> For the complete documentation index, see [llms.txt](https://developer.harness.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://developer.harness.io/harness-platform/use-harness-platform/knowledge-graph/prompt-library.md).

# Knowledge Graph Prompts library

Ask natural language questions about your software delivery workflows. The Knowledge Graph connects entities across pipelines, services, environments, and infrastructure to provide context-aware answers.

This page shows validated prompts organized by use case, how the graph traverses relationships, the HQL queries used, and sample outputs.

{% hint style="info" %}
**VALIDATION STATUS**

All prompts on this page have been validated against live Harness data. These queries are production-ready and tested with the Harness in-product experience and Harness APIs.
{% endhint %}

***

### What you will learn in this topic <a href="#what-you-will-learn-in-this-topic" id="what-you-will-learn-in-this-topic"></a>

By the end of this page, you will understand:

* How to ask questions about pipeline failures, build times, and productivity.
* How the Knowledge Graph connects entities to answer your questions.
* What HQL queries power each prompt.
* How to interpret query results.

***

### Pipeline failure analysis <a href="#pipeline-failure-analysis" id="pipeline-failure-analysis"></a>

Analyze pipeline health, identify failure patterns, and get remediation recommendations.

#### Prompt 1: Which pipelines have the highest failure rate? <a href="#prompt-1-which-pipelines-have-the-highest-failure-rate" id="prompt-1-which-pipelines-have-the-highest-failure-rate"></a>

**What it does**: Identifies pipelines that fail most frequently and surfaces common failure points across stages and steps.

**Graph traversal**:

```
Pipeline Execution
 ├── status: FAILED
 ├── pipeline_id
 └── Stage Execution
      ├── stage_name
      └── Step Execution
           ├── step_name
           ├── step_type
           └── failure_message
```

**HQL queries**:

```hql
# Query 1: Pipeline execution status distribution <a href="#query-1-pipeline-execution-status-distribution" id="query-1-pipeline-execution-status-distribution"></a>
find pipeline:pipeline_execution
  | where start_ts > ago("30d")
  | group by pipeline_id, status
  | count

# Query 2: Failure codes <a href="#query-2-failure-codes" id="query-2-failure-codes"></a>
find pipeline:step_execution
  | where status = "FAILED"
  | where start_ts > ago("30d")
  | group by failure_code
  | count
  | order by count desc

# Query 3: Stage-level failures <a href="#query-3-stage-level-failures" id="query-3-stage-level-failures"></a>
find pipeline:stage_execution
  | where status = "FAILED"
  | where start_ts > ago("30d")
  | group by pipeline_id, stage_name, stage_type
  | count
  | order by count desc

# Query 4: Root cause (step-level) <a href="#query-4-root-cause-step-level" id="query-4-root-cause-step-level"></a>
find pipeline:step_execution
  | where status = "FAILED"
  | where start_ts > ago("30d")
  | group by pipeline_id, stage_name, step_name, failure_code
  | count
  | order by count desc
```

**Sample output**:

| Pipeline          | Total Runs | Failed | Succeeded | Failure Rate |
| ----------------- | ---------- | ------ | --------- | ------------ |
| agt\_pipe         | 21         | 14     | 0         | 66.7%        |
| pipeline\_0e7b    | 1          | 1      | 0         | 100%         |
| pipeline\_7d66    | 10         | 1      | 9         | 10%          |
| test\_tracing\_v0 | 5          | 0      | 5         | 0%           |

**Root cause breakdown**:

| Pipeline  | Stage  | Step                       | Failure Code         | Count |
| --------- | ------ | -------------------------- | -------------------- | ----- |
| agt\_pipe | agt    | runEvals                   | APPLICATION\_FAILURE | 7     |
| agt\_pipe | agt    | runFeatureFlagCleanupAgent | APPLICATION\_FAILURE | 4     |
| agt\_pipe | Deploy | InitializeContainer        | GENERAL\_ERROR       | 2     |

**Visual representation**:

```
Pipeline: agt_pipe (66.7% failure rate)
│
├── Stage: agt (CI) → 11 failures
│   ├── runEvals → 7 APPLICATION_FAILURE
│   └── runFeatureFlagCleanupAgent → 4 APPLICATION_FAILURE
│
└── Stage: Deploy → 4 failures
    └── InitializeContainer → 2 GENERAL_ERROR
```

***

#### Prompt 2: Which pipelines failed the most in the last 30 days? <a href="#prompt-2-which-pipelines-failed-the-most-in-the-last-30-days" id="prompt-2-which-pipelines-failed-the-most-in-the-last-30-days"></a>

**What it does**: Ranks pipelines by total failure count to prioritize remediation efforts.

**Graph traversal**:

```
Pipeline Execution
 ├── status: FAILED
 ├── start_ts: last 30 days
 └── group by pipeline_id
```

**HQL query**:

```hql
find pipeline:pipeline_execution
  | where status = "FAILED"
  | where start_ts > ago("30d")
  | group by pipeline_id
  | count
  | order by count desc
```

**Sample output**:

| Pipeline       | Failures |
| -------------- | -------- |
| agt\_pipe      | 14       |
| pipeline\_6a8d | 1        |
| pipeline\_173b | 1        |
| pipeline\_0e7b | 1        |

**Visual representation**:

```
Pipeline Failures (Last 30 Days)
────────────────────────────────
agt_pipe       ██████████████ 14
pipeline_6a8d  █ 1
pipeline_173b  █ 1
pipeline_0e7b  █ 1
```

***

#### Prompt 3: What stages fail most often across all my pipelines? <a href="#prompt-3-what-stages-fail-most-often-across-all-my-pipelines" id="prompt-3-what-stages-fail-most-often-across-all-my-pipelines"></a>

**What it does**: Identifies problematic stages across all pipelines for cross-pipeline insights.

**Graph traversal**:

```
Pipeline Execution
 └── Stage Execution
      ├── status: FAILED
      ├── stage_name
      └── pipeline_id
```

**HQL query**:

```hql
find pipeline:stage_execution
  | where status = "FAILED"
  | where start_ts > ago("30d")
  | group by stage_name, pipeline_id
  | count
  | order by count desc
```

**Sample output**:

| Stage    | Pipeline       | Failures |
| -------- | -------------- | -------- |
| agt      | agt\_pipe      | 11       |
| Deploy   | agt\_pipe      | 4        |
| Build\_0 | pipeline\_0e7b | 1        |
| Stage    | pipeline\_173b | 1        |

**Visual representation**:

```
Cross-Pipeline Stage Failures
──────────────────────────────
agt (agt_pipe)          ███████████ 11 failures
Deploy (agt_pipe)       ████ 4 failures
Build_0 (pipeline_0e7b) █ 1 failure
Stage (pipeline_173b)   █ 1 failure
```

***

#### Prompt 4: Show me the top 5 error messages from failed pipelines <a href="#prompt-4-show-me-the-top-5-error-messages-from-failed-pipelines" id="prompt-4-show-me-the-top-5-error-messages-from-failed-pipelines"></a>

**What it does**: Surfaces the most common error messages to identify systemic issues.

**Graph traversal**:

```
Pipeline Execution
 └── Stage Execution
      └── Step Execution
           ├── status: FAILED
           ├── failure_message
           └── count
```

**HQL query**:

```hql
find pipeline:step_execution
  | where status = "FAILED"
  | where start_ts > ago("7d")
  | where failure_message != null
  | group by failure_message
  | count
  | order by count desc
  | limit 5
```

**Sample output**:

| Error Message                                                                          | Count |
| -------------------------------------------------------------------------------------- | ----- |
| Pull access denied for nodejs, repository does not exist or may require 'docker login' | 1,793 |
| Exit status 1 (generic shell error)                                                    | 78    |
| Hosted infrastructure connector misconfiguration                                       | 51    |
| Exit status 122                                                                        | 22    |
| No eligible runners found                                                              | 18    |

**Visual representation**:

```
Top Error Messages (This Week)
───────────────────────────────
Docker pull failure  ████████████████████ 1,793 (76%)
Exit status 1        ███ 78 (3%)
Connector config     ██ 51 (2%)
Exit status 122      █ 22 (1%)
No runners           █ 18 (1%)
```

***

#### Prompt 5: Recommend fixes for my most-failing pipeline <a href="#prompt-5-recommend-fixes-for-my-most-failing-pipeline" id="prompt-5-recommend-fixes-for-my-most-failing-pipeline"></a>

**What it does**: Analyzes error patterns and provides actionable remediation recommendations.

**Graph traversal**:

```
Pipeline Execution
 ├── failure count
 └── Step Execution
      ├── failure_message
      ├── failure_code
      └── step_type
```

**HQL queries**:

```hql
# Query 1: Find most-failing pipeline <a href="#query-1-find-most-failing-pipeline" id="query-1-find-most-failing-pipeline"></a>
find pipeline:pipeline_execution
  | where status = "FAILED"
  | where start_ts > ago("30d")
  | group by pipeline_id
  | count
  | order by count desc
  | limit 1

# Query 2: Analyze error patterns <a href="#query-2-analyze-error-patterns" id="query-2-analyze-error-patterns"></a>
find pipeline:step_execution
  | where pipeline_id = "<most_failing_pipeline>"
  | where status = "FAILED"
  | where start_ts > ago("30d")
  | group by step_name, failure_message
  | count
  | order by count desc
```

**Sample output**:

| Step                       | Error                     | Count | Recommendation                                          |
| -------------------------- | ------------------------- | ----- | ------------------------------------------------------- |
| runEvals                   | exit status 6             | 7     | Replace placeholder API credentials with actual secrets |
| runFeatureFlagCleanupAgent | exit status 1             | 4     | Verify LLM connector configuration and credentials      |
| InitializeContainer        | No eligible runners found | 2     | Check delegate health and connectivity                  |

**Recommendations**:

1. Fix placeholder credentials in Agent steps (resolves 7 failures)
2. Validate LLM connector configuration (resolves 4 failures)
3. Ensure delegate availability (resolves 2 failures)

***

### Build time analysis <a href="#build-time-analysis" id="build-time-analysis"></a>

Analyze build performance, identify bottlenecks, and optimize CI pipelines.

#### Prompt 6: Which builds are taking the longest? <a href="#prompt-6-which-builds-are-taking-the-longest" id="prompt-6-which-builds-are-taking-the-longest"></a>

**What it does**: Identifies slow pipelines and surfaces stage/step bottlenecks with optimization recommendations.

**Graph traversal**:

```
CI Stage Execution
 ├── pipeline_id
 ├── stage_build_time
 └── Step Execution
      ├── step_name
      ├── step_type
      └── duration
```

**HQL queries**:

```hql
# Query 1: Slowest pipelines <a href="#query-1-slowest-pipelines" id="query-1-slowest-pipelines"></a>
find pipeline:pipeline_execution
  | where status = "SUCCEEDED"
  | where start_ts > ago("30d")
  | group by pipeline_id
  | avg duration
  | p95 duration
  | max duration
  | count
  | order by avg_duration desc

# Query 2: Slowest step types <a href="#query-2-slowest-step-types" id="query-2-slowest-step-types"></a>
find pipeline:step_execution
  | where status = "SUCCEEDED"
  | where start_ts > ago("30d")
  | group by step_type
  | avg duration
  | p95 duration
  | max duration
  | count
  | order by avg_duration desc
```

**Sample output**:

**Slowest pipelines**:

| Pipeline          | Avg Duration | P95 Duration | Max Duration | Runs |
| ----------------- | ------------ | ------------ | ------------ | ---- |
| Code Coverage     | 151 min      | 164 min      | 164 min      | 4    |
| Worker Agent Demo | 27 min       | 68 min       | 68 min       | 10   |
| autofix           | 17 min       | 43 min       | 43 min       | 3    |

**Slowest step types**:

| Step Type           | Avg Duration | Max Duration |
| ------------------- | ------------ | ------------ |
| Barrier             | 164 min      | 600 min      |
| HarnessApproval     | 119 min      | 600 min      |
| Background          | 66 sec       | 5.8 min      |
| InitializeContainer | 53 sec       | 2.6 min      |
| Run                 | 4.6 sec      | 164 min      |

**Recommendations**:

* Parallelize AI agent workloads in Code Coverage pipeline
* Reduce approval wait times with notifications
* Optimize Windows build initialization
* Enable caching for Maven builds

***

#### Prompt 7: What are my slowest builds in the last 30 days? <a href="#prompt-7-what-are-my-slowest-builds-in-the-last-30-days" id="prompt-7-what-are-my-slowest-builds-in-the-last-30-days"></a>

**What it does**: Ranks successful builds by duration to prioritize optimization efforts.

**Graph traversal**:

```
Pipeline Execution
 ├── status: SUCCEEDED
 ├── duration
 └── pipeline_id
```

**HQL query**:

```hql
find pipeline:pipeline_execution
  | where status = "SUCCEEDED"
  | where start_ts > ago("30d")
  | group by pipeline_id
  | avg duration
  | p95 duration
  | max duration
  | count
  | order by avg_duration desc
```

**Sample output**:

| Pipeline          | Avg Duration | P95 Duration | Max Duration | Runs |
| ----------------- | ------------ | ------------ | ------------ | ---- |
| test-mcp-functor  | 2m 3s        | 2m 3s        | 2m 3s        | 1    |
| pipeline\_7d66    | 1m 34s       | 4m 54s       | 4m 54s       | 9    |
| test\_tracing\_v0 | 1m 23s       | 1m 44s       | 1m 44s       | 5    |

**Visual representation**:

```
Slowest Successful Builds
──────────────────────────
test-mcp-functor   ████ 2m 3s (1 run)
pipeline_7d66      ███ 1m 34s avg, 4m 54s P95 (high variance)
test_tracing_v0    ██ 1m 23s (5 runs)
```

***

#### Prompt 8: Which build stage takes the longest in my pipeline? <a href="#prompt-8-which-build-stage-takes-the-longest-in-my-pipeline" id="prompt-8-which-build-stage-takes-the-longest-in-my-pipeline"></a>

**What it does**: Identifies bottleneck stages within a specific pipeline.

**Graph traversal**:

```
Pipeline: <pipeline_id>
 └── Stage Execution
      ├── stage_name
      ├── duration
      └── status
```

**HQL query**:

```hql
find pipeline:stage_execution
  | where pipeline_id = "agt_pipe"
  | where start_ts > ago("30d")
  | group by stage_name
  | avg duration
  | p95 duration
  | max duration
  | count
  | order by avg_duration desc
```

**Sample output**:

| Stage  | Avg Duration | P95 Duration | Max Duration | Runs |
| ------ | ------------ | ------------ | ------------ | ---- |
| agt    | 101.3 sec    | 242.1 sec    | 248.6 sec    | 16   |
| Deploy | 25.8 sec     | 159.4 sec    | 159.4 sec    | 8    |

**Visual representation**:

```
Pipeline: agt_pipe - Stage Duration
────────────────────────────────────
agt stage:    ████████████████████ 101.3s (bottleneck)
Deploy stage: █████ 25.8s

Recommendation: Investigate agt stage for optimization
```

***

#### Prompt 9: Why did my build time increase by 40% last week? <a href="#prompt-9-why-did-my-build-time-increase-by-40percent-last-week" id="prompt-9-why-did-my-build-time-increase-by-40percent-last-week"></a>

**What it does**: Detects temporal regressions by comparing week-over-week build times and identifying root causes.

**Graph traversal**:

```
CI Stage Execution
 ├── start_ts: week 1 vs week 2
 ├── stage_build_time
 └── init_time
```

**HQL queries**:

```hql
# Query 1: Previous week average <a href="#query-1-previous-week-average" id="query-1-previous-week-average"></a>
find ci:stage_execution
  | where status = "SUCCEEDED"
  | where start_ts > ago("14d")
  | where start_ts < ago("7d")
  | group by pipeline_id
  | avg stage_build_time

# Query 2: Current week average <a href="#query-2-current-week-average" id="query-2-current-week-average"></a>
find ci:stage_execution
  | where status = "SUCCEEDED"
  | where start_ts > ago("7d")
  | group by pipeline_id
  | avg stage_build_time

# Query 3: Init time analysis <a href="#query-3-init-time-analysis" id="query-3-init-time-analysis"></a>
find ci:stage_execution
  | where status = "SUCCEEDED"
  | where start_ts > ago("14d")
  | group by pipeline_id
  | avg init_time
  | max init_time
```

**Sample output**:

| Time Period  | Avg Build Time | Avg Init Time |
| ------------ | -------------- | ------------- |
| Week 2-3 ago | 66 sec         | 5-6 sec       |
| Week 1-2 ago | 86 sec         | 5-35 sec      |

**Root cause**: Init time variance increased from consistent \~5-6 sec to 5-35 sec.

**Visual representation**:

```
Build Time Regression Analysis
───────────────────────────────
Week 2-3 ago:  ████████████ 66s (5-6s init)
Week 1-2 ago:  ████████████████ 86s (5-35s init)
                               ↑
                        +30% regression
                        
Root cause: Init time spiked to 35s (6x normal)
Recommendation: Check infrastructure availability
```

***

#### Prompt 10: Which test suites are the biggest bottleneck? <a href="#prompt-10-which-test-suites-are-the-biggest-bottleneck" id="prompt-10-which-test-suites-are-the-biggest-bottleneck"></a>

**What it does**: Identifies slow test suites and test execution bottlenecks in CI pipelines.

**Graph traversal**:

```
CI Stage Execution
 ├── stage_name: test stages
 ├── stage_build_time
 └── Test Execution
      ├── test_suite
      └── duration
```

**HQL queries**:

```hql
# Query 1: Test suite bottlenecks <a href="#query-1-test-suite-bottlenecks" id="query-1-test-suite-bottlenecks"></a>
find ci:test_execution
  | where start_ts > ago("30d")
  | group by test_suite_name
  | avg duration
  | max duration
  | count
  | order by avg_duration desc
  | limit 10

# Query 2: CI stage bottlenecks (if no test data) <a href="#query-2-ci-stage-bottlenecks-if-no-test-data" id="query-2-ci-stage-bottlenecks-if-no-test-data"></a>
find ci:stage_execution
  | where start_ts > ago("30d")
  | group by stage_name, pipeline_id
  | avg stage_build_time
  | max stage_build_time
  | avg init_time
  | count
  | order by avg_stage_build_time desc
```

**Sample output**:

**CI Stage Bottlenecks**:

| Stage | Pipeline | Avg Build Time | Max Build Time | Avg Init Time | Runs |
| ----- | -------- | -------------- | -------------- | ------------- | ---- |
| agt   | -        | 101.1s         | 248.3s         | 22.4s         | 16   |
| test  | -        | 79.0s          | 93.6s          | 15.3s         | 6    |

**Recommendations**:

* Enable Test Intelligence on test stages
* Investigate agt stage for optimization opportunities
* Reduce init times (15-33s is high)

***

#### Prompt 11: Which builds have the most cache misses? <a href="#prompt-11-which-builds-have-the-most-cache-misses" id="prompt-11-which-builds-have-the-most-cache-misses"></a>

**What it does**: Identifies pipelines without caching enabled to prioritize optimization.

**Graph traversal**:

```
CI Stage Execution
 ├── optimization_state: NONE
 ├── pipeline_id
 └── stage_build_time
```

**HQL query**:

```hql
find ci:stage_execution
  | where optimization_state = "NONE"
  | where start_ts > ago("30d")
  | group by pipeline_id
  | count
  | sum stage_build_time
  | order by count desc
```

**Sample output**:

| Pipeline       | Cache Misses | Total Build Time Wasted |
| -------------- | ------------ | ----------------------- |
| agt\_pipe      | 16           | 26m 58s                 |
| pipeline\_7d66 | 10           | 5m 39s                  |
| pipeline\_0e7b | 2            | 3s                      |

**Visual representation**:

```
Cache Miss Distribution (Last 30 Days)
───────────────────────────────────────
agt_pipe:      ████████████████ 16 misses (53%)
pipeline_7d66: ██████████ 10 misses (33%)
pipeline_0e7b: █ 2 misses (7%)

Total wasted time: 32m 40s
Recommendation: Enable Cache Intelligence on agt_pipe
```

***

### Pipeline productivity <a href="#pipeline-productivity" id="pipeline-productivity"></a>

Get actionable recommendations to improve pipeline efficiency and reduce costs.

#### Prompt 12: What are the top recommendations to improve pipeline productivity? <a href="#prompt-12-what-are-the-top-recommendations-to-improve-pipeline-productivity" id="prompt-12-what-are-the-top-recommendations-to-improve-pipeline-productivity"></a>

**What it does**: Analyzes pipeline execution patterns and provides ranked optimization recommendations.

**Graph traversal**:

```
Pipeline Execution
 ├── Cache miss analysis
 ├── Approval wait times
 ├── Step durations
 └── Failure patterns
```

**HQL queries**:

```hql
# Query 1: Cache miss opportunities <a href="#query-1-cache-miss-opportunities" id="query-1-cache-miss-opportunities"></a>
find ci:stage_execution
  | where optimization_state = "NONE"
  | where start_ts > ago("30d")
  | group by pipeline_id
  | count
  | sum stage_build_time
  | order by sum_stage_build_time desc

# Query 2: Long-running steps <a href="#query-2-long-running-steps" id="query-2-long-running-steps"></a>
find pipeline:step_execution
  | where start_ts > ago("30d")
  | group by step_type
  | avg duration
  | count
  | order by avg_duration desc

# Query 3: High failure rate pipelines <a href="#query-3-high-failure-rate-pipelines" id="query-3-high-failure-rate-pipelines"></a>
find pipeline:pipeline_execution
  | where start_ts > ago("30d")
  | group by pipeline_id, status
  | count
```

**Sample recommendations**:

1. **Enable caching** on agt\_pipe (saves 27 min/week)
2. **Reduce approval wait times** in Worker Agent Demo (avg 71 min)
3. **Fix high-failure pipelines** (agt\_pipe at 66.7% failure rate)
4. **Parallelize background services** in multibg pipeline

***

#### Prompt 13: Where am I wasting the most compute time on cache misses? <a href="#prompt-13-where-am-i-wasting-the-most-compute-time-on-cache-misses" id="prompt-13-where-am-i-wasting-the-most-compute-time-on-cache-misses"></a>

**What it does**: Quantifies time and cost wasted due to missing or disabled caching.

**Graph traversal**:

```
CI Stage Execution
 ├── optimization_state: NONE
 ├── stage_build_time
 └── pipeline_id
```

**HQL query**:

```hql
find ci:stage_execution
  | where optimization_state = "NONE"
  | where start_ts > ago("30d")
  | group by pipeline_id
  | count
  | sum stage_build_time
  | order by sum_stage_build_time desc
```

**Sample output**:

| Pipeline       | Cache Misses | Wasted Time | % of Total Waste |
| -------------- | ------------ | ----------- | ---------------- |
| agt\_pipe      | 16           | 26m 58s     | 53%              |
| pipeline\_7d66 | 10           | 5m 39s      | 33%              |
| pipeline\_0e7b | 2            | 3s          | 7%               |

**Total wasted time**: 32m 40s across 30 days

**Recommendation**: Enable Cache Intelligence on agt\_pipe to save 27 min/week

***

#### Prompt 14: How can I reduce my CI costs without increasing build time? <a href="#prompt-14-how-can-i-reduce-my-ci-costs-without-increasing-build-time" id="prompt-14-how-can-i-reduce-my-ci-costs-without-increasing-build-time"></a>

**What it does**: Identifies cost optimization opportunities that don't compromise build speed.

**Validation**: ✅ Validated - 100% success rate

**Graph traversal**:

```
Pipeline Execution
 ├── Resource usage patterns
 ├── Cache opportunities
 └── Parallelization potential
```

**HQL queries**:

```hql
# Query 1: Cache Intelligence opportunities <a href="#query-1-cache-intelligence-opportunities" id="query-1-cache-intelligence-opportunities"></a>
find ci:stage_execution
  | where optimization_state = "NONE"
  | where start_ts > ago("30d")
  | group by pipeline_id
  | count
  | sum stage_build_time

# Query 2: Serial vs parallel opportunities <a href="#query-2-serial-vs-parallel-opportunities" id="query-2-serial-vs-parallel-opportunities"></a>
find pipeline:stage_execution
  | where start_ts > ago("30d")
  | group by pipeline_id
  | avg duration
  | count
```

**Sample recommendations**:

1. **Enable Cache Intelligence**: Saves 32m 40s/month (53% on agt\_pipe)
2. **Use Harness Cloud**: Reduce delegate overhead (saves infrastructure costs)
3. **Parallelize stages**: Reduce wall-clock time without adding compute
4. **Right-size runners**: Match compute to workload needs

***

### Flaky test identification <a href="#flaky-test-identification" id="flaky-test-identification"></a>

Track test reliability, identify flaky tests, and measure retry costs.

#### Prompt 15: Which tests are flaky in my CI pipelines? <a href="#prompt-15-which-tests-are-flaky-in-my-ci-pipelines" id="prompt-15-which-tests-are-flaky-in-my-ci-pipelines"></a>

**What it does**: Identifies tests that fail intermittently and calculates retry cost.

**Validation**: ✅ Validated - 100% success rate

**Graph traversal**:

```
Test Execution
 ├── test_name
 ├── status: FAILED then SUCCEEDED
 └── retry_count
```

**HQL query**:

```hql
find ci:test_execution
  | where start_ts > ago("30d")
  | group by test_name, pipeline_id
  | count
  | where retry_count > 0
  | order by retry_count desc
```

**Sample output**:

| Test Name                      | Pipeline     | Failures | Retries | Success Rate |
| ------------------------------ | ------------ | -------- | ------- | ------------ |
| integration.api.test\_timeout  | api-service  | 12       | 24      | 66%          |
| e2e.checkout.flaky\_assertion  | web-frontend | 8        | 16      | 75%          |
| unit.database.connection\_pool | backend      | 5        | 10      | 80%          |

**Total retry cost**: 45 min/week wasted on retries

***

#### Prompt 16: Which tests fail intermittently across my pipelines? <a href="#prompt-16-which-tests-fail-intermittently-across-my-pipelines" id="prompt-16-which-tests-fail-intermittently-across-my-pipelines"></a>

**What it does**: Identifies tests with inconsistent pass/fail patterns.

**Validation**: ✅ Validated - 100% success rate

**Graph traversal**:

```
Test Execution
 ├── test_name
 ├── pass count
 └── fail count
```

**HQL query**:

```hql
find ci:test_execution
  | where start_ts > ago("30d")
  | group by test_name
  | count by status
  | where fail_count > 0 AND pass_count > 0
  | order by fail_count desc
```

**Sample output**:

| Test Name                      | Passed | Failed | Flake Rate |
| ------------------------------ | ------ | ------ | ---------- |
| integration.api.test\_timeout  | 8      | 12     | 60% fail   |
| e2e.checkout.flaky\_assertion  | 12     | 8      | 40% fail   |
| unit.database.connection\_pool | 15     | 5      | 25% fail   |

***

#### Prompt 17: How many build minutes are wasted on retries per week? <a href="#prompt-17-how-many-build-minutes-are-wasted-on-retries-per-week" id="prompt-17-how-many-build-minutes-are-wasted-on-retries-per-week"></a>

**What it does**: Calculates the cost of test retries across all pipelines.

**Graph traversal**:

```
Test Execution
 ├── retry_count
 └── duration
```

**HQL query**:

```hql
find ci:test_execution
  | where start_ts > ago("7d")
  | where retry_count > 0
  | sum duration
```

**Sample output**:

| Week        | Retry Count | Wasted Time |
| ----------- | ----------- | ----------- |
| Current     | 156         | 45 min      |
| Previous    | 142         | 38 min      |
| 2 weeks ago | 135         | 41 min      |

**Average**: 41 min/week wasted on test retries

***

#### Prompt 18: Which flaky tests are getting worse over time? <a href="#prompt-18-which-flaky-tests-are-getting-worse-over-time" id="prompt-18-which-flaky-tests-are-getting-worse-over-time"></a>

**What it does**: Tracks flaky test trends to identify degrading test quality.

**Graph traversal**:

```
Test Execution
 ├── test_name
 ├── start_ts (time buckets)
 └── failure_rate by week
```

**HQL query**:

```hql
find ci:test_execution
  | where start_ts > ago("60d")
  | group by test_name, week_bucket(start_ts)
  | count by status
  | order by test_name, week_bucket
```

**Sample output**:

| Test Name                     | 4 weeks ago | 3 weeks ago | 2 weeks ago | Last week | Trend        |
| ----------------------------- | ----------- | ----------- | ----------- | --------- | ------------ |
| integration.api.test\_timeout | 20% fail    | 40% fail    | 50% fail    | 60% fail  | 📈 Worsening |
| e2e.checkout.flaky\_assertion | 50% fail    | 45% fail    | 40% fail    | 40% fail  | 📉 Improving |

***

#### Prompt 19: What's causing my integration tests to be flaky? <a href="#prompt-19-whats-causing-my-integration-tests-to-be-flaky" id="prompt-19-whats-causing-my-integration-tests-to-be-flaky"></a>

**What it does**: Analyzes flaky test patterns to identify root causes (timing, resources, isolation).

**Graph traversal**:

```
Test Execution (flaky tests)
 ├── failure_message patterns
 ├── execution time variance
 └── infrastructure correlation
```

**HQL queries**:

```hql
# Query 1: Common failure messages for flaky tests <a href="#query-1-common-failure-messages-for-flaky-tests" id="query-1-common-failure-messages-for-flaky-tests"></a>
find ci:test_execution
  | where retry_count > 0
  | where start_ts > ago("30d")
  | group by failure_message
  | count
  | order by count desc

# Query 2: Timing variance <a href="#query-2-timing-variance" id="query-2-timing-variance"></a>
find ci:test_execution
  | where test_name in [<flaky_tests>]
  | group by test_name
  | avg duration
  | stddev duration
```

**Sample root causes**:

| Test Name                      | Root Cause          | Evidence                            |
| ------------------------------ | ------------------- | ----------------------------------- |
| integration.api.test\_timeout  | Timing issue        | High duration variance (2s - 35s)   |
| e2e.checkout.flaky\_assertion  | Resource contention | Fails more on shared runners        |
| unit.database.connection\_pool | Test isolation      | Fails when run after specific tests |

**Recommendations**:

* Increase timeouts for timing-sensitive tests
* Use dedicated runners for resource-intensive tests
* Improve test isolation and cleanup

***

### Related articles <a href="#related-articles" id="related-articles"></a>

* [Knowledge Graph](/harness-platform/use-harness-platform/knowledge-graph/overview.md) - Understand entities and relationships
* [DevOps Agent](/harness-ai/untitled-1/use-harness-ai/ai-agents/devops-agent.md) - Ask these questions in natural language
* [Harness AI Overview](https://github.com/iKettles/harness-gitbook/tree/main/docs/platform/harness-ai/overview/README.md) - Explore AI features powered by the Knowledge Graph

***