custom-metrics

Name: custom-metrics
Author: launchdarkly

20stars6forks

How to install custom-metrics?

launchdarkly/ai-tooling/custom-metrics

$npx -y skills add launchdarkly/ai-tooling --skill custom-metrics

Installs into the current project.

Summary

Create, track, retrieve, update, and delete custom business metrics for configs. Covers full lifecycle: define metric kinds via API, emit events via SDK, and query results.

Files · 1

View on GitHub

SKILL.md

1	# Custom Metrics for Configs
2
3	Full lifecycle management of custom business metrics: create metric definitions via API, track events via SDK, retrieve metric data, and manage metrics programmatically.
4
5	## Prerequisites
6
7	- LaunchDarkly SDK initialized (see `sdk`)
8	- LaunchDarkly API token with `writer` role for metric management
9	- Understanding of built-in agent metrics (see `built-in-metrics`)
10
11	## API Key Detection
12
13	Before prompting the user for an API key, try to detect it automatically:
14
15	1. Check Claude MCP config - Read `~/.claude/config.json` and look for `mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY`
16	2. Check environment variables - Look for `LAUNCHDARKLY_API_KEY`, `LAUNCHDARKLY_API_TOKEN`, or `LD_API_KEY`
17	3. Prompt user - Only if detection fails, ask the user for their API key
18
19	```python
20	import os
21	import json
22	from pathlib import Path
23
24	def get_launchdarkly_api_key():
25	"""Auto-detect LaunchDarkly API key from Claude config or environment."""
26	# 1. Check Claude MCP config
27	claude_config = Path.home() / ".claude" / "config.json"
28	if claude_config.exists():
29	try:
30	config = json.load(open(claude_config))
31	api_key = config.get("mcpServers", {}).get("launchdarkly", {}).get("env", {}).get("LAUNCHDARKLY_API_KEY")
32	if api_key:
33	return api_key
34	except (json.JSONDecodeError, IOError):
35	pass
36
37	# 2. Check environment variables
38	for var in ["LAUNCHDARKLY_API_KEY", "LAUNCHDARKLY_API_TOKEN", "LD_API_KEY"]:
39	if os.environ.get(var):
40	return os.environ[var]
41
42	return None
43	```
44
45	## Metrics Lifecycle Overview
46
47	\| Step \| Method \| Purpose \|
48	\|------\|--------\|---------\|
49	\| 1. Create \| API \| Define metric in LaunchDarkly \|
50	\| 2. Track \| SDK \| Send events to the metric \|
51	\| 3. Get \| API \| Retrieve metric definition/data \|
52	\| 4. Update \| API \| Modify metric properties \|
53	\| 5. Delete \| API \| Remove metric \|
54
55	## 1. Create Metric (API)
56
57	Required fields for numeric custom metrics:
58	- `successCriteria` - Must be one of: `"HigherThanBaseline"`, `"LowerThanBaseline"`
59	- `unit` - e.g., `"count"`, `"percent"`, `"milliseconds"`
60
61	The API will return `400 Bad Request` if these are missing for numeric metrics.
62
63	```python
64	import requests
65	import os
66
67	def create_metric(
68	project_key: str,
69	metric_key: str,
70	name: str,
71	kind: str = "custom",
72	is_numeric: bool = True,
73	unit: str = "count",
74	success_criteria: str = "HigherThanBaseline",
75	event_key: str = None,
76	description: str = None
77	):
78	"""Create a new metric definition in LaunchDarkly."""
79	API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")
80
81	url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}"
82
83	payload = {
84	"key": metric_key,
85	"name": name,
86	"kind": kind,
87	"isNumeric": is_numeric,
88	"eventKey": event_key or metric_key
89	}
90
91	# Unit and successCriteria are required for numeric custom metrics
92	if is_numeric and kind == "custom":
93	payload["unit"] = unit
94	payload["successCriteria"] = success_criteria
95
96	if description:
97	payload["description"] = description
98
99	headers = {
100	"Authorization": API_TOKEN,
101	"Content-Type": "application/json"
102	}
103
104	response = requests.post(url, json=payload, headers=headers)
105
106	if response.status_code == 201:
107	print(f"[OK] Created metric: {metric_key}")
108	return response.json()
109	elif response.status_code == 409:
110	print(f"[INFO] Metric already exists: {metric_key}")
111	return None
112	else:
113	print(f"[ERROR] Failed to create metric: {response.status_code}")
114	print(f" {response.text}")
115	return None
116	```
117
118	Metric Kinds:
119	- `custom` - Track any event (most common for agent metrics)
120	- `pageview` - Track page views
121	- `click` - Track click events
122
123	Success Criteria (for numeric metrics):
124	- `HigherThanBaseline` - Higher values are better (e.g., revenue, satisfaction)
125	- `LowerThanBaseline` - Lower values are better (e.g., errors, latency)
126
127	Common Units:
128	- `count` - Generic count
129	- `milliseconds` - Time duration
130	- `percent` - Percentage values
131	- `dollars` - Currency
132
133	## 2. Track Events (SDK)
134
135	Once the metric is created, track events using the SDK:
136
137	```python
138	from ldclient import Context
139	from ldclient.config import Config
140	import ldclient
141
142	# Initialize (see sdk for details)
143	ldclient.set_config(Config("your-sdk-key"))
144	ld_client = ldclient.get()
145
146	def track_metric(ld_client, user_id: str, metric_key: str, value: float, data: dict = None):
147	"""Tra

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custom-metrics

20stars6forks

View on GitHub

How to install custom-metrics?

launchdarkly/ai-tooling/custom-metrics

$npx -y skills add launchdarkly/ai-tooling --skill custom-metrics

Installs into the current project.

Summary

Create, track, retrieve, update, and delete custom business metrics for configs. Covers full lifecycle: define metric kinds via API, emit events via SDK, and query results.

Files · 1

View on GitHub

SKILL.md

1	# Custom Metrics for Configs
2
3	Full lifecycle management of custom business metrics: create metric definitions via API, track events via SDK, retrieve metric data, and manage metrics programmatically.
4
5	## Prerequisites
6
7	- LaunchDarkly SDK initialized (see `sdk`)
8	- LaunchDarkly API token with `writer` role for metric management
9	- Understanding of built-in agent metrics (see `built-in-metrics`)
10
11	## API Key Detection
12
13	Before prompting the user for an API key, try to detect it automatically:
14
15	1. Check Claude MCP config - Read `~/.claude/config.json` and look for `mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY`
16	2. Check environment variables - Look for `LAUNCHDARKLY_API_KEY`, `LAUNCHDARKLY_API_TOKEN`, or `LD_API_KEY`
17	3. Prompt user - Only if detection fails, ask the user for their API key
18
19	```python
20	import os
21	import json
22	from pathlib import Path
23
24	def get_launchdarkly_api_key():
25	"""Auto-detect LaunchDarkly API key from Claude config or environment."""
26	# 1. Check Claude MCP config
27	claude_config = Path.home() / ".claude" / "config.json"
28	if claude_config.exists():
29	try:
30	config = json.load(open(claude_config))
31	api_key = config.get("mcpServers", {}).get("launchdarkly", {}).get("env", {}).get("LAUNCHDARKLY_API_KEY")
32	if api_key:
33	return api_key
34	except (json.JSONDecodeError, IOError):
35	pass
36
37	# 2. Check environment variables
38	for var in ["LAUNCHDARKLY_API_KEY", "LAUNCHDARKLY_API_TOKEN", "LD_API_KEY"]:
39	if os.environ.get(var):
40	return os.environ[var]
41
42	return None
43	```
44
45	## Metrics Lifecycle Overview
46
47	\| Step \| Method \| Purpose \|
48	\|------\|--------\|---------\|
49	\| 1. Create \| API \| Define metric in LaunchDarkly \|
50	\| 2. Track \| SDK \| Send events to the metric \|
51	\| 3. Get \| API \| Retrieve metric definition/data \|
52	\| 4. Update \| API \| Modify metric properties \|
53	\| 5. Delete \| API \| Remove metric \|
54
55	## 1. Create Metric (API)
56
57	Required fields for numeric custom metrics:
58	- `successCriteria` - Must be one of: `"HigherThanBaseline"`, `"LowerThanBaseline"`
59	- `unit` - e.g., `"count"`, `"percent"`, `"milliseconds"`
60
61	The API will return `400 Bad Request` if these are missing for numeric metrics.
62
63	```python
64	import requests
65	import os
66
67	def create_metric(
68	project_key: str,
69	metric_key: str,
70	name: str,
71	kind: str = "custom",
72	is_numeric: bool = True,
73	unit: str = "count",
74	success_criteria: str = "HigherThanBaseline",
75	event_key: str = None,
76	description: str = None
77	):
78	"""Create a new metric definition in LaunchDarkly."""
79	API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")
80
81	url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}"
82
83	payload = {
84	"key": metric_key,
85	"name": name,
86	"kind": kind,
87	"isNumeric": is_numeric,
88	"eventKey": event_key or metric_key
89	}
90
91	# Unit and successCriteria are required for numeric custom metrics
92	if is_numeric and kind == "custom":
93	payload["unit"] = unit
94	payload["successCriteria"] = success_criteria
95
96	if description:
97	payload["description"] = description
98
99	headers = {
100	"Authorization": API_TOKEN,
101	"Content-Type": "application/json"
102	}
103
104	response = requests.post(url, json=payload, headers=headers)
105
106	if response.status_code == 201:
107	print(f"[OK] Created metric: {metric_key}")
108	return response.json()
109	elif response.status_code == 409:
110	print(f"[INFO] Metric already exists: {metric_key}")
111	return None
112	else:
113	print(f"[ERROR] Failed to create metric: {response.status_code}")
114	print(f" {response.text}")
115	return None
116	```
117
118	Metric Kinds:
119	- `custom` - Track any event (most common for agent metrics)
120	- `pageview` - Track page views
121	- `click` - Track click events
122
123	Success Criteria (for numeric metrics):
124	- `HigherThanBaseline` - Higher values are better (e.g., revenue, satisfaction)
125	- `LowerThanBaseline` - Lower values are better (e.g., errors, latency)
126
127	Common Units:
128	- `count` - Generic count
129	- `milliseconds` - Time duration
130	- `percent` - Percentage values
131	- `dollars` - Currency
132
133	## 2. Track Events (SDK)
134
135	Once the metric is created, track events using the SDK:
136
137	```python
138	from ldclient import Context
139	from ldclient.config import Config
140	import ldclient
141
142	# Initialize (see sdk for details)
143	ldclient.set_config(Config("your-sdk-key"))
144	ld_client = ldclient.get()
145
146	def track_metric(ld_client, user_id: str, metric_key: str, value: float, data: dict = None):
147	"""Tra

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