managed-toolchain

name	managed-toolchain
description	Use when captured linters, generated tool configs, managed binaries, config drift, missing tools, package-relative path resolution, or host tool usage causes lint or hook failures.
metadata	{"source":"coding_ethos.yml","generated_by":"coding-ethos","ethos_principles":["static-analysis-is-the-first-line-of-defense","validation-at-the-gate","one-path-for-critical-operations","security-by-design"]}

Managed Toolchain And Config Integrity

Use this skill when lint behavior differs between repos, a generated config drift check fails, or a tool invocation is using consumer or host settings instead of the managed coding-ethos toolchain.

ETHOS Grounding

• static-analysis-is-the-first-line-of-defense: Make ruff and mypy blocking quality gates rather than advisory tools.
• validation-at-the-gate: Validate configuration, schema, and extensions during bootstrap rather than on first use.
• one-path-for-critical-operations: Keep one explicit, validated path for critical operations.
• security-by-design: Design for least privilege, validation, and safe defaults from the start.

Short Hint

Linters and configs must resolve through coding-ethos managed paths. Restore generated configs and run captured tools through the wrapper.

Use When

• managed toolchain
• tool_capabilities
• config drift
• generated config
• real binary not found
• host linter
• package-relative path
• tool capture
• fix-configs

Remediation Workflow

1. Treat the consumer repo as untrusted for linter binaries and config.
2. Use MCP tool_capabilities to inspect managed sandbox posture before diagnosing missing tools, network access, or write-path failures.
3. Keep target file resolution faithful to the caller, but run the managed linter and managed config from the coding-ethos checkout.
4. If generated configs drift, restore them with the documented coding-ethos fix-configs path instead of editing hashes or ignores.
5. When a managed binary is missing, repair through make build rather than installing host-global tools or editing shims.
6. Capture the normalized lint output and trace so repeated failures can become stronger ETHOS mappings.

Principle Details

Static Analysis is the First Line of Defense

We rely on linters (ruff) and type checkers (mypy) to catch errors before the code ever runs.

Directive: Make ruff and mypy blocking quality gates rather than advisory tools.

Quick ref:

• Use make release-audit for the full local gate.
• Use make -C coding-ethos parent-lint for coding-ethos policy lint.
• Keep parent lint scoped to production code; tests are verified by pytest.

Overview

We rely on linters (ruff) and type checkers (mypy) to catch errors before the code ever runs.

• We do not suppress linter errors unless absolutely necessary.
• Type hints are mandatory, not optional.
• If the CI pipeline fails static analysis, the code is effectively broken.

Machine-Enforced Gates

Static analysis belongs in enforced local hooks and CI, not in optional tribal knowledge.

• Repos should run their canonical lint and type suites automatically before code lands.
• If a repo exposes pre-commit, pre-push, or CI static analysis gates, treat them as part of the engineering contract.
• Passing the current local gate matters more than saying a previous run was green.

Repo Addendum

Gmeow uses project-local Makefile targets as the stable command surface for agents and CI. Prefer those targets so uv, pytest, build, and release hygiene checks remain consistent.

Validation at the Gate

Configuration, schema, and extension availability are validated immediately upon container initialization.

Directive: Validate configuration, schema, and extensions during bootstrap rather than on first use.

Quick ref:

• Validate configuration, schema, and extensions during bootstrap rather than on first use.
• Configuration, schema, and extension availability are validated immediately upon container initialization.

Overview

Configuration, schema, and extension availability are validated immediately upon container initialization.

• If a Postgres extension is missing, we raise ExtensionMissingError immediately. We do not wait for a query to fail 5 hours later.
• If a cache adapter is misconfigured, we prevent the application from starting.

One Path for Critical Operations

When there are multiple ways to accomplish a critical operation, bugs hide in the less-traveled path.

Directive: Keep one explicit, validated path for critical operations.

Quick ref:

• Keep one explicit, validated path for critical operations.
• When there are multiple ways to accomplish a critical operation, bugs hide in the less-traveled path.
• Use the repo's canonical validation entrypoint; do not invent partial substitutes.

Overview

When there are multiple ways to accomplish a critical operation, bugs hide in the less-traveled path. We enforce a single, canonical path for operations that matter.

No Modal Behavior Switches

We strictly ban boolean parameters that fundamentally change what a function does.

A parameter like persist: bool = True creates two different functions masquerading as one. Callers must understand not just what the function does, but which version they're invoking. This is a recipe for confusion and bugs.

The Anti-Pattern (Forbidden):

# ❌ WRONG: Boolean switch creates two code paths
async def onboard_dataframe(
    self,
    entity_id: str,
    dataframe: DataFrame,
    *,
    persist: bool = True,  # This creates a fork in behavior
) -> ProcessingResult:
    result = self._compute_schema_and_stats(dataframe)

    if persist:  # One code path: compute AND store
        await self._store_metadata(entity_id, result)
        await self._store_statistics(entity_id, result)

    return result  # Other code path: compute only

Why This Is Dangerous:

1. Hidden Complexity: The function signature lies about what it does. Two behaviors, one name.
2. Testing Burden: Both paths must be tested, but only one is typically exercised in production.
3. Bug Magnet: Callers pick the wrong path. The bug we hit: persist=True bypassed ULID generation because it wasn't the canonical persistence path.
4. Violation of SRP: The function has two reasons to change—computation logic OR persistence logic.
5. Documentation Rot: Docstrings must explain both modes, inevitably becoming stale for one.

The Correct Way: Separate Concerns, Single Path

If an operation can be decomposed, decompose it. If persistence must happen, it happens through ONE designated service.

# ✅ CORRECT: Function does ONE thing
async def onboard_dataframe(
    self,
    entity_id: str,
    dataframe: DataFrame,
) -> ProcessingResult:
    """Compute schema and statistics for a dataframe.

    This function ONLY computes. It does NOT persist.
    Persistence is handled by EntityMetadataService.ingest_dataset().
    """
    return ProcessingResult(
        schema=self._infer_schema(dataframe),
        profile=self._compute_statistics(dataframe),
    )

# ✅ CORRECT: Orchestration layer calls the ONE persistence path
async def ingest(self, request: IngestionRequest) -> IngestionResponse:
    onboarding_result = await onboarding_service.onboard_dataframe(...)

    # THE canonical path for persistence—generates ULID, stores everything
    await dataset_service.ingest_dataset(entity_id, metadata_payload)

Identifying Modal Functions

Watch for these warning signs:

• Parameters named persist, save, commit, execute, dry_run, skip_*
• Boolean parameters that guard large if/else blocks
• Functions where "it depends" on a flag whether side effects occur
• Docstrings that say "If X is True, then... otherwise..."

Canonical Validation Entry Points

Repos must define one documented way to run quality gates locally.

If a repo documents make check, pre-commit run --all-files, or another validation entrypoint, use that path.

Do not substitute a hand-picked subset and then claim the work is validated.

Alternate commands may exist for debugging, but only the canonical gate proves readiness.

The Exception: Explicit Dry-Run at API Boundaries

A dry_run parameter is acceptable ONLY at the outermost API boundary (CLI commands, REST endpoints) where the user explicitly requests a preview. It must:

1. Be clearly named and documented as a preview mode
2. Execute the SAME code path but skip the final commit/write
3. Never be the mechanism for internal code to "optionally" persist

# ✅ ACCEPTABLE: CLI dry-run for user preview
@cli.command()
def ingest(path: Path, dry_run: bool = False):
    """Ingest a dataset. Use --dry-run to preview without persisting."""
    result = service.ingest(path, dry_run=dry_run)
    # dry_run affects the FINAL write, not internal behavior

Anti-Patterns (Forbidden)

• ❌ def process(data, save: bool = True) — modal persistence
• ❌ def validate(schema, strict: bool = False) — if strictness matters, make two functions
• ❌ def fetch(url, cache: bool = True) — caching is infrastructure, not a per-call decision
• ❌ Internal functions with persist, commit, or store boolean parameters

The Rule

For any critical operation, ask: "Is there exactly ONE way to do this correctly?" If the answer involves "it depends on a boolean parameter," the design is wrong. Split the function, or designate a single orchestration point.

Security by Design

Security is not a feature to be added later—it is a property of the design.

Directive: Design for least privilege, validation, and safe defaults from the start.

Quick ref:

• Design for least privilege, validation, and safe defaults from the start.
• Security is not a feature to be added later—it is a property of the design.
• Secrets, credentials, and API keys must never appear in source code, configuration files, or commit history.

Overview

Security is not a feature to be added later—it is a property of the design. We build security in from the start.

No Secrets in Code

Secrets, credentials, and API keys must never appear in source code, configuration files, or commit history.

• Environment variables: All secrets loaded via environment (.env files)
• Never commit secrets: No API keys, passwords, or tokens in any file
• No default secrets: Never provide "example" credentials that could be used in production

The Anti-Pattern (Forbidden):

# ❌ WRONG: Hardcoded secrets
API_KEY = "sk-abc123..."  # NEVER
DATABASE_URL = "postgresql://admin:password123@prod-db/..."  # NEVER

# ❌ WRONG: "Example" secrets that become real
API_KEY = os.getenv("API_KEY", "sk-test-key-for-development")  # Still wrong

The Correct Way:

# ✅ CORRECT: Secrets from environment, no defaults
API_KEY = os.environ["API_KEY"]  # Fails if not set - good

# ✅ CORRECT: Explicit configuration validation
@dataclass
class Config:
    api_key: str = field(default_factory=lambda: os.environ["API_KEY"])

    def __post_init__(self) -> None:
        if not self.api_key:
            raise ConfigurationError(
                "API_KEY environment variable is required"
            )

Parameterized Queries Only

SQL injection is a solved problem. We solve it by using parameterized queries exclusively.

The Anti-Pattern (Forbidden):

# ❌ CATASTROPHIC: String formatting SQL
query = f"SELECT * FROM users WHERE id = {user_id}"  # SQL injection
query = "SELECT * FROM users WHERE name = '%s'" % name  # SQL injection

The Correct Way:

# ✅ CORRECT: Parameterized queries
query = "SELECT * FROM users WHERE id = $1"
result = await conn.fetch(query, user_id)

# ✅ CORRECT: Using query builder with parameterization
query = select(users).where(users.c.id == bindparam("user_id"))
result = await conn.execute(query, {"user_id": user_id})

Input Validation at Boundaries

All external input is untrusted. Validate at the boundary, then trust internally.

• API endpoints: Validate all request parameters with Pydantic
• File inputs: Validate file types, sizes, and content
• Configuration: Validate at startup, not at use time (see Validation at the Gate)

Production Database Detection

Operations that could harm production must detect and refuse production environments.

# ✅ CORRECT: Production safety guard
async def reset_database(conn: Connection) -> None:
    """Reset database to clean state. NEVER runs in production."""
    db_name = await conn.fetchval("SELECT current_database()")
    if "prod" in db_name.lower():  # Add your production DB name check here
        raise SecurityError(
            f"Refusing to reset production database: {db_name}",
            database=db_name,
    )
    await conn.execute("DROP SCHEMA public CASCADE")

Automated Security Gates

Repos should automatically detect obvious security regressions before commit and in CI.

Secret scanning, unsafe query detection, boundary validation, and environment-safety guards should be machine-enforced where the repo can express them.

If a repo ships security hooks, they are part of the build contract and must not be bypassed casually.

Anti-Patterns (Forbidden)

• ❌ Secrets in source code, config files, or environment defaults
• ❌ String-formatted SQL queries
• ❌ Trusting user input without validation
• ❌ Running destructive operations without environment checks
• ❌ Assuming security features exist without verifying them

The Rule: Security is not optional. It is validated, enforced, and designed into every component. If a security measure can be bypassed, it's not a security measure—it's a suggestion.

Output Discipline

When explaining a fix, name the ETHOS principle, the concrete code change, and the verification evidence. Do not recommend weakening lint config or adding suppressions unless the ETHOS policy explicitly allows it.