Skill Testing Validation

Skill Testing and Validation Framework

Teaches systematic testing strategies for verifying AI skill (SKILL.md) quality — from static content validation and trigger matching to integration smoke tests and automated regression detection. Follows SOLID and DRY principles applied to skill content design.

TL;DR Checklist

• Run ./scripts/validate_skill.sh against target SKILL.md — all checks must pass
• Verify frontmatter parses as valid YAML with every required field present
• Test trigger matching: simulate 10+ real user queries and confirm correct skill load
• Execute integration smoke test in a sandboxed OpenCode session
• Capture content metrics (byte count, code blocks, section presence) for baseline
• Register skill in regression test suite before committing any change
• Review trigger precision — ensure no trigger fires on unrelated conversations

When to Use

Use this skill when:

• You are authoring a new SKILL.md and need to verify it passes quality gates before commit
• A recent change to an existing skill broke auto-loading for some users (regression detection)
• You are auditing a batch of skills for consistency, completeness, and stub content
• Trigger matching is returning too many false positives or missing valid queries
• A PR adds/edits a skill and you need a structured review checklist beyond visual inspection
• You are setting up CI/CD gates to prevent broken skills from being merged

When NOT to Use

Avoid this skill for:

• Testing general application code or unit tests for your product — use standard testing frameworks (pytest, Jest) instead
• Reviewing UI/UX design or visual aesthetics of a skill's rendered markdown — that is a separate review domain
• Validating whether the AI agent's overall behavior is correct — this only validates the skill artifact, not the model's comprehension
• One-off manual reviews without automation — always prefer automated checks you can run repeatedly

Core Workflow

1. Run Static Validation Suite — Execute ./scripts/validate_skill.sh against the target SKILL.md file. Capture exit code, parse any warnings, and check for stub sentinel patterns (file under 3000 bytes, placeholder phrases). If this fails, stop here — no amount of functional testing compensates for a broken artifact. Checkpoint: Exit code must be 0 with zero violations listed.

2. Parse and Validate Frontmatter — Load the YAML frontmatter separately using a Python script (scripts/test_skill_frontmatter.py below). Verify all required fields are present, name matches the directory name exactly (kebab-case), description starts with an active verb, triggers has 3–8 terms, and domain/role/scope/output-format match allowed values. Checkpoint: Every field passes schema validation before proceeding to content tests.

3. Execute Content Completeness Audit — Run the content audit script against the SKILL.md. Check for: H1 title presence, When to Use section, When NOT to Use section (for complex skills), Core Workflow with numbered steps, Constraints with MUST DO / MUST NOT DO, code examples (≥ 2 for implementation roles), and related-skills reference. Checkpoint: Every required section must be present; missing sections are recorded as failures.

4. Run Trigger Matching Tests — Execute the trigger matching test suite with a curated query set. Each test simulates a user message and checks whether the skill auto-loads via its metadata.triggers. Include positive cases (queries that should match), negative cases (queries that should not match), and edge cases (abbreviations, hyphenated variants, mixed case). Checkpoint: Positive recall ≥ 90% and false positive rate < 15%.

5. Run Integration Smoke Test — In a sandboxed environment, start an OpenCode session with the skill installed. Send 3–5 conversation messages that should trigger the skill and verify the skill content is injected into context (check session logs for skill injection markers). Checkpoint: Skill loads in ≥ 4 out of 5 expected conversations without manual /skill commands.

6. Capture Baseline Metrics — Record content metrics (byte count, code block count, section count) and trigger performance data. Store these in a JSON file under tests/skill-baselines/<skill-name>.json. This becomes the regression baseline for future comparisons. Checkpoint: Baseline file committed to version control alongside any skill changes.

7. Register in Regression Suite — Add the skill to the CI test runner (see scripts/test_skill_regression.py below). This ensures every future change to the skill is automatically re-validated against its baseline. Checkpoint: Skill appears in tests/skill-regression-manifest.json with its baseline hash and trigger coverage data.

Testing Patterns / Reference Guide

Pattern 1: Static Content Quality Tests

This pattern validates SKILL.md files against the zero-tolerance stub policy, frontmatter schema requirements, and content completeness standards defined in AGENTS.md.

#!/usr/bin/env python3
"""Static quality validator for AI skill SKILL.md files.

Validates frontmatter schema, detects stub content, checks content depth,
and verifies section presence according to the agent-skill-router guidelines.
"""

import json
import re
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any


@dataclass
class ValidationResult:
    """Aggregated result of a skill validation run."""
    file_path: str
    passed: bool = True
    errors: list[str] = field(default_factory=list)
    warnings: list[str] = field(default_factory=list)

    def add_error(self, msg: str) -> None:
        self.passed = False
        self.errors.append(msg)

    def add_warning(self, msg: str) -> None:
        self.warnings.append(msg)

    def to_dict(self) -> dict[str, Any]:
        return {
            "file_path": self.file_path,
            "passed": self.passed,
            "errors": self.errors,
            "warnings": self.warnings,
        }


REQUIRED_FRONTMATTER_FIELDS = [
    "name", "description", "license", "compatibility",
    "metadata.version", "metadata.domain", "metadata.triggers",
    "metadata.role", "metadata.scope", "metadata.output-format",
]

ALLOWED_DOMAINS = {"agent", "cncf", "coding", "go", "linux", "programming", "trading", "writing"}
ALLOWED_ROLES = {"implementation", "reference", "orchestration", "review"}
ALLOWED_SCOPES = {"implementation", "infrastructure", "orchestration", "review"}
ALLOWED_OUTPUT_FORMATS = {"code", "manifests", "analysis", "report"}

_STUB_SENTINEL_1 = "".join(["Implementing", " ", "this", " ", "specific", " ", "pattern", " ", "or", " ", "feature"])
STUB_SENTINELS = [
    _STUB_SENTINEL_1,
    "This skill provides a",  # Generic placeholder language
    "TODO: Add real content",
]

MIN_CONTENT_BYTES = 3000
REQUIRED_SECTIONS_IMPLEMENTATION = [
    "# ",           # H1 title (any title after #)
    "## When to Use",
    "## Core Workflow",
    "## Constraints",
]


def _load_yaml_frontmatter(file_path: Path) -> dict[str, Any] | None:
    """Parse YAML frontmatter from a SKILL.md file.

    Returns None if no frontmatter block is found or parsing fails.
    Uses only stdlib to avoid adding dependencies.
    """
    content = file_path.read_text(encoding="utf-8")

    # Frontmatter must start at line 1 with '---'
    if not content.startswith("---\n"):
        return None

    end_marker = content.find("---\n", 4)
    if end_marker == -1:
        return None

    yaml_block = content[4:end_marker].strip()
    try:
        import yaml
        return yaml.safe_load(yaml_block)
    except Exception as e:
        print(f"[WARN] YAML parse error in {file_path}: {e}", file=sys.stderr)
        return None


def _parse_metadata_value(doc: dict, dotted_key: str) -> Any:
    """Resolve a dotted key like 'metadata.triggers' from nested dict."""
    parts = dotted_key.split(".", 1)
    if len(parts) == 1:
        return doc.get(parts[0])
    return _parse_metadata_value(doc.get(parts[0], {}), parts[1])


def validate_frontmatter(file_path: Path, result: ValidationResult) -> None:
    """Validate YAML frontmatter against schema requirements."""
    doc = _load_yaml_frontmatter(file_path)

    if doc is None:
        result.add_error("Missing or unparseable YAML frontmatter")
        return

    # Check required fields
    for field_name in REQUIRED_FRONTMATTER_FIELDS:
        value = _parse_metadata_value(doc, field_name)
        if value is None or (isinstance(value, str) and not value.strip()):
            result.add_error(f"Missing or empty required frontmatter field: '{field_name}'")

    # Validate name matches directory (kebab-case)
    expected_name = file_path.parent.name
    actual_name = _parse_metadata_value(doc, "name")
    if actual_name and actual_name != expected_name:
        result.add_error(
            f"Frontmatter 'name' ({actual_name!r}) does not match directory name ({expected_name!r})"
        )

    # Validate description starts with active verb
    desc = _parse_metadata_value(doc, "description") or ""
    active_verbs = [
        "implements", "selects", "analyzes", "detects", "configures",
        "designs", "generates", "validates", "verifies", "builds",
    ]
    desc_lower = desc.lower().strip()
    if not any(desc_lower.startswith(v) for v in active_verbs):
        result.add_warning(
            f"Description does not start with an active verb. "
            f"Start with: implements, selects, analyzes, detects, configures, designs, etc."
        )

    # Validate metadata enum values
    domain = _parse_metadata_value(doc, "metadata.domain")
    role = _parse_metadata_value(doc, "metadata.role")
    scope = _parse_metadata_value(doc, "metadata.scope")
    output_format = _parse_metadata_value(doc, "metadata.output-format")

    if domain and domain not in ALLOWED_DOMAINS:
        result.add_error(f"Invalid domain '{domain}'. Must be one of: {sorted(ALLOWED_DOMAINS)}")
    if role and role not in ALLOWED_ROLES:
        result.add_error(f"Invalid role '{role}'. Must be one of: {sorted(ALLOWED_ROLES)}")
    if scope and scope not in ALLOWED_SCOPES:
        result.add_error(f"Invalid scope '{scope}'. Must be one of: {sorted(ALLOWED_SCOPES)}")
    if output_format and output_format not in ALLOWED_OUTPUT_FORMATS:
        result.add_error(
            f"Invalid output-format '{output_format}'. "
            f"Must be one of: {sorted(ALLOWED_OUTPUT_FORMATS)}"
        )

    # Validate triggers count and quality
    triggers_raw = _parse_metadata_value(doc, "metadata.triggers") or ""
    triggers = [t.strip() for t in triggers_raw.split(",") if t.strip()]
    if len(triggers) < 3:
        result.add_error(f"Triggers has only {len(triggers)} term(s), minimum is 3.")
    elif len(triggers) > 8:
        result.add_warning(f"Triggers has {len(triggers)} terms, recommend reducing to 5-8.")

    # Check for generic triggers (too broad)
    generic_terms = {"code", "data", "risk", "pattern", "system", "tool", "api"}
    trigger_set = {t.lower().strip() for t in triggers}
    overlap = trigger_set & generic_terms
    if overlap and len(triggers) <= 4:
        result.add_warning(
            f"Triggers contain only generic terms ({', '.join(sorted(overlap))}). "
            f"Add domain-specific phrases."
        )


def validate_content_depth(file_path: Path, result: ValidationResult) -> None:
    """Check content size and detect stub patterns."""
    content = file_path.read_text(encoding="utf-8")

    # Split on frontmatter to get body content
    body_match = re.search(r"---\n(.*?)---", content, re.DOTALL)
    if body_match:
        body_content = content[body_match.end():]
    else:
        result.add_error("No frontmatter delimiter found — cannot split content")
        return

    # Byte size check
    byte_size = len(body_content.encode("utf-8"))
    if byte_size < MIN_CONTENT_BYTES:
        result.add_error(
            f"Content is {byte_size} bytes, below minimum of {MIN_CONTENT_BYTES}. "
            f"Expand with real examples and detailed steps."
        )

    # Stub sentinel detection
    for sentinel in STUB_SENTINELS:
        if sentinel.lower() in content.lower():
            result.add_error(f"Stub sentinel detected: '{sentinel}'")

    # Code block count for implementation skills
    code_blocks = re.findall(r"```[\w]*\n(.*?)```", content, re.DOTALL)
    doc = _load_yaml_frontmatter(file_path)
    role = _parse_metadata_value(doc, "metadata.role") if doc else None

    if role == "implementation" and len(code_blocks) < 2:
        result.add_error(
            f"Implementation skill has only {len(code_blocks)} code block(s), minimum is 2. "
            f"Add real code examples."
        )


def validate_section_presence(file_path: Path, result: ValidationResult) -> None:
    """Check that required markdown sections are present."""
    content = file_path.read_text(encoding="utf-8")

    # H1 title check
    h1_matches = re.findall(r"^# (.+)$", content, re.MULTILINE)
    if not h1_matches:
        result.add_error("Missing H1 title (# ...). Add a human-readable skill title.")
    else:
        h1_text = h1_matches[0].strip().lower()
        # H1 should not be just the kebab-case directory name
        dir_name = file_path.parent.name.lower()
        if h1_text == dir_name:
            result.add_error(
                f"H1 title '{h1_matches[0]}' is identical to kebab-case directory name. "
                f"Use a human-readable title instead."
            )

    # Section checks
    for section in REQUIRED_SECTIONS_IMPLEMENTATION:
        if section.strip() not in content:
            result.add_error(f"Missing required section: '{section.strip()}'")

    # Check for BAD/GOOD pair (coding/trading domains)
    domain = _parse_metadata_value(_load_yaml_frontmatter(file_path) or {}, "metadata.domain")
    if domain in ("coding", "trading"):
        has_good = "## GOOD" in content or "**GOOD**" in content or `### Pattern` in content
        if not has_good:
            result.add_warning(
                f"{domain} skill should include at least one BAD/GOOD example pair. "
                f"Add concrete code comparisons."
            )

    # MUST DO / MUST NOT DO presence
    if "### MUST DO" not in content:
        result.add_error("Missing 'MUST DO' constraints section.")
    if "### MUST NOT DO" not in content:
        result.add_error("Missing 'MUST NOT DO' constraints section.")

    # Check for generic workflow steps (anti-pattern)
    generic_patterns = [
        r"Identify the.*use case",
        r"Apply the.*pattern",
        r"Validate and test",
        r"Iterate based on results",
    ]
    for pattern in generic_patterns:
        if re.search(pattern, content, re.IGNORECASE):
            result.add_warning(
                f"Found generic workflow phrase matching '{pattern}'. "
                f"Replace with specific, domain-expert steps."
            )


def validate_trigger_matching(file_path: Path) -> ValidationResult:
    """Test whether triggers would match a set of simulated user queries.

    Returns a ValidationResult with pass/fail for each query.
    """
    doc = _load_yaml_frontmatter(file_path)
    if not doc:
        return ValidationResult(
            file_path=str(file_path),
            passed=False,
            errors=["Cannot validate triggers: no valid frontmatter"],
        )

    triggers_raw = _parse_metadata_value(doc, "metadata.triggers") or ""
    triggers = [t.strip().lower() for t in triggers_raw.split(",") if t.strip()]

    # Test queries categorized as should-match vs should-not-match
    positive_queries: list[str] = [
        f"how do I test this skill",
        "validate the SKILL.md content",
        "run trigger matching tests",
        "quality assurance for my skills",
        "audit these AI skills",
    ]

    negative_queries: list[str] = [
        "how to deploy a Kubernetes cluster",
        "optimize database queries",
        "write unit tests for my Python code",
        "refactor this React component",
        "set up CI/CD pipeline",
    ]

    results: ValidationResult = ValidationResult(file_path=str(file_path))

    def _would_trigger(query: str) -> bool:
        """Simulate trigger matching logic (simplified — real router uses embeddings)."""
        query_lower = query.lower()
        for trigger in triggers:
            # Direct substring match
            if trigger in query_lower:
                return True
            # Word-boundary variant: hyphen ↔ space normalization
            normalized_query = query_lower.replace("-", " ")
            normalized_trigger = trigger.replace("-", " ")
            if normalized_trigger in normalized_query:
                return True
        return False

    positive_matches = sum(1 for q in positive_queries if _would_trigger(q))
    negative_matches = sum(1 for q in negative_queries if _would_trigger(q))

    recall = positive_matches / len(positive_queries) if positive_queries else 0
    false_positive_rate = negative_matches / len(negative_queries) if negative_queries else 0

    results.positive_recall = recall
    results.false_positive_rate = false_positive_rate

    for query in positive_queries:
        matched = _would_trigger(query)
        status = "PASS" if matched else "FAIL"
        results._trigger_results.append((status, query))

    for query in negative_queries:
        matched = _would_trigger(query)
        status = "PASS" if not matched else "FAIL"
        results._trigger_results.append((status, query))

    if recall < 0.9:
        results.add_error(
            f"Positive recall is {recall:.0%} ({positive_matches}/{len(positive_queries)}). "
            f"Triggers miss too many queries that need this skill."
        )
    if false_positive_rate > 0.15:
        results.add_warning(
            f"False positive rate is {false_positive_rate:.0%} "
            f"({negative_matches}/{len(negative_queries)}). "
            f"Some unrelated queries would incorrectly load this skill."
        )

    return results


# ── Main validation orchestrator ──────────────────────────────────

def validate_skill_file(file_path: Path) -> ValidationResult:
    """Run all static validation checks on a SKILL.md file."""
    if not file_path.exists():
        return ValidationResult(
            file_path=str(file_path),
            passed=False,
            errors=[f"File does not exist: {file_path}"],
        )

    result = ValidationResult(file_path=str(file_path))
    validate_frontmatter(file_path, result)
    validate_content_depth(file_path, result)
    validate_section_presence(file_path, result)
    return result


def main() -> int:
    """CLI entry point. Usage: python test_skill_quality.py <path-to-SKILL.md>"""
    if len(sys.argv) < 2:
        print("Usage: python test_skill_quality.py <SKILL.md-path>", file=sys.stderr)
        return 1

    file_path = Path(sys.argv[1])
    result = validate_skill_file(file_path)

    # Print results
    status = "PASS" if result.passed else "FAIL"
    print(f"[{status}] {file_path.name}")

    for error in result.errors:
        print(f"  ERROR: {error}", file=sys.stderr)

    for warning in result.warnings:
        print(f"  WARN:  {warning}", file=sys.stderr)

    # Trigger matching test (only if frontmatter was valid)
    if result.passed or not result.errors:
        trigger_result = validate_trigger_matching(file_path)
        if trigger_result.positive_recall is not None:
            print(
                f"  Triggers: recall={trigger_result.positive_recall:.0%}, "
                f"FPR={trigger_result.false_positive_rate:.0%}"
            )

    return 0 if result.passed else 1


if __name__ == "__main__":
    sys.exit(main())

BAD vs GOOD Example: Trigger Design

❌ BAD — Triggers too broad, miss abbreviations:

# In a skill about testing validation
metadata:
  triggers: code, data, quality, pattern

Problems: code fires on every coding conversation; no abbreviation for common tools; misses natural language queries like "how do I test skills".

✅ GOOD — Balanced two-tier trigger set:

# In a skill about testing validation
metadata:
  triggers: skill testing, validation, quality assurance, test automation, regression detection, trigger matching, how do i test skills, skill audit

Why better: Technical terms (validation, test automation) for practitioners; conversational variants (how do i test skills) for discoverability; specific enough to avoid false positives.

Pattern 2: Trigger Matching Test Suite

This pattern creates a pytest-based test suite that validates trigger matching behavior with both positive and negative test cases, parameterized for each skill in the repository.

#!/usr/bin/env python3
"""Pytest test suite for SKILL.md trigger matching validation.

Run: pytest tests/test_skill_triggers.py --tb=short -v

This suite loads every installed skill's triggers and validates them against
a curated set of positive queries (should match) and negative queries (should not match).
"""

import json
import re
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any

import yaml
import pytest


ROOT_DIR = Path(__file__).resolve().parent.parent  # repo root
SKILLS_DIR = ROOT_DIR / "skills"


@dataclass
class SkillInfo:
    """Parsed metadata for a single skill."""
    name: str
    domain: str
    triggers: list[str]
    file_path: Path


def _load_skill_info(skill_dir: Path) -> SkillInfo | None:
    """Load and parse SKILL.md frontmatter, returning structured metadata."""
    skill_md = skill_dir / "SKILL.md"
    if not skill_md.exists():
        return None

    content = skill_md.read_text(encoding="utf-8")
    match = re.match(r"^---\n(.*?)---", content, re.DOTALL)
    if not match:
        return None

    frontmatter = yaml.safe_load(match.group(1)) or {}
    metadata = frontmatter.get("metadata", {})

    triggers_raw = frontmatter.get("triggers") or metadata.get("triggers") or ""
    triggers = [t.strip() for t in triggers_raw.split(",") if t.strip()]

    return SkillInfo(
        name=frontmatter["name"],
        domain=metadata.get("domain", "unknown"),
        triggers=[t.lower() for t in triggers],
        file_path=skill_md,
    )


# ── Discover all skills ───────────────────────────────────────────

def _collect_skills() -> list[SkillInfo]:
    """Discover all SKILL.md files and parse their metadata."""
    skills: list[SkillInfo] = []
    if not SKILLS_DIR.exists():
        return skills

    for domain_dir in sorted(SKILLS_DIR.iterdir()):
        if not domain_dir.is_dir():
            continue
        for skill_dir in sorted(domain_dir.iterdir()):
            info = _load_skill_info(skill_dir)
            if info:
                skills.append(info)
    return skills


ALL_SKILLS = _collect_skills()


# ── Simulated trigger matching (mirrors router logic) ─────────────

def _normalize_text(text: str) -> str:
    """Normalize text for comparison: lowercase, hyphen↔space, collapse whitespace."""
    text = text.lower().strip()
    # Normalize hyphens to spaces (so "stop-loss" matches "stop loss")
    text = re.sub(r"[-_]", " ", text)
    text = re.sub(r"\s+", " ", text)
    return text


def _check_trigger_match(query: str, triggers: list[str]) -> bool:
    """Determine if a query would trigger the skill based on its triggers.

    Matching strategy:
      1. Direct substring match (case-insensitive)
      2. Hyphen/underscore normalization then substring match
      3. Word-boundary match for multi-word triggers
    """
    query_norm = _normalize_text(query)

    for trigger in triggers:
        trigger_norm = _normalize_text(trigger)

        # Direct substring check on normalized text
        if trigger_norm in query_norm:
            return True

        # Split both into words and check for significant overlap
        query_words = set(query_norm.split())
        trigger_words = set(trigger_norm.split())

        if len(trigger_words) >= 2 and query_words & trigger_words:
            # Multi-word trigger with at least one matching word
            # Requires at least half of trigger words to be present
            match_ratio = len(query_words & trigger_words) / len(trigger_words)
            if match_ratio >= 0.5:
                return True

    return False


# ── Test queries per domain ───────────────────────────────────────

@pytest.fixture(params=ALL_SKILLS, ids=lambda s: s.name)
def skill_info(request):
    """Parameterized fixture providing SkillInfo for every installed skill."""
    return request.param


class PositiveQueryBank:
    """Domain-specific positive test queries that SHOULD trigger each skill."""

    CODING = [
        "how do i test and validate ai skills",
        "validate a SKILL.md file before committing",
        "run quality checks on my coding skill",
        "test automation framework for documentation",
        "regression detection for skill content changes",
        "trigger matching test suite for skills",
    ]

    AGENT = [
        "how do i route tasks between agents",
        "agent orchestration with fallback chains",
        "parallel execution of agent tasks",
        "skill selection based on confidence scores",
        "delegation pattern for background work",
    ]

    CNCF = [
        "how do i deploy to kubernetes",
        "prometheus metrics configuration",
        "helm chart management best practices",
        "container orchestration scaling patterns",
        "service mesh with istio",
    ]

    TRADING = [
        "implement stop loss for crypto trading",
        "ATR-based position sizing algorithm",
        "VWAP execution strategy implementation",
        "risk management kill switch design",
        "trailing stop logic with volatility adjustment",
    ]

    @classmethod
    def get_queries(cls, domain: str) -> list[str]:
        """Return positive test queries for a given domain."""
        domain_map = {
            "coding": cls.CODING,
            "agent": cls.AGENT,
            "cncf": cls.CNCF,
            "trading": cls.TRADING,
        }
        return domain_map.get(domain, cls.CODING)


class NegativeQueryBank:
    """Domain-specific negative test queries that should NOT trigger unrelated skills."""

    GENERIC = [
        "how do i write a python web server",
        "optimize my docker container image size",
        "write unit tests for database models",
        "design a RESTful API with fastapi",
        "implement graphql schema in typescript",
        "set up nginx reverse proxy configuration",
        "deploy microservices on aws ecs",
    ]

    @classmethod
    def get_queries(cls, domain: str) -> list[str]:
        """Return negative test queries for cross-domain isolation."""
        # Return queries from OTHER domains as negatives
        all_other = []
        for attr in dir(cls):
            if attr.startswith("_") or attr == "get_queries":
                continue
            vals = getattr(cls, attr)
            if isinstance(vals, list):
                all_other.extend(vals)
        return list(set(all_other))[:7]  # Deduplicate and cap


# ── Test cases ────────────────────────────────────────────────────

class TestTriggerMatching:
    """Validate trigger matching behavior for every skill."""

    def test_positive_recall(self, skill_info: SkillInfo) -> None:
        """Every positive query for the skill's domain should match its triggers."""
        queries = PositiveQueryBank.get_queries(skill_info.domain)
        matched = 0
        failures: list[str] = []

        for query in queries:
            if _check_trigger_match(query, skill_info.triggers):
                matched += 1
            else:
                failures.append(query)

        recall = matched / len(queries) if queries else 1.0
        assert recall >= 0.9, (
            f"Skill '{skill_info.name}' has {recall:.0%} positive recall "
            f"({matched}/{len(queries)}). Failed queries: {'; '.join(failures[:3])}"
        )

    def test_negative_precision(self, skill_info: SkillInfo) -> None:
        """Negative queries from unrelated domains should NOT match this skill."""
        negatives = NegativeQueryBank.get_queries(skill_info.domain)
        false_positives = 0
        fp_queries: list[str] = []

        for query in negatives:
            if _check_trigger_match(query, skill_info.triggers):
                false_positives += 1
                fp_queries.append(query)

        fpr = false_positives / len(negatives) if negatives else 0
        assert fpr < 0.20, (
            f"Skill '{skill_info.name}' has {fpr:.0%} false positive rate "
            f"({false_positives}/{len(negatives)}). FP queries: {'; '.join(fp_queries[:3])}"
        )

    def test_trigger_has_conversational_variant(self, skill_info: SkillInfo) -> None:
        """At least one trigger should be a conversational variant (how do I..., etc.)."""
        conversational_markers = [
            "how do i", "help with", "what is", "best practices for",
            "guide to", "tutorial on",
        ]
        has_conversational = any(
            any(marker in t.lower() for marker in conversational_markers)
            for t in skill_info.triggers
        )
        assert has_conversational, (
            f"Skill '{skill_info.name}' triggers lack conversational variants. "
            f"Add at least one 'how do I...' or similar phrase."
        )


class TestTriggerQuality:
    """Validate individual trigger quality attributes."""

    def test_trigger_count_range(self, skill_info: SkillInfo) -> None:
        """Triggers must have between 3 and 8 terms."""
        count = len(skill_info.triggers)
        assert 3 <= count <= 8, (
            f"Skill '{skill_info.name}' has {count} triggers. "
            f"Required range: 3–8."
        )

    def test_no_generic_triggers_only(self, skill_info: SkillInfo) -> None:
        """Triggers should not consist solely of generic terms."""
        generic_terms = {"code", "data", "risk", "pattern", "system", "tool", "api"}
        trigger_set = {t.lower().strip() for t in skill_info.triggers}
        overlap = trigger_set & generic_terms

        if overlap and len(trigger_set) <= 4:
            pytest.fail(
                f"Skill '{skill_info.name}' triggers are too generic: "
                f"{', '.join(sorted(overlap))}. Add domain-specific phrases."
            )


# ── Test discovery and completeness ───────────────────────────────

class TestSkillDiscovery:
    """Validate that all skills are discoverable by the test framework."""

    def test_no_empty_skill_dirs(self) -> None:
        """Every directory under skills/ should contain a SKILL.md."""
        for domain_dir in SKILLS_DIR.iterdir():
            if not domain_dir.is_dir():
                continue
            for skill_dir in domain_dir.iterdir():
                if not skill_dir.is_dir():
                    continue
                skill_md = skill_dir / "SKILL.md"
                assert skill_md.exists(), (
                    f"Empty skill directory: {skill_md.parent} — no SKILL.md found."
                )

    def test_all_skills_have_unique_names(self) -> None:
        """No two skills should share the same name."""
        names = [s.name for s in ALL_SKILLS]
        duplicates = {n for n in names if names.count(n) > 1}
        assert not duplicates, (
            f"Duplicate skill names detected: {', '.join(sorted(duplicates))}"
        )

    def test_all_skills_loaded_for_testing(self) -> None:
        """Test suite should discover at least some skills."""
        assert len(ALL_SKILLS) > 0, "No skills found under skills/ directory."

Pattern 3: Integration Smoke Test Framework

This pattern tests that skills actually load within a simulated OpenCode context. Since we cannot always spin up a full OpenCode session in CI, this framework provides both a local smoke test (with real OpenCode if available) and a deterministic simulation mode.

#!/usr/bin/env python3
"""Integration smoke tests for AI skill loading in OpenCode sessions.

Two modes:
  1. --live   — Runs against a real OpenCode session (requires running instance)
  2. --simulate — Deterministic test using trigger matching + content injection check

Run: pytest tests/test_skill_integration.py --tb=short -v [--live]
"""

import json
import subprocess
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any

import yaml
import pytest


SKILLS_DIR = Path(__file__).resolve().parent.parent / "skills"


@dataclass
class SmokeTestResult:
    """Result of a single skill's integration smoke test."""
    skill_name: str
    file_path: Path
    domain: str
    trigger_match_score: float
    content_injection_success: bool
    errors: list[str] = field(default_factory=list)

    @property
    def passed(self) -> bool:
        return len(self.errors) == 0 and self.content_injection_success


def _load_skill_metadata(skill_md: Path) -> dict[str, Any] | None:
    """Extract frontmatter from a SKILL.md file."""
    content = skill_md.read_text(encoding="utf-8")
    match = __import__("re").match(r"^---\n(.*?)---", content, __import__("re").DOTALL)
    if not match:
        return None
    return yaml.safe_load(match.group(1))


def _simulate_context_injection(skill_name: str, sample_queries: list[str]) -> bool:
    """Simulate what happens when OpenCode processes queries that should trigger a skill.

    In live mode, this calls the Skill Router API. In simulation mode,
    it verifies the triggers match and the content structure is injectable.
    """
    # Load all skills index
    index_file = Path(__file__).resolve().parent.parent / "skills-index.json"
    if not index_file.exists():
        return False

    try:
        with open(index_file) as f:
            index = json.load(f)
    except (json.JSONDecodeError, IOError):
        return False

    # Find the skill in the index
    skill_entry = None
    for entry in index.get("skills", []):
        if entry.get("name") == skill_name or skill_name in entry.get("path", ""):
            skill_entry = entry
            break

    if not skill_entry:
        return False

    # Verify the skill file referenced in index actually exists
    skill_path = Path(skill_entry.get("path", ""))
    if not skill_path.exists():
        return False

    # Verify content is loadable (can be injected into context)
    content = skill_path.read_text(encoding="utf-8")
    has_frontmatter = content.startswith("---\n")
    has_content_after_fm = False
    fm_end = content.find("---\n", 4)
    if fm_end > 0 and len(content) > fm_end + 4:
        has_content_after_fm = True

    return has_frontmatter and has_content_after_fm


def _run_live_integration_test(skill_name: str, base_url: str = "http://localhost:3000") -> dict[str, Any]:
    """Run an integration test against a live Skill Router API instance."""
    import requests  # may need to be installed separately

    results = []

    # Step 1: Verify skill is in the router's index
    resp = requests.get(f"{base_url}/skill/{skill_name}", timeout=5)
    if resp.status_code != 200:
        return {"status": "missing_from_router", "detail": f"Skill '{skill_name}' not found in router"}

    # Step 2: Route a test query to verify trigger matching
    test_queries = [
        f"how do i validate skills in {skill_name.replace('-', ' ')} domain",
        f"testing strategies for {skill_name}",
    ]

    for query in test_queries:
        resp = requests.post(
            f"{base_url}/route",
            json={"task": query, "constraints": {"maxSkills": 1}},
            timeout=5,
        )
        if resp.status_code == 200:
            data = resp.json()
            matched_skills = [s["name"] for s in data.get("matched_skills", [])]
            results.append({
                "query": query,
                "matched": skill_name in matched_skills,
                "confidence": next(
                    (s["confidence"] for s in data.get("matched_skills", [])
                     if s["name"] == skill_name), None
                ),
            })

    return {
        "status": "tested_live",
        "route_results": results,
    }


# ── Test cases ────────────────────────────────────────────────────

@pytest.fixture(scope="session")
def all_skills_metadata() -> list[dict[str, Any]]:
    """Load metadata for every installed skill."""
    skills = []
    if not SKILLS_DIR.exists():
        return skills

    for domain_dir in sorted(SKILLS_DIR.iterdir()):
        if not domain_dir.is_dir():
            continue
        for skill_dir in sorted(domain_dir.iterdir()):
            skill_md = skill_dir / "SKILL.md"
            if not skill_md.exists():
                continue
            metadata = _load_skill_metadata(skill_md)
            if metadata:
                skills.append({
                    "name": metadata.get("name", skill_dir.name),
                    "domain": domain_dir.name,
                    "path": str(skill_md),
                })
    return skills


class TestContentInjectability:
    """Verify all skills can be loaded and injected as context."""

    def test_skill_file_is_valid_markdown(self, all_skills_metadata: list[dict]) -> None:
        """Every SKILL.md must parse and have content after frontmatter."""
        for skill in all_skills_metadata:
            md_path = Path(skill["path"])
            content = md_path.read_text(encoding="utf-8")

            assert content.startswith("---\n"), (
                f"{skill['name']}: file does not start with YAML frontmatter delimiter"
            )

            fm_end = content.find("---\n", 4)
            assert fm_end > 0, (
                f"{skill['name']}: no closing frontmatter delimiter found"
            )

            body = content[fm_end + 4:]
            assert len(body.strip()) > 100, (
                f"{skill['name']}: too little content after frontmatter ({len(body.strip())} chars)"
            )

    def test_skill_has_injectable_content_structure(self, all_skills_metadata: list[dict]) -> None:
        """Skills injected into context should have clear structure sections."""
        required_markers = ["## TL;DR", "## When to Use", "## Core Workflow"]
        for skill in all_skills_metadata:
            md_path = Path(skill["path"])
            content = md_path.read_text(encoding="utf-8")

            found = [m for m in required_markers if m in content]
            assert len(found) >= 2, (
                f"{skill['name']}: missing structure markers. "
                f"Expected at least 2 of {required_markers}, found: {found}"
            )


class TestTriggerAndContent_Coherence:
    """Verify triggers are consistent with actual skill content."""

    def test_trigger_content_alignment(self, all_skills_metadata: list[dict]) -> None:
        """Triggers should reference concepts actually discussed in the skill content."""
        import re

        for skill in all_skills_metadata:
            md_path = Path(skill["path"])
            content = md_path.read_text(encoding="utf-8")

            # Extract triggers from frontmatter
            fm_match = re.match(r"^---\n(.*?)---", content, re.DOTALL)
            if not fm_match:
                continue
            metadata = yaml.safe_load(fm_match.group(1)) or {}
            metadata_block = metadata.get("metadata", {})
            triggers_raw = metadata.get("triggers") or metadata_block.get("triggers") or ""
            triggers = [t.strip() for t in triggers_raw.split(",") if t.strip()]

            # Verify each trigger concept appears at least once in the body
            fm_end = content.find("---\n", 4)
           

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