Fixed typo errors and sentence framing

docs/contributing-to-metric-library.md

@@ -4,25 +4,25 @@
 
 ## Overview
 
-Legend supports a wide variety of metric types for creating panels for "components" like SQS, Promtail, Loki, EC2 and many more. Refer to [sample_input.yaml](../sample_input.yaml) for getting to know all the supported metrics.
+Legend supports a wide variety of metric types for creating panels for "components" like SQS, Promtail, Loki, EC2, and many more. Refer to [sample_input.yaml](../sample_input.yaml) for getting to know all the supported metrics.
 
-But if your use-case requires a metric component type which is currently not supported by legend, then you can refer to the following guide to add metrics to legend.
+But if your use-case requires a metric component type that is currently not supported by legend, then you can refer to the following guide to add metrics to legend.
 
 ## Metrics library
 
 All the metrics plotted per component are part of the metrics library which lives within legend at `legend/metrics_library/metrics`.
 Each component has an associated metrics file in the metrics library in the format
-`<component>_metrics.yaml`. The metrics file is a Jinja2 template which is rendered to
+`<component>_metrics.yaml`. The metrics file is a Jinja2 template that is rendered to
 a yaml file.
 
 ## Spec
 
 The input file has to be written based on the data source of the metrics.
-Each component is seperate with a `row` panel in grafana, and the panels within each of these
-consists of one or more `targets`. These are the actual graphs which are plotted in Grafana.
+Each component is separate with a `row` panel in grafana, and the panels within each of these
+consist of one or more `targets`. These are the actual graphs that are plotted in Grafana.
 
 Legend uses [grafonnet-lib](https://github.com/grafana/grafonnet-lib/tree/v0.1.0) internally to generate the
-dashboard's json and apply it to Grafana.
+dashboard's JSON and apply it to Grafana.
 
 ### Basic spec
 
@@ -35,46 +35,48 @@ alert_config: # Configuration to add notification channels and tags to alerts
   tags: # Tags for your alerts
     key: value
 references: # Reference link to the components documentation
-description: # Short description on what the component does
+description: # Short description of what the component does
 ```
 
 ### Panels
 
 ```yaml
 panels: # The metrics to be plotted
 - title: # Title of the panel
-    type: # Type of the panel in Grafana. Currently only 'graph' is supported in legend
+    type: # Type of the panel in Grafana. Currently, only 'graph' is supported in legend
     description: # Describe what the panel represents
     targets:
     {% for dimension in data %} # The 'dimensions' dict from the input file is passed to the targets
-      - metric: django_http_responses_total_by_status_total{job=~"{{ dimension.job }}"} # metric to be plotted. You can use jinja2 templating fill in the vars passed in the input file
+      - metric: django_http_responses_total_by_status_total{job=~"{{ dimension.job }}"} # metric to be plotted. You can use the jinja2 template to fill in the vars passed in the input file
         legend: # Legend to be displayed in the panel. Optional.
-        ref_no: 1 # Reference number which is used in the alert config. Do not confuse with the ref_id which Grafana creates. 'ref_no' is internal, but when the grafana dashboard is created grafana actually creates a ref id (from A to Z). Legend associates the ref_no to the ref_id and sets the appropriate alert rule on the metric
+        ref_no: 1 # Reference number which is used in the alert config. Do not confuse with the ref_id which Grafana creates. 'ref_no' is internal, but when the grafana dashboard is created grafana creates a ref id (from A to Z). Legend associates the ref_no to the ref_id and sets the appropriate alert rule on the metric
       - metric: # second metric
         legend: '{{ '{{instance}}' }}'
         ref_no: 2 # Incremenatal ref_no, used to associate alerts to this particular metric
     {% endfor %}
     alert_config: # Alert config
-      priority: # Priority of the alert. Must be one of P1-P5. This is configured as a tag in grafana with the key:value - og_priority:<priority>. This priority is associated to the alert/incident in opsgenie automatically
+      priority: # Priority of the alert. Must be one of P1-P5. This is configured as a tag in grafana with the key:value - og_priority:<priority>. This priority is associated with the alert/incident in opsgenie automatically
       message: # Alerting message
       rule: # Alerting rule, follows the alerting rules from Grafana
         for_duration: 5m # Sample
         evaluate_every: 10s # Sample
       condition_query: # The list of condition queries, evaluated per target. Follows the same format as described in Grafana alerts
-      - OR,avg,1,now,5m,gt,20 # The first condition is automatically converted to 'WHEN' when the alert if being configured in grafana. The ref_no of the target must be filled in the third field to reference which target has to be evaluated against this rule. 
+      - OR,avg,1, now,5m,gt,20 # The first condition is automatically converted to 'WHEN' when the alert is being configured in grafana. The ref_no of the target must be filled in the third field to reference which target has to be evaluated against this rule. 
       - OR,avg,2,now,5m,gt,30
 
 ```
 
 ### Addtional Spec
 
 ``` yaml
-formatY1: # The format of the data in the Y1 axis, this follows the Grafana standard (sample : Bps, bytes, s, percent)
-labelY1:  # The label to put in the Y1 graph panel (sample : bytes/sec , bytes , seconds, percent)
+formatY1: # The format of the data in the Y1 axis, follows the Grafana standard (sample: Bps, bytes, s, percent)
+labelY1:  # The label to put in the Y1 graph panel (sample: bytes/sec, bytes, seconds, percent)
 ```
 
 ## Adding metrics for new components
 
 * If you are adding metrics for a new component, please follow the spec as mentioned above and name the Jinja2 file of your metric with the following convention `<component name>_metrics.j2` and update docs [here](docs/enabling-monitoring.md).
-* The variables and the component spec has to be added in the [metrics_schema](../legend/metrics_library/metrics_schema.py) and import the configuration into the [schema](../legend/metrics_library/schema.py) to ensure proper validation - this will enable input validation.
+* The variables and the component spec have to be added in the [metrics_schema](../legend/metrics_library/metrics_schema.py) and import the configuration into the [schema](../legend/metrics_library/schema.py) to ensure proper validation - this will enable input validation.
 * In the [sample_input.yaml](../sample_input.yaml) add the component with basic/sample configuration - this will enable testing and ensure backward compatibility also making adaptability easy.
+
+