audit-markers

name	audit-markers
description	Audit and fix pytest markers on test files and examples. Classifies tests as unit/integration/e2e/qualitative using general heuristics and project-specific marker rules. Estimates GPU VRAM and RAM requirements by tracing model identifiers and looking up parameter counts. Use when: writing a new test and unsure which markers to apply; reviewing or auditing existing test markers; a test is unexpectedly skipped or not collected; a test is consuming too much GPU/RAM and you want to check its resource gates; checking marker correctness before committing; or any question about why a test does or doesn't run in a given configuration.
argument-hint	[file-or-directory] [--dry-run | --apply]
compatibility	Claude Code, IBM Bob
metadata	{"version":"2026-03-26","capabilities":["read_file","write_file","bash","grep","glob"]}

Audit & Fix Pytest Markers

Classify tests, validate markers, and fix issues. Works in two layers: general test classification (applicable to any project) plus project-specific marker rules for mellea.

Inputs

• $ARGUMENTS — file path, directory, or glob. If empty, audit test/ and docs/examples/.
• No flags (default) — produce report, then ask user to confirm before applying.
• --apply — produce report and apply fixes without asking.
• --dry-run — report only, do not offer to apply.

Single-file use: skip the triage phase (Step 0) entirely — deep-read the file directly and proceed from Step 1.

Modes of use

This skill has two modes. Choose based on what the user asked:

Audit mode (default)

User wants markers classified or fixed — for a new test, an existing file, or a pre-commit check. Follow the full Audit Procedure (Steps 0–4).

Diagnostic mode

User wants to know why a specific test is not running, is being skipped, or is consuming unexpected resources. Do NOT produce an audit report. Instead:

1. Read the test — identify its markers and any predicate decorators.
2. Check the default filter — read pyproject.toml [tool.pytest.ini_options] addopts. The project default is -m "not slow". If the test has slow, it is excluded from a plain uv run pytest run.
3. Check backend auto-skip — read test/conftest.py pytest_configure and the pytest_collection_modifyitems hook. Backend markers (ollama, huggingface, etc.) trigger auto-skip when the backend is unavailable. Check whether the relevant service or credentials are present on the user's machine.
4. Evaluate predicates — if the test has a predicate decorator (require_gpu, require_api_key, require_ram, etc.), read test/predicates.py and explain what condition would cause the skip. For require_gpu(min_vram_gb=N), compare N against the system's detected VRAM (run get_system_capabilities() logic or check sysctl hw.memsize on Apple Silicon / nvidia-smi on CUDA).
5. Report directly — answer "this test is skipped because X" with the specific condition, the value it evaluated to, and how to override if appropriate (e.g. uv run pytest test/path/test_foo.py bypasses the -m "not slow" default filter).

For resource overload (test consuming too much GPU/RAM): classify the test's resource gates using the VRAM heuristics in Part 2, compare against what the test actually loads, and report whether the gate is correctly set or too loose.

Project References

Read these before auditing — they are the authoritative source for marker conventions:

• Marker guide: test/MARKERS_GUIDE.md
• Marker registration: test/conftest.py (pytest_configure) and pyproject.toml ([tool.pytest.ini_options])
• Resource predicates: test/predicates.py (predicate functions for resource gating)
• Example marker format: docs/examples/conftest.py (_extract_markers_from_file)

---

Part 1: General Test Classification

These principles apply to any test suite, not just mellea. Use them as the foundation for classifying every test function.

The Four Granularity Tiers

Tests fall into exactly one tier based on what they exercise and what they need:

Unit

Entirely self-contained — no services, no I/O, no fixtures that connect to anything external. Pure logic testing.

Recognise by:

• Imports only from the project and stdlib — no external service clients
• Creates objects directly, calls methods, checks return values
• If it uses test doubles, they replace all external boundaries (network, DB, services, third-party SDKs)
• Third-party library is imported only as a type or helper, not as a real collaborator being asserted against
• No fixture that starts/connects to a real or fixture-managed service
• Runs in milliseconds to low seconds
• Would pass identically if you replaced a real SDK import with a stub of the same interface

Examples of unit assertions:

assert str(cb) == "hello"
assert len(items) == 3
assert raises(ValueError)
mock_backend.generate.assert_called_once()

Integration

Verifies that your code correctly communicates across a real boundary. The boundary may be a third-party SDK/library whose API contract you are asserting against, multiple internal components wired together, or a fixture-managed local service. What distinguishes integration from unit is that at least one real external component — not a mock or stub — is on the other side of the boundary being tested.

Key distinction from unit: The boundary is not limited to network or hardware. A test that wires project code against a real third-party SDK object to assert on output format or values is integration — even when entirely in-memory with no network I/O. The question is whether a real external component's API contract is being verified, not whether there is network activity.

Key distinction from e2e: Integration controls or provides its dependencies (mocks, in-memory SDK components, fixture-managed local services). E2E depends on real backends that exist independently (Ollama, cloud APIs, GPU-loaded models).

Positive indicators:

• Uses a real third-party SDK object to capture and assert on output — e.g. InMemoryMetricReader, InMemorySpanExporter, LoggingHandler — rather than patching the SDK away
• Asserts on format or content of data as received by the external component (semantic conventions, attribute names, accumulated values)
• Wires multiple real project components together and mocks only at the outermost boundary (LLM call, network, hardware)
• Breaking the interface between your code and the external component (e.g. a changed attribute name, a missing SDK method call) would cause the test to fail
• Fixture-managed dependencies that stand up or configure real local services

Negative indicators (likely unit instead):

• All external boundaries replaced with MagicMock, patch, or AsyncMock
• Third-party library imported only as a type or helper, not as a real collaborator being asserted against
• Toggles env vars and checks booleans or config state with no real SDK objects instantiated
• Only one real component under test; everything else is faked

Tie-breaker: If you changed the contract between your code and the external component (e.g. renamed an attribute, stopped calling the right SDK method), would this test catch it? If yes → integration. If no → unit.

Examples:

# Integration — real OTel InMemoryMetricReader, asserting SDK contract
@pytest.mark.integration
def test_token_metrics_attributes(clean_metrics_env):
    reader = InMemoryMetricReader()
    provider = MeterProvider(metric_readers=[reader])
    record_token_usage_metrics(input_tokens=10, output_tokens=5, ...)
    # Asserts against real OTel output — breaking the attribute name would fail this
    assert attrs["gen_ai.provider.name"] == "ollama"

# Integration — multiple real project components, only LLM call mocked
@pytest.mark.integration
def test_session_chains_components(mock_backend):
    session = start_session(backend=mock_backend)
    result = session.instruct("hello")
    assert mock_backend.generate.called

# Unit — real OTel SDK imported but only for isinstance check on a no-op
def test_instruments_are_noop_when_disabled(clean_metrics_env):
    counter = create_counter("test.counter")
    assert counter.__class__.__name__ == "_NoOpCounter"

E2E (End-to-End)

Tests against real backends — cloud APIs, local servers, or GPU-loaded models. No mocks on the critical path.

Recognise by:

• Uses a real backend (however started — cloud API, local server, script-launched, GPU-loaded)
• Needs infrastructure: running server, API key, GPU, sufficient RAM
• Fixtures create real service connections, not mocks
• Assertions check that the real service behaved correctly
• Assertions are deterministic — structural, type-based, or functional

Examples of e2e assertions:

assert isinstance(result, CBlock)              # type check
assert json.loads(result.value)                 # valid JSON
assert result._meta["status"] == "complete"     # status check
assert tool_call.function.name == "get_weather" # tool was invoked
assert result.parsed_repr is not None           # output exists

Qualitative

Subset of e2e. Same infrastructure requirements, but assertions are on non-deterministic output content that may vary across model versions, temperatures, or runs.

Recognise by:

• Same as e2e (real backend, real calls)
• Assertions check semantic content, natural language output, or quality
• A different model version could break the assertion even if the system works correctly

Examples of qualitative assertions:

assert "hello" in result.value.lower()          # content check
assert result.value.startswith("Subject")       # format of generated text
assert len(result.value.split()) > 50           # output length
assert "error" not in result.value.lower()      # absence of bad content

The decision rule: If swapping the model version could break the assertion despite the system working correctly → qualitative. If the assertion checks structure, types, or functional correctness → e2e.

Behavioural Signal Detection

Before deep-reading a file, grep-able signals reveal whether it is likely unit or non-unit. Use these to triage at scale (see Audit Procedure, Step 0).

Live-backend signals (test is likely NOT unit)

Category	Grep patterns	Notes
Network literals	localhost, 127.0.0.1, 0.0.0.0, port numbers (:11434, :8000)	Direct infra dependency
HTTP clients	requests.get(, httpx., aiohttp.ClientSession, urllib.request.urlopen	Real network unless mocked
Raw networking	socket.socket(, socket.connect(	Low-level network
Subprocess	subprocess.Popen(, subprocess.run(, subprocess.call(, os.system(	Spawns external process
API credentials	_API_KEY, _TOKEN, _SECRET in os.environ/os.getenv calls	Credential dependency
GPU / model loading	import torch, .to("cuda"), .from_pretrained(	Hardware dependency
External downloads	URL literals (http://, https://), urlopen, requests.get with URLs	Network fetch

Training memory signals (check require_gpu threshold)

Training tests consume significantly more memory than inference. When these patterns appear, verify that require_gpu(min_vram_gb=N) uses the training peak, not just the model parameter size:

Category	Grep patterns	Notes
Model load	from_pretrained(, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer	Downloading/loading a real model
Training	train_model(, Trainer(, trainer.train(, epochs=, num_train_epochs=	Any training loop
Inference on real model	.generate( in a test body without a mock	Full model forward pass
HF dataset download	load_dataset(	Dataset fetch + tokenisation

Training memory rule: Training requires ~2× the base model weight memory (activations, optimizer states, gradient temporaries). A test that trains then reloads the model for inference has two separate peaks — use the training peak plus headroom:

min_vram_gb = (model_param_bytes_in_bfloat16 * 2) + headroom
            ≈ (params_B * 2 GB) * 2  + ~4 GB headroom

Example: 3B bfloat16 model → 6 GB weights → training peak ~12 GB → set require_gpu(min_vram_gb=20) so the gate fires on machines where available GPU memory is below that, rather than letting the test OOM mid-run.

The VRAM heuristic (predicates.py) reports estimated available memory, not total RAM. A 32 GB Apple Silicon machine reports ~16 GB available. Setting min_vram_gb=20 correctly skips on that machine while running on 48 GB+.

Adapter accumulation signals (module-scoped backend with multiple intrinsics)

When a test module uses a module-scoped backend fixture and calls multiple different intrinsic functions (call_intrinsic, guardian_check, policy_guardrails, guardian_check_harm, etc.) against the same backend, each intrinsic loads a separate LoRA adapter that stays resident in _loaded_adapters for the lifetime of the fixture. Adapters do NOT auto-unload between tests.

Signal	Pattern	Notes
Module-scoped HF backend	scope="module" on a LocalHFBackend fixture	Adapter accumulation possible
Multiple intrinsic calls	call_intrinsic(, guardian_check(, policy_guardrails(, factuality_	Each loads a distinct adapter
No unload_adapter	Absence of backend.unload_adapter( in fixture teardown	Adapters pile up

Memory estimate for modules with adapter accumulation:

min_vram_gb = base_model_gb + (N_distinct_intrinsics × ~0.2 GB) + inference_overhead_gb

For test_guardian.py (6 tests, 3B base model, ~4 distinct adapters):

• Base model: ~6 GB
• 4 adapters: ~0.8 GB
• Inference overhead: ~2 GB
• Total: ~9 GB minimum → use require_gpu(min_vram_gb=12) for headroom

Flag any module where scope="module" backend + multiple distinct intrinsic calls has require_gpu(min_vram_gb=N) set only to cover the base model size.

SDK-boundary signals (test is likely integration, not unit)

These patterns indicate real third-party SDK objects are being used as collaborators to assert on output — not mocked away:

Category	Grep patterns	Notes
OTel metrics	InMemoryMetricReader, MeterProvider(metric_readers=	Asserting against real OTel metrics output
OTel tracing	InMemorySpanExporter, SimpleSpanProcessor, TracerProvider(	Asserting against real OTel span output
OTel logging	LoggingHandler, LoggerProvider, set_logger_provider	Asserting against real OTel log output
Real SDK setup	provider.force_flush(), reader.get_metrics_data()	Consuming real SDK output

Cross-reference: if these appear alongside @patch( that patches the SDK itself away → unit. If the SDK objects are instantiated and used directly → integration.

Mock signals (test is likely unit)

Category	Grep patterns
Mock objects	MagicMock, Mock(, AsyncMock, create_autospec
Patching	@patch(, @mock.patch, monkeypatch, mocker fixture
HTTP mocks	responses, respx, httpx_mock, aioresponses, vcr

Fixture signals (need chain tracing to resolve)

Signal	Likely tier
tmp_path, capsys, monkeypatch, caplog only	Unit
Custom fixtures named session, backend, m_session	Could be real or mock — trace the chain
Session/module-scoped fixtures (scope="session")	Usually infra setup → e2e
Fixture name starts with mock_, fake_, stub_	Unit

Cross-referencing signals

A single file may contain multiple signal types. Cross-reference to determine the correct bucket:

Live-backend signals?	SDK-boundary signals?	Mock signals?	Classification
Yes	Any	No	Almost certainly e2e — deep-read to confirm
Yes	Any	Yes	Needs inspection — partial mock = integration, or mixed file
No	Yes	No	Likely integration — deep-read to confirm SDK objects are asserted against, not just imported
No	Yes	Yes	Needs inspection — if SDK is patched away → unit; if used directly → integration
No	No	Yes	Likely unit — skip deep read
No	No	No	Likely unit — skip deep read

When to Ask the User

Some classifications are ambiguous. Ask for confirmation when:

• A test mixes structural and content assertions (e2e vs qualitative)
• A test uses a real backend but only checks that no exception was raised (could be e2e or integration if the backend call is incidental)
• A test patches some but not all external boundaries (partial mock — unit or integration?)
• An assertion is borderline: assert len(result.value) > 0 could be structural (e2e) or content-dependent (qualitative)

When asking, present the test code and your reasoning so the user can make an informed decision.

---

Part 2: Project-Specific Rules

Read test/MARKERS_GUIDE.md for the full marker reference (marker tables, resource gates, auto-skip logic, common patterns). This section covers only the code analysis heuristics the skill needs to classify tests — things that require reading the test source code rather than looking up a table.

Key project rules

• unit is auto-applied by conftest — never write it explicitly
• llm is deprecated (synonym for e2e) — flag and recommend replacing. This applies to both pytestmark lists and # pytest: comments in examples.
• Backend/resource markers only go on e2e/qualitative tests
• qualitative is per-function; module carries e2e + backend markers. Exception: if every test function in the file is qualitative, module-level qualitative is acceptable to avoid repetitive per-function decorators.
• If a file mixes unit and non-unit tests, apply markers per-function, not module-level

Backend detection heuristics

When classifying a test file, check ALL of the following to determine which backend(s) it uses:

• Imports: from mellea.backends.ollama import ... → ollama
• Session creation: start_session("ollama", ...) → ollama; bare start_session() with no backend arg → ollama (default backend)
• Backend constructors: OllamaModelBackend(...) → ollama; OpenAIBackend(...) → openai
• Environment variables checked: OPENAI_API_KEY → openai; WATSONX_API_KEY → watsonx
• Dual backends: OpenAIBackend pointed at Ollama's /v1 endpoint → both openai AND ollama (but NOT requires_api_key)

Project-specific triage signals

These supplement the general behavioural signals (Part 1) with mellea patterns:

Signal	Grep pattern	Implies
Backend import	from mellea.backends.	e2e (which backend depends on module)
Session creation	start_session(	e2e, default ollama
Backend constructor	OllamaModelBackend(|OpenAIBackend(|LocalHFBackend(|LocalVLLMBackend(|WatsonxAIBackend(|LiteLLMBackend(	e2e
Example marker comment	# pytest:	Already classified — validate
Ollama port	11434	e2e, ollama

Fixture chain tracing

This is the most important analysis step. A test's tier depends on what its fixtures actually provide. The test function signature alone is not enough — you must trace each fixture back to its definition to determine whether it connects to a real backend, a mock, or nothing external.

How to trace

1. Read the test function signature. List every fixture parameter (e.g., session, backend, m_session, gh_run).
2. Locate each fixture definition. Check (in order):
   • Same file (local @pytest.fixture functions)
   • Nearest conftest.py in the same directory
   • Parent conftest.py files up to test/conftest.py
   • Root conftest.py or plugin-provided fixtures
3. Follow the chain recursively. If a fixture depends on another fixture, trace that one too. Stop when you reach a leaf: a constructor, a mock, or a conftest-provided value.
4. Classify the leaf. The leaf determines the tier:
   • Real backend constructor (OllamaModelBackend(), LocalHFBackend(), LocalVLLMBackend(), OpenAIBackend(), WatsonxAIBackend(), LiteLLMBackend()) → e2e
   • start_session() (no mock involved) → e2e (default backend is ollama)
   • Subprocess that starts a server (subprocess.Popen(["vllm", "serve", ...])) → e2e
   • Mock/MagicMock/patch replacing the backend → unit (if self-contained) or integration (if wiring multiple real components around the mock)
   • No external dependency at all → unit

Common fixture chain patterns in this project

Pattern 1 — Direct session creation (e2e):

test_func(session) → session fixture → start_session() → real ollama

Backend: ollama. Tier: e2e.

Pattern 2 — Backend → session chain (e2e):

test_func(session) → session(backend) → backend fixture → LocalHFBackend(...)

Backend: huggingface. Tier: e2e.

Pattern 3 — Process → backend → session chain (e2e):

test_func(m_session) → m_session(backend) → backend(vllm_process) → vllm_process spawns subprocess

Backend: vllm (via OpenAI client). Tier: e2e.

Pattern 4 — OpenAI-via-Ollama (e2e, dual markers):

test_func(m_session) → m_session(backend) → OpenAIBackend(base_url="...ollama.../v1", api_key="ollama")

Backend markers: openai + ollama. NOT requires_api_key.

Pattern 5 — Mocked backend (unit or integration):

test_func(session) → session uses MagicMock/MockBackend/patch

If the test only checks the mock was called → unit. If the test wires real components around the mock → integration.

Pattern 5b — Real SDK collaborator (integration):

test_func(clean_metrics_env) → creates InMemoryMetricReader() + MeterProvider()
                              → calls project code → asserts on reader output

No network, no backend — but a real OTel SDK object is on the other side of the boundary being asserted against → integration.

Pattern 6 — No backend at all (unit):

test_func() — or test_func(tmp_path, capsys, ...)

Only uses pytest built-in fixtures. Tier: unit.

What to watch for

• gh_run fixture (from root conftest) — provides CI flag, does NOT indicate a backend. Ignore for classification purposes.
• autouse fixtures — aggressive_cleanup, normalize_ollama_host, auto_register_acceptance_sets are infrastructure. They do not affect tier.
• Conditional fixture bodies — some fixtures branch on gh_run to choose model IDs or options. The backend is still real in both branches → still e2e.
• pytest.skip() inside fixtures — a fixture that skips on CI (e.g., watsonx) is still e2e when it runs.
• MagicMock vs real instance — if a fixture returns MagicMock(spec=Backend), the test is NOT e2e regardless of what the test function does with it.
• Mixed files — a file might define both a real backend fixture (used by some tests) and have other tests that don't use any fixture. Classify per-function, not per-file.

Resource gating

E2e and qualitative tests need gating so they skip cleanly when the required infrastructure is absent. The preferred mechanism is predicate functions — reusable decorators that encapsulate availability checks. Test authors apply the predicate that matches their test's actual requirements.

The predicate factory pattern (general)

Projects should provide a shared module of predicate functions that return pytest.mark.skipif(...) decorators. This gives test authors precision (exact thresholds, specific env vars) without ad-hoc skipif or blunt resource markers scattered across files.

Determining min_vram_gb and min_gb values

When migrating legacy requires_gpu markers to predicates, do not guess or use blanket thresholds. Determine the correct min_vram_gb by tracing the model each test loads and computing VRAM requirements from parameter counts. Legacy requires_heavy_ram markers should simply be removed (see "What to audit" below).

Trace the model identifier

For each file needing GPU/RAM gating, determine which model(s) it loads. Check in order:

1. Module-level constants — e.g. BASE_MODEL = "ibm-granite/..." or MODEL_ID = model_ids.QWEN3_0_6B.
2. Fixture definitions — trace @pytest.fixture functions for:
   • LocalHFBackend(model_id=...) — extract the model_id argument
   • LocalVLLMBackend(model_id=...) — extract the model_id argument
   • start_session("hf", model_id=...) — extract model_id
3. ModelIdentifier resolution — if the model_id is a constant like model_ids.QWEN3_0_6B, read mellea/backends/model_ids.py and extract the hf_model_name field.
4. Conftest fixtures — check conftest.py files up the directory tree for fixture definitions that provide model/backend instances.
5. Per-function overrides — some files have different models per test function. Track per-function when this occurs.

Look up parameter count

Use these strategies in priority order. Stop at the first that succeeds.

Strategy A: HuggingFace Hub API (preferred, requires network)

from huggingface_hub.utils._safetensors import get_safetensors_metadata
meta = get_safetensors_metadata("ibm-granite/granite-3.3-8b-instruct")
total_params = sum(meta.parameter_count.values())

Run via uv run python -c "..." — only needs huggingface_hub (in the [hf] extra).

Strategy B: Ollama model info (for Ollama-tagged models)

ollama show <model_name> --modelfile 2>/dev/null | grep -i 'parameter'

Strategy C: Model name parsing (offline fallback)

Pattern	Extract	Example match
(\d+\.?\d*)b[-_.] or -(\d+\.?\d*)b	N billion	granite-3.3-8b → 8B
(\d+\.?\d*)B (capital B in HF names)	N billion	Qwen3-0.6B → 0.6B
-(\d+)m[-_.] or (\d+)m-	N million ÷ 1000	granite-4.0-h-350m → 0.35B
micro without explicit size	0.35B–3B	Check ModelIdentifier catalog

When the name is ambiguous (e.g. granite4:micro-h), resolve via the ModelIdentifier constant in model_ids.py — the HF name usually contains the explicit size.

Strategy D: Conservative default (last resort)

• Assume 8B parameters (16 GB at fp16)
• Flag as "model unidentified — manual review needed"

Backend determines GPU gating need

Backend	GPU loaded locally?	Predicate needed
LocalHFBackend	Yes	require_gpu(min_vram_gb=N)
LocalVLLMBackend	Yes	require_gpu(min_vram_gb=N)
OllamaModelBackend	Managed by Ollama	None — ollama backend marker + conftest auto-skip handles availability
OpenAIBackend (real API)	No	No GPU gate
OpenAIBackend → Ollama /v1	Managed by Ollama	None — ollama backend marker handles it
WatsonxAIBackend / LiteLLMBackend / Cloud	No	No GPU gate

Key rule: Ollama manages its own GPU memory. Tests using Ollama backends should use the ollama backend marker only, NOT require_gpu().

Compute VRAM estimate

VRAM formula:

vram_gb = params_B × bytes_per_param × 1.2

Where bytes_per_param depends on precision: fp32=4.0, fp16/bf16=2.0 (default), int8=1.0, int4=0.5. The 1.2 multiplier covers KV cache, activations, and framework buffers. Round min_vram_gb up to the next even integer.

Models load into GPU VRAM, not system RAM — do not add require_ram() to GPU tests. The require_ram() predicate exists for tests that are genuinely RAM-bound (large dataset processing, etc.), not as a companion to require_gpu().

What to audit

Check the project's predicate module (see Project References) for available predicates, then apply the following checks to every e2e/qualitative file:

1. Legacy resource markers → migrate to predicates. If a test uses @pytest.mark.requires_gpu or @pytest.mark.requires_api_key, replace with the equivalent predicate from the project's predicate module. If a test uses @pytest.mark.requires_heavy_ram or @pytest.mark.requires_gpu_isolation, remove them — these are deprecated markers with no direct replacement (requires_heavy_ram was a blanket 48 GB threshold that conflated VRAM with RAM; GPU isolation is now automatic). Resource markers are deprecated in favour of predicates. This is a fix (same priority as llm → e2e), not just a recommendation — apply it in Step 4 like any other marker fix. The replacement requires adding an import for the predicate and swapping the marker in the pytestmark list or decorator.
2. Ad-hoc skipif → migrate to predicate. If a predicate exists for the same check (e.g., require_gpu() exists but the test has a raw skipif(not torch.cuda.is_available())), replace with the predicate.
3. Missing gating. A test that uses a GPU backend but has no GPU predicate and no skipif — add the appropriate predicate.
4. Imprecise gating. A predicate that's too broad for the actual model being loaded — tighten the min_vram_gb threshold.
5. Redundant CICD skipif. skipif(CICD == 1) is usually redundant when conftest auto-skip or predicates already handle the condition. Flag as removable.

What NOT to flag

Not every skipif needs migrating. Leave these alone:

• Python version gates (skipif(sys.version_info < (3, 11))) — one-off, or use require_python() predicate if available.
• importorskip for optional deps — idiomatic pytest, or use require_package() predicate if available and a decorator style is preferred.
• Truly one-off conditions with no predicate equivalent and no pattern of recurrence across files.

For any inline skipif that IS NOT covered above, check whether a matching predicate exists. If it does → recommend migration. If it doesn't and the same condition appears in multiple files → flag as an infrastructure note ("consider adding a predicate for this condition").

Resource gating is orthogonal to tier classification — a test gated by require_gpu() is still e2e/qualitative based on what it exercises.

Project predicates (test/predicates.py)

Read test/predicates.py for the available predicates. Expected patterns:

Predicate	Use when test needs
require_gpu()	Any GPU (CUDA or MPS)
require_gpu(min_vram_gb=N)	GPU with at least N GB VRAM
require_ram(min_gb=N)	N GB+ system RAM (for genuinely RAM-bound tests, not GPU model loading)
require_api_key("OPENAI_API_KEY")	Specific API credentials
require_api_key("WATSONX_API_KEY", "WATSONX_URL", "WATSONX_PROJECT_ID")	Multiple credentials
require_package("cpex.framework")	Optional dependency
require_python((3, 11))	Minimum Python version

Typical combinations for backends:

• huggingface → require_gpu(min_vram_gb=N) (compute N from model params)
• vllm → require_gpu(min_vram_gb=N) (compute N from model params)
• watsonx → require_api_key("WATSONX_API_KEY", "WATSONX_URL", "WATSONX_PROJECT_ID")
• openai → require_api_key("OPENAI_API_KEY") only for real OpenAI (not Ollama-compat)

Example files (docs/examples/)

Examples use a comment-based marker format (not pytestmark):

# pytest: e2e, ollama, qualitative

Same classification rules apply. Parser: docs/examples/conftest.py (_extract_markers_from_file).

Legacy markers in examples: The same deprecation rules apply to # pytest: comments. Remove requires_heavy_ram, requires_gpu_isolation, and llm when found. Replace requires_gpu with the appropriate predicate marker if the comment format supports it, or just remove it and rely on the backend marker (e.g., huggingface already triggers GPU checks in the examples conftest).

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Audit Procedure

Step 0 — Triage (for scopes larger than ~5 files)

When auditing a directory or the full repo, do NOT deep-read every file. Use behavioural signal detection (Part 1) to bucket files first, then deep-read only files that need inspection.

Phase 0: Fixture discovery

Read conftest.py files in the target scope to catalog fixture names. Classify each fixture as live (returns a real backend/connection) or mock (returns a MagicMock, patch, or fake). Record these lists — they become additional grep patterns for the next phase.

Phase 1: Signal grep

Run grep across all target files for:

1. Live-backend signals — backend imports, constructors, start_session(, network literals (localhost, 127.0.0.1, port numbers), HTTP client usage, subprocess calls, _API_KEY/_TOKEN/_SECRET in env var checks, GPU/model loading (torch, .from_pretrained(), URL literals. 1a. Training signals — from_pretrained(, train_model(, Trainer(, epochs=, .generate( (non-mock), load_dataset(. Cross-reference against require_gpu(min_vram_gb=N) — check N uses the 2× training memory rule.
2. SDK-boundary signals — real third-party SDK objects used as collaborators: InMemoryMetricReader, InMemorySpanExporter, MeterProvider(metric_readers=, TracerProvider(, LoggingHandler, provider.force_flush(), reader.get_metrics_data(). See the SDK-boundary signal table in the Behavioural Signal Detection section.
3. Mock signals — MagicMock, Mock(, AsyncMock, @patch(, monkeypatch, mocker, HTTP mock libraries.
4. Existing markers — pytestmark, @pytest.mark., # pytest:.
5. Live/mock fixture names from Phase 0.

Phase 2: Bucket and prioritise

Cross-reference the signal hits into four priority buckets:

Priority	Condition	Action
P1 — Missing markers	Live-backend or SDK-boundary signals present, NO existing markers	Deep-read and classify. These are the most likely gaps.
P2 — Mixed signals	Both live/SDK signals AND mock signals present	Deep-read to determine if integration, partial mock, or mixed file.
P3 — Validate existing	Live-backend or SDK-boundary signals present, markers already exist	Spot-check that markers match. Replace deprecated llm.
P4 — Skip	No live-backend or SDK-boundary signals (mock-only or no signals)	Likely unit. Report as clean without deep-reading. Spot-check a sample if the count is large.

Phase 3: Deep-read

Process P1 → P2 → P3 files using Steps 1–5 below. For P4 files, list them in the summary as "N files — no live-backend signals, assumed unit."

Step 1 — Read and identify

Read the file fully. Identify:

• Module-level pytestmark (test files) or # pytest: comment (examples)
• Per-function @pytest.mark.* decorators
• Fixtures and their backend dependencies (trace the fixture chain — see above)
• Any use of the deprecated llm marker

For example files (docs/examples/): Examples are standalone scripts, not fixture-based tests. Classification comes from reading the code directly — look for backend imports, start_session() calls, and constructor usage. The # pytest: comment is the only marker mechanism (no pytestmark).

Step 2 — Classify each test function

For each def test_* or async def test_*, apply the general classification from Part 1 using the project-specific heuristics from Part 2:

1. Real backend, SDK collaborator, or fully mocked?
   • Real LLM backend (Ollama, HF, cloud API) → e2e
   • Real third-party SDK object asserted against (OTel reader, logging handler) → integration
   • All external boundaries mocked/patched → unit (single component) or integration (multiple real components wired)
2. Which backend(s)? → backend markers (e2e only)
3. Deterministic or content-dependent assertions? → e2e vs qualitative
4. What resources? → resource markers
5. Training memory? → if training signals present (train_model(, Trainer(, epochs=), verify require_gpu(min_vram_gb=N) uses 2× the model inference memory + headroom (see Training memory signals table).

If uncertain about a classification (especially qualitative vs e2e), note it and ask the user to confirm.

Step 3 — Compare and report

Output tiers

Scale the report detail to the scope of the audit:

Tier 1 — Summary table (always). Print first so the user sees the big picture before any detail:

| Category | Files | Functions |
|----------|------:|----------:|
| Correct (no changes) | 42 | — |
| Deprecated `llm` → `e2e` (simple) | 27 | — |
| Missing tier marker | 8 | 12 |
| Wrong marker | 3 | 5 |
| Over-marked | 2 | 4 |
| Missing resource gating | 4 | 6 |
| Legacy resource marker → predicate | 5 | 9 |
| Infrastructure notes | 3 | — |

Tier 2 — Issues-only detail. For each file with at least one issue, print the file header and only the functions that need changes. Omit functions that are already correct — they are noise at scale:

## test/backends/test_ollama.py

Module markers — Current: [llm, ollama] → Proposed: [e2e, ollama]
  ↳ replace deprecated `llm` with `e2e`

  test_structured_output — WRONG: asserts JSON schema, remove `qualitative`

Functions without issues (test_simple_instruct ✓, test_chat ✓) are not listed. Files where everything is correct appear only in the Tier 1 count.

Tier 3 — Batch groups (for mechanical fixes). When many files share the same fix (e.g. llm → e2e in pytestmark), collapse them into a single block instead of repeating the per-file template:

### Deprecated `llm` → `e2e` (27 files, module-level pytestmark)

test/backends/test_ollama.py
test/backends/test_openai.py
test/backends/test_watsonx.py
... (24 more)

The agent should list all files (not truncate) so the user can review before applying, but one line per file is sufficient when the fix is identical.

Step 4 — Apply fixes

The apply behaviour depends on the flags passed:

Flag	Behaviour
(none)	Output the full report (Steps 1–3 + Output Summary), then ask the user "Apply these N changes?" before writing any files.
--apply	Output the full report, then apply all fixes without asking.
--dry-run	Output the full report. Do NOT write any files or offer to apply.

When applying fixes:

Surgical edits only — change specific marker lines, do not reformat surrounding code. When replacing llm with e2e in pytestmark lists, keep the same list structure. When replacing legacy resource markers with predicates, add the necessary import (from test.predicates import ...) at the top of the file and swap the marker in the pytestmark list or decorator.

Step 5 — Backend registry audit

Check that every backend used in test files has a registered marker. The project's backend registry is BACKEND_MARKERS in test/conftest.py (single source of truth). Markers must also appear in pyproject.toml [tool.pytest.ini_options].markers and in test/MARKERS_GUIDE.md.

For each backend constructor or start_session(backend_name=...) call found during classification, verify:

1. A marker exists in BACKEND_MARKERS for that backend.
2. The marker appears in pyproject.toml.
3. The marker appears in the MARKERS_GUIDE.md backend table.

If a backend is used in tests but has no registered marker, flag it as a missing backend marker issue and add it to the registry, pyproject.toml, and MARKERS_GUIDE.md (same apply/confirm rules as other fixes in Step 4).

Step 6 — Flag infrastructure notes

Report issues outside marker-edit scope as notes. Do NOT fix these:

• Missing conftest skip logic for a backend
• Tests with no assertions
• Files mixing unit and integration tests that could be split
• Files mixing unit and e2e tests that could be split

Output Summary

The output is the Tier 1 summary table (always printed first) followed by Tier 2 issues-only detail and Tier 3 batch groups as described in Step 3. End the report with:

The summary table should include a row for Missing backend marker when backends are used in tests but not registered in BACKEND_MARKERS.

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Files audited: N | Correct: N | With issues: N
Changes: N applied / N pending confirmation / N dry-run
Infrastructure notes: N (see notes section)

Infrastructure (already in place — do not re-add)

The following infrastructure was set up in #727 and should NOT be recreated by this skill. If an audit finds these missing, something has regressed — flag as a blocker, don't silently re-add:

• Auto-unit hook: test/conftest.py pytest_collection_modifyitems adds pytest.mark.unit to any test without integration, e2e, or qualitative.
• Backend marker registry: BACKEND_MARKERS dict in test/conftest.py is the single source of truth for backend markers. pytest_configure iterates over it. New backends are added by inserting one entry into the dict. pyproject.toml and test/MARKERS_GUIDE.md must stay in sync manually.
• Resource predicates: test/predicates.py provides require_gpu, require_ram, require_api_key, require_package, require_python.