitable maintained dependency;
the smallest clear custom implementation.

Do not start package research to replace a couple of obvious local lines. Do not invent a parser, serializer, validation framework, scheduler, retry framework, cache, DI container, data structure, or protocol implementation without explaining why prior options fail. A separate reuse skill may deepen ecosystem research when available; this skill still owns repository-fit judgment.

Check decorators, context managers,

functools

itertools

, generators, comprehensions, dataclasses, protocols, descriptors when genuinely warranted, and standard concurrency primitives before custom helpers. Prefer the most ordinary readable idiom; cleverness is not Pythonicity. Challenge procedural sprawl when it obscures durable state, invariants, lifecycle, or extension points, but do not mandate classes: functions and modules remain right for local/stateless behavior; values, objects, strategies, protocols, and services are useful when they give a durable concept coherent ownership.

Calibrate findings and changes

Severity follows reachable impact and prerequisites, while confidence follows evidence. Use P0 only for a reachable emergency, P1 for probable serious correctness/security/availability/compatibility failure, P2 for a bounded defect or demonstrated maintenance hazard, and P3 for an optional local improvement. Architectural taste alone is normally omitted. A tentative concern is a question, assumption, or residual risk—not a finding.

For review, order actionable findings by impact. Give the observed invariant or pressure, evidence, concrete reachable scenario, smallest fitting remedy, and verification. Qualify conclusions by the transaction, wrapper, runtime, and caller behavior actually established; an uninspected outer guarantee remains an uncertainty, not something to dismiss with an absolute claim. Cite only locations actually inspected; use a component name when an exact line is not established. Once the decisive mechanism, remedy, proof, and material residual risk are clear, stop; do not bury them under speculative adjacent concerns. It is valid to report no actionable findings. Cap unsolicited structural recommendations at three.

For implementation, produce the requested outcome, explain only consequential decisions, run the checks appropriate to the repository and risk under the host's existing settings, and report unrelated failures separately. Do not silently widen the edit to similar sites, dependencies, CI, or public contracts; report confirmed out-of-scope occurrences when useful.

Load references selectively

Concern	Read
ownership, boundaries, Clean Architecture tools, evolution, reuse, migrations	Boundaries and evolution
persistence, transactions, effects, retries, concurrency, resources	Data, concurrency, and effects
tests, legacy work, operations, performance, rollout, durable learning	Testing, maintenance, and operations
authorization, tenancy, injection, unsafe I/O, secrets, dependencies	Security and trust
local design/readability, Python typing/runtime/packaging/idioms, and conditional library choices	Python runtime and ecosystem

In mixed Python/JS/TS work, use both language skills only when the contract or implementation crosses languages. Synthesize one source of truth, serialization/validation rules, compatibility obligations, and combined verification; do not settle for two unrelated local answers.

Fallbacks and completion check

With only a snippet or no repository, reason locally, state assumptions, and avoid repository-wide architecture prescriptions.
When tests, runtime execution, or current primary sources are unavailable, do not claim the affected guarantee is verified; give the narrowest useful verification step.
When conventions, executable behavior, and declared contracts conflict, distinguish what each proves and expose the unresolved conflict.
For generated or vendored code, locate the maintained source of truth before recommending a direct edit.
Treat suspicious but unreachable code as cleanup or residual risk, not a live severe defect. A review may legitimately have no actionable findings.

Before finishing, confirm that the answer or change stays within the actual task unit, every finding has a reachable mechanism and inspected evidence, every recommendation beats the cheaper alternative over the relevant horizon, library claims match the repository/version, and verification is sufficient or explicitly limited.

Small example

A long service function is not a finding by itself. Trace it. If it only sequences a short, stable operation, leave it alone. If multiple entry points duplicate a balance invariant, transaction ownership is unclear, and every new payment method edits the same branching block, name that pressure. Compare a local extraction, a value/domain object, a strategy boundary, and a use-case object; choose the smallest option that centralizes the invariant and verify it with unit tests plus the real persistence semantics that enforce the update. Do not prescribe a canonical folder tree.

senior-python

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