Registry indexed
Use when the user has changed a prompt (system prompt, RAG template, agent instruction, etc.) and wants to know whether the candidate is better or worse than the baseline. Also use when the user mentions prompt A/B testing, prompt comparison, prompt optimization validation, "did
Use when the user has changed a prompt (system prompt, RAG template, agent instruction, etc.) and wants to know whether the candidate is better or worse than the baseline. Also use when the user mentions prompt A/B testing, prompt comparison, prompt optimization validation, "did my prompt change help," or prompt regression testing. Outputs per-dimension win rates with statistical significance using OpenJudge PairwiseAnalyzer.
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NO conclusion about which prompt is better WITHOUT bootstrap 95% CI reported. NO candidate declared "better" WITHOUT position-debiased (swap-aggregate) comparison. NO comparison with fewer than 10 samples per axis — CI is too wide to be meaningful.
Compare two prompts head-to-head and determine, with statistical rigor, whether the candidate is better, worse, or tied on each evaluation dimension.
You MUST create a task for each item and complete them in order:
Don't hand-write the win-rate + bootstrap math (the swap-aggregation and CI are easy to get
wrong). Run the bundled, tested script (scripts/pairwise.py, standard library only, no
OpenJudge dependency):
python scripts/pairwise.py --comparisons comparisons.jsonl --candidate candidate --baseline baseline
Each comparison row: {"id","model_a","model_b","score","dimension"?} where score >= 0.5
means model_a won. Emit two rows per query with A/B swapped to debias position. The
script reports per-dimension candidate/baseline/tie rates, bootstrap 95% CI, and a verdict
(BETTER / WORSE / TIED / INSUFFICIENT_EVIDENCE / INCONCLUSIVE; exit 0 only if
better). --self-test to verify it.
Steps below explain how to derive dimensions and produce the comparisons (with OpenJudge or any judge); the inline snippets are the reference behind the script.
Read the baseline and candidate prompts. Identify:
Based on the task type and what changed, derive 3-5 comparison dimensions.
Chatbot / Conversational:
RAG Generation:
Code Review / Generation:
Agent Instructions:
Each dimension gets:
id (slug)pairwise or judge or ruleDecision priority:
FunctionGrader or StringMatchGrader. Free, deterministic.
Example: output length, keyword presence, JSON validity.pairwise against reference.pairwise A/B comparison.judge (binary pass/fail per output).LLM judges have position bias — the first response shown wins 5-15% more often. Swap-aggregate eliminates this: run each comparison twice with swapped positions, keep only consistent wins:
from openjudge.graders.llm_grader import LLMGrader
from openjudge.graders.schema import GraderMode
from openjudge.runner.grading_runner import GradingRunner
from openjudge.analyzer.pairwise_analyzer import PairwiseAnalyzer
# Judge prompt for relevance comparison
relevance_judge = LLMGrader(
model=model,
name="relevance_compare",
mode=GraderMode.POINTWISE,
template="""
Compare Response A and Response B for the query below.
Which response better addresses the user's question?
Query: {query}
Response A: {response_a}
Response B: {response_b}
Score 1.0 if A is better, 0.0 if B is better, 0.5 if tied.
Respond in JSON: {{"score": <float>, "reason": "<explanation>"}}
""",
)
# Build pairwise dataset with position swap
dataset = []
for sample in test_samples:
# Original order
dataset.append({
"query": sample["query"],
"response_a": baseline_outputs[sample["id"]],
"response_b": candidate_outputs[sample["id"]],
"metadata": {"model_a": "baseline", "model_b": "candidate"},
})
# Swapped order — critical for debiasing
dataset.append({
"query": sample["query"],
"response_a": candidate_outputs[sample["id"]],
"response_b": baseline_outputs[sample["id"]],
"metadata": {"model_a": "candidate", "model_b": "baseline"},
})
runner = GradingRunner(
grader_configs={"relevance": relevance_judge},
max_concurrency=8,
)
results = await runner.arun(dataset)
# Analyze with PairwiseAnalyzer
analyzer = PairwiseAnalyzer(model_names=["baseline", "candidate"])
analysis = analyzer.analyze(dataset, results["relevance"])
print(f"Win rates: {analysis.win_rates}")
# → {'baseline': 0.35, 'candidate': 0.55} → candidate wins 55% of comparisons
print(f"Best model: {analysis.best_model}")
Why swap-aggregate? Without it, if the judge prefers the first response shown, and you always show baseline first, you'll systematically underrate the candidate.
For each dimension, report:
PairwiseAnalyzer.analyze interprets each comparison as score >= 0.5 → model_a wins,
using the row's metadata.model_a / metadata.model_b. So derive a per-comparison
winner list from dataset + results, then bootstrap over that list — never index the
PairwiseAnalysisResult object (it has no per-sample rows).
import numpy as np
from openjudge.graders.schema import GraderScore
def per_comparison_winners(dataset, grader_results):
"""One named winner per comparison row (handles swapped order via metadata)."""
winners = []
for sample, result in zip(dataset, grader_results):
if not isinstance(result, GraderScore):
continue # skip errors
meta = sample.get("metadata", {})
winners.append(meta["model_a"] if result.score >= 0.5 else meta["model_b"])
return winners
def bootstrap_win_rate(winners, target, n_iter=1000):
n = len(winners)
rates = []
for _ in range(n_iter):
idx = np.random.choice(n, n, replace=True)
rates.append(sum(1 for i in idx if winners[i] == target) / n)
return float(np.percentile(rates, 2.5)), float(np.percentile(rates, 97.5))
winners = per_comparison_winners(dataset, results["relevance"])
n = len(winners)
candidate_rate = sum(1 for w in winners if w == "candidate") / n
baseline_rate = sum(1 for w in winners if w == "baseline") / n
ci_low, ci_high = bootstrap_win_rate(winners, target="candidate")
if ci_low > 0.5:
verdict = "candidate BETTER"
elif ci_high < 0.5:
verdict = "candidate WORSE"
elif (ci_high - ci_low) < 0.3:
verdict = "TIED (CI brackets 0.5, narrow)"
else:
verdict = "INCONCLUSIVE (CI too wide — need more samples)"
print({"candidate_win_rate": candidate_rate, "baseline_win_rate": baseline_rate,
"ci_95": [ci_low, ci_high], "verdict": verdict})
Note: with swap-aggregate each query produces 2 comparison rows. Bootstrapping over rows (above) is the simple approach; for a tighter estimate, bootstrap over queries and average the 2 swapped rows per query so position pairs stay together.
Prompt Regression: v1 (baseline) vs v2 (candidate)
Task: Customer support chatbot
Samples: 50
Dimension Candidate Baseline Tie 95% CI Verdict
===========================================================================
Answer relevance 58% 32% 10% [51%, 65%] ✓ BETTER
Factual accuracy 48% 44% 8% [41%, 55%] = TIED
Tone appropriateness 38% 52% 10% [31%, 45%] ✗ WORSE
Conciseness 62% 28% 10% [55%, 69%] ✓ BETTER
Summary: v2 is significantly better on relevance and conciseness,
but worse on tone appropriateness. The tone regression likely comes
from the new "be direct" instruction — consider softening it.
Top 3 tone failures (candidate worse):
1. Query: "I'm really frustrated..." → v2 response too curt
2. Query: "This is my first time..." → v2 missing empathetic opening
3. Query: "Can you help me understand..." → v2 skipped explanation
After 06-prompt-regression:
03-align-human: Calibrate the pairwise judge against human preferences.02-metric-design: Turn validated dimensions into permanent graders.04-eval-report: Include prompt comparison results in a comprehensive report.name: prompt-regression description: > Use when the user has changed a prompt (system prompt, RAG template, agent instruction, etc.) and wants to know whether the candidate is better or worse than the baseline. Also use when the user mentions prompt A/B testing, prompt comparison, prompt optimization validation, "did my prompt change help," or prompt regression testing. Outputs per-dimension win rates with statistical significance using OpenJudge PairwiseAnalyzer.
---
name: prompt-regression
description: >
Use when the user has changed a prompt (system prompt, RAG template, agent instruction,
etc.) and wants to know whether the candidate is better or worse than the baseline.
Also use when the user mentions prompt A/B testing, prompt comparison, prompt
optimization validation, "did my prompt change help," or prompt regression testing.
Outputs per-dimension win rates with statistical significance using OpenJudge
PairwiseAnalyzer.
---
<HARD-GATE>
NO conclusion about which prompt is better WITHOUT bootstrap 95% CI reported.
NO candidate declared "better" WITHOUT position-debiased (swap-aggregate) comparison.
NO comparison with fewer than 10 samples per axis — CI is too wide to be meaningful.
</HARD-GATE>
# Prompt Regression
Compare two prompts head-to-head and determine, with statistical rigor, whether
the candidate is better, worse, or tied on each evaluation dimension.
## When to Activate
- You changed the system prompt and want to verify it's actually better
- You're iterating on RAG answer templates
- You're optimizing agent step-by-step instructions
- You want data to support a prompt change decision
## Checklist
You MUST create a task for each item and complete them in order:
1. **Load and analyze prompts** — diff the baseline vs candidate
2. **Derive comparison dimensions** — from the prompt changes + task type
3. **Select graders per dimension** — pairwise, judge, or rule
4. **Run position-debiased comparison** — swap-aggregate to eliminate order bias
5. **Compute statistics** — win rates + bootstrap 95% CI per dimension
6. **Present results** — per-dimension verdict with confidence intervals
## Fast path: run the bundled script
Don't hand-write the win-rate + bootstrap math (the swap-aggregation and CI are easy to get
wrong). Run the bundled, tested script (`scripts/pairwise.py`, standard library only, **no
OpenJudge dependency**):
```bash
python scripts/pairwise.py --comparisons comparisons.jsonl --candidate candidate --baseline baseline
```
Each comparison row: `{"id","model_a","model_b","score","dimension"?}` where `score >= 0.5`
means `model_a` won. Emit two rows per query with A/B **swapped** to debias position. The
script reports per-dimension candidate/baseline/tie rates, bootstrap 95% CI, and a verdict
(`BETTER` / `WORSE` / `TIED` / `INSUFFICIENT_EVIDENCE` / `INCONCLUSIVE`; exit 0 only if
better). `--self-test` to verify it.
Steps below explain how to derive dimensions and produce the comparisons (with OpenJudge or
any judge); the inline snippets are the reference behind the script.
## Step 1: Load and Analyze Prompts
Read the baseline and candidate prompts. Identify:
- **Task type**: chatbot / RAG generation / code review / translation / summarization
/ agent instruction / other
- **What changed**: added constraints, changed tone, new examples, different output
format, expanded/shortened instructions
- **Intent of change**: what problem was the user trying to fix?
## Step 2: Derive Comparison Dimensions
Based on the task type and what changed, derive 3-5 comparison dimensions.
### Dimension templates by task type
**Chatbot / Conversational**:
- Answer relevance — does it address the user's question?
- Tone appropriateness — does the tone match context?
- Factual accuracy — no fabricated information
- Conciseness — doesn't ramble or over-explain
- Instruction following — obeys system prompt constraints
**RAG Generation**:
- Faithfulness — grounded in retrieved documents
- Citation accuracy — correctly references sources
- Completeness — covers all aspects of the query
- No hallucination — no claims beyond documents
**Code Review / Generation**:
- Bug detection — finds real issues
- False positive rate — doesn't flag correct code
- Actionability — suggestions are specific and implementable
- Code style — follows conventions
**Agent Instructions**:
- Tool selection — picks the right tool
- Step efficiency — minimal steps to goal
- Error recovery — handles failures gracefully
- Output format — follows specified structure
Each dimension gets:
- An `id` (slug)
- A one-sentence description
- A grader type: `pairwise` or `judge` or `rule`
## Step 3: Select Graders
Decision priority:
1. **Can a rule check this?** → `FunctionGrader` or `StringMatchGrader`. Free, deterministic.
Example: output length, keyword presence, JSON validity.
2. **Is there a reference answer?** → `pairwise` against reference.
3. **Subjective quality, no reference?** → `pairwise` A/B comparison.
4. **Single-output judgment needed?** → `judge` (binary pass/fail per output).
## Step 4: Run Position-Debiased Comparison
### Pairwise comparison with swap-aggregate
LLM judges have position bias — the first response shown wins 5-15% more often.
Swap-aggregate eliminates this: run each comparison twice with swapped positions,
keep only consistent wins:
```python
from openjudge.graders.llm_grader import LLMGrader
from openjudge.graders.schema import GraderMode
from openjudge.runner.grading_runner import GradingRunner
from openjudge.analyzer.pairwise_analyzer import PairwiseAnalyzer
# Judge prompt for relevance comparison
relevance_judge = LLMGrader(
model=model,
name="relevance_compare",
mode=GraderMode.POINTWISE,
template="""
Compare Response A and Response B for the query below.
Which response better addresses the user's question?
Query: {query}
Response A: {response_a}
Response B: {response_b}
Score 1.0 if A is better, 0.0 if B is better, 0.5 if tied.
Respond in JSON: {{"score": <float>, "reason": "<explanation>"}}
""",
)
# Build pairwise dataset with position swap
dataset = []
for sample in test_samples:
# Original order
dataset.append({
"query": sample["query"],
"response_a": baseline_outputs[sample["id"]],
"response_b": candidate_outputs[sample["id"]],
"metadata": {"model_a": "baseline", "model_b": "candidate"},
})
# Swapped order — critical for debiasing
dataset.append({
"query": sample["query"],
"response_a": candidate_outputs[sample["id"]],
"response_b": baseline_outputs[sample["id"]],
"metadata": {"model_a": "candidate", "model_b": "baseline"},
})
runner = GradingRunner(
grader_configs={"relevance": relevance_judge},
max_concurrency=8,
)
results = await runner.arun(dataset)
# Analyze with PairwiseAnalyzer
analyzer = PairwiseAnalyzer(model_names=["baseline", "candidate"])
analysis = analyzer.analyze(dataset, results["relevance"])
print(f"Win rates: {analysis.win_rates}")
# → {'baseline': 0.35, 'candidate': 0.55} → candidate wins 55% of comparisons
print(f"Best model: {analysis.best_model}")
```
Why swap-aggregate? Without it, if the judge prefers the first response shown,
and you always show baseline first, you'll systematically underrate the candidate.
## Step 5: Compute Statistics
For each dimension, report:
- Candidate win rate, baseline win rate, tie rate
- Bootstrap 95% confidence interval
- Verdict: better / worse / tied / inconclusive
`PairwiseAnalyzer.analyze` interprets each comparison as `score >= 0.5 → model_a wins`,
using the row's `metadata.model_a` / `metadata.model_b`. So derive a per-comparison
winner list from `dataset` + `results`, then bootstrap over that list — never index the
`PairwiseAnalysisResult` object (it has no per-sample rows).
```python
import numpy as np
from openjudge.graders.schema import GraderScore
def per_comparison_winners(dataset, grader_results):
"""One named winner per comparison row (handles swapped order via metadata)."""
winners = []
for sample, result in zip(dataset, grader_results):
if not isinstance(result, GraderScore):
continue # skip errors
meta = sample.get("metadata", {})
winners.append(meta["model_a"] if result.score >= 0.5 else meta["model_b"])
return winners
def bootstrap_win_rate(winners, target, n_iter=1000):
n = len(winners)
rates = []
for _ in range(n_iter):
idx = np.random.choice(n, n, replace=True)
rates.append(sum(1 for i in idx if winners[i] == target) / n)
return float(np.percentile(rates, 2.5)), float(np.percentile(rates, 97.5))
winners = per_comparison_winners(dataset, results["relevance"])
n = len(winners)
candidate_rate = sum(1 for w in winners if w == "candidate") / n
baseline_rate = sum(1 for w in winners if w == "baseline") / n
ci_low, ci_high = bootstrap_win_rate(winners, target="candidate")
if ci_low > 0.5:
verdict = "candidate BETTER"
elif ci_high < 0.5:
verdict = "candidate WORSE"
elif (ci_high - ci_low) < 0.3:
verdict = "TIED (CI brackets 0.5, narrow)"
else:
verdict = "INCONCLUSIVE (CI too wide — need more samples)"
print({"candidate_win_rate": candidate_rate, "baseline_win_rate": baseline_rate,
"ci_95": [ci_low, ci_high], "verdict": verdict})
```
Note: with swap-aggregate each query produces 2 comparison rows. Bootstrapping over rows
(above) is the simple approach; for a tighter estimate, bootstrap over *queries* and
average the 2 swapped rows per query so position pairs stay together.
## Step 6: Present Results
```
Prompt Regression: v1 (baseline) vs v2 (candidate)
Task: Customer support chatbot
Samples: 50
Dimension Candidate Baseline Tie 95% CI Verdict
===========================================================================
Answer relevance 58% 32% 10% [51%, 65%] ✓ BETTER
Factual accuracy 48% 44% 8% [41%, 55%] = TIED
Tone appropriateness 38% 52% 10% [31%, 45%] ✗ WORSE
Conciseness 62% 28% 10% [55%, 69%] ✓ BETTER
Summary: v2 is significantly better on relevance and conciseness,
but worse on tone appropriateness. The tone regression likely comes
from the new "be direct" instruction — consider softening it.
Top 3 tone failures (candidate worse):
1. Query: "I'm really frustrated..." → v2 response too curt
2. Query: "This is my first time..." → v2 missing empathetic opening
3. Query: "Can you help me understand..." → v2 skipped explanation
```
## Common Mistakes
- **Not doing position swap.** Position bias in LLM judges is 5-15%. Without
swap-aggregate, results are systematically skewed.
- **Comparing with < 10 samples.** Bootstrap CI at n=10 is ±15%+ half-width.
At n=5 it's ±25%+. Results are noise, not signal. Minimum 10, prefer 30+.
- **Single "overall" comparison without dimensions.** "V2 is 55% better" hides
that it's +20% on relevance but -15% on tone. Always report per-dimension.
- **Accepting ties as "no difference."** A true tie and insufficient data look
identical without CI. Always report confidence intervals.
- **Not pinning model versions.** If baseline and candidate are run on different
model versions (even same model, different date), model drift contaminates
the prompt comparison. Same model, same version, same temperature.
## Next Skills
After `06-prompt-regression`:
- **`03-align-human`**: Calibrate the pairwise judge against human preferences.
- **`02-metric-design`**: Turn validated dimensions into permanent graders.
- **`04-eval-report`**: Include prompt comparison results in a comprehensive report.Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
70/100
Strong
Trust
64/100
Sandbox only
Audit
77/100
Risky
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"The full implementation of scripts/pairwise.py is not visible in the excerpt, but the provided portion indicates a well-structured, stdlib-only script with clear usage and exit codes.",
"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
"Quality score needs review"
]
},
"agent_proven": {
"version": "agent-proven-v1",
"score": 0,
"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 77,
"risk_level": "risky",
"risk_label": "Risky",
"warnings": [
"Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
"The full implementation of scripts/pairwise.py is not visible in the excerpt, but the provided portion indicates a well-structured, stdlib-only script with clear usage and exit codes.",
"The skill relies on the user to correctly generate the comparisons.jsonl file with swapped rows; no automated validation is provided for that input format.",
"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
"Quality score needs review"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 70,
"label": "Strong"
},
"supply": {
"track": "Coding and developer agents",
"scenario": "Coding agents",
"maintenance": "1mo since push",
"risk": "Risky"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"The full implementation of scripts/pairwise.py is not visible in the excerpt, but the provided portion indicates a well-structured, stdlib-only script with clear usage and exit codes.",
"No OpenAgentSkill engagement data yet",
"Audit risk risky exceeds max_risk=medium",
"High-risk permission hints: Shell or command execution",
"Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
"The skill relies on the user to correctly generate the comparisons.jsonl file with swapped rows; no automated validation is provided for that input format."
],
"agent_contract": {
"task_input": "Use prompt-regression in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 72/100 Strong shortlist",
"Audit: 77/100 Risky",
"Safety: 49/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "agentscope-ai-prompt-regression (prompt-regression)",
"install_command": "npx skills add agentscope-ai/OpenJudge --skill prompt-regression",
"risk_summary": "Risky; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "agentscope-ai-prompt-regression",
"task": "Use prompt-regression in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/agentscope-ai-prompt-regression",
"api": "https://www.openagentskill.com/api/agent/skills/agentscope-ai-prompt-regression",
"audit": "https://www.openagentskill.com/skills/agentscope-ai-prompt-regression/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=agentscope-ai-prompt-regression&task=Use%20prompt-regression%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20prompt-regression%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20prompt-regression%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/agentscope-ai-prompt-regression/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/agentscope-ai-prompt-regression"
}
}Listing source
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Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.