when-optimizing-prompts-use-prompt-architect

Name: when-optimizing-prompts-use-prompt-architect
Author: DNYoussef

32stars6forks

How to install when-optimizing-prompts-use-prompt-architect?

dnyoussef/context-cascade/when-optimizing-prompts-use-prompt-architect

$npx -y skills add DNYoussef/context-cascade --skill when-optimizing-prompts-use-prompt-architect

Installs into the current project.

Summary

/*============================================================================*/ /* WHEN-OPTIMIZING-PROMPTS-USE-PROMPT-ARCHITECT SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/

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SKILL.md

1	/============================================================================/
2	/* WHEN-OPTIMIZING-PROMPTS-USE-PROMPT-ARCHITECT SKILL :: VERILINGUA x VERIX EDITION */
3	/============================================================================/
4
5	---
6	name: when-optimizing-prompts-use-prompt-architect
7	version: 1.0.0
8	description: \|
9	[assert\|neutral] Comprehensive framework for analyzing, creating, and refining prompts for AI systems using evidence-based techniques [ground:given] [conf:0.95] [state:confirmed]
10	category: utilities
11	tags:
12	- prompt-engineering
13	- optimization
14	- ai-systems
15	- llm
16	author: ruv
17	cognitive_frame:
18	primary: compositional
19	goal_analysis:
20	first_order: "Execute when-optimizing-prompts-use-prompt-architect workflow"
21	second_order: "Ensure quality and consistency"
22	third_order: "Enable systematic utilities processes"
23	---
24
25	/----------------------------------------------------------------------------/
26	/* S0 META-IDENTITY */
27	/----------------------------------------------------------------------------/
28
29	[define\|neutral] SKILL := {
30	name: "when-optimizing-prompts-use-prompt-architect",
31	category: "utilities",
32	version: "1.0.0",
33	layer: L1
34	} [ground:given] [conf:1.0] [state:confirmed]
35
36	/----------------------------------------------------------------------------/
37	/* S1 COGNITIVE FRAME */
38	/----------------------------------------------------------------------------/
39
40	[define\|neutral] COGNITIVE_FRAME := {
41	frame: "Compositional",
42	source: "German",
43	force: "Build from primitives?"
44	} [ground:cognitive-science] [conf:0.92] [state:confirmed]
45
46	## Kanitsal Cerceve (Evidential Frame Activation)
47	Kaynak dogrulama modu etkin.
48
49	/----------------------------------------------------------------------------/
50	/* S2 TRIGGER CONDITIONS */
51	/----------------------------------------------------------------------------/
52
53	[define\|neutral] TRIGGER_POSITIVE := {
54	keywords: ["when-optimizing-prompts-use-prompt-architect", "utilities", "workflow"],
55	context: "user needs when-optimizing-prompts-use-prompt-architect capability"
56	} [ground:given] [conf:1.0] [state:confirmed]
57
58	/----------------------------------------------------------------------------/
59	/* S3 CORE CONTENT */
60	/----------------------------------------------------------------------------/
61
62	# Prompt Architect - Evidence-Based Prompt Engineering
63
64	## Kanitsal Cerceve (Evidential Frame Activation)
65	Kaynak dogrulama modu etkin.
66
67
68
69	## Overview
70
71	Comprehensive framework for analyzing, creating, and refining prompts for AI systems (Claude, GPT, etc.). Applies structural optimization, self-consistency patterns, and anti-pattern detection to transform prompts into highly effective versions.
72
73	## When to Use This Skill
74
75	- Creating new prompts for AI systems
76	- Existing prompts produce poor results
77	- Inconsistent AI outputs
78	- Need to improve prompt clarity
79	- Applying evidence-based prompt engineering
80	- Optimizing agent instructions
81	- Building prompt libraries
82
83	## Theoretical Foundation
84
85	### Evidence-Based Techniques
86
87	1. Chain-of-Thought (CoT): Explicit reasoning steps
88	2. Self-Consistency: Multiple reasoning paths
89	3. ReAct: Reasoning + Acting pattern
90	4. Program-of-Thought: Structured logic
91	5. Plan-and-Solve: Decomposition strategy
92	6. Role-Playing: Persona assignment
93	7. Few-Shot Learning: Example-based instruction
94
95	### Prompt Structure Principles
96
97	```
98	[System Context] → [Role Definition] → [Task Description] →
99	[Constraints] → [Format Specification] → [Examples] → [Quality Criteria]
100	```
101
102	## Phase 1: Analyze Current Prompt
103
104	### Objective
105	Identify weaknesses and improvement opportunities
106
107	### Agent: Researcher
108
109	Step 1.1: Structural Analysis
110	```javascript
111	const promptAnalysis = {
112	components: {
113	hasSystemContext: checkForContext(prompt),
114	hasRoleDefinition: checkForRole(prompt),
115	hasTaskDescription: checkForTask(prompt),
116	hasConstraints: checkForConstraints(prompt),
117	hasFormatSpec: checkForFormat(prompt),
118	hasExamples: checkForExamples(prompt),
119	hasQualityCriteria: checkForCriteria(prompt)
120	},
121	metrics: {
122	length: prompt.length,
123	clarity: calculateClarity(prompt),
124	specificity: calculateSpecificity(prompt),
125	completeness: calculateCompleteness(prompt)
126	},
127	antiPatterns: detectAntiPatterns(prompt)
128	};
129
130	await memory.store('prompt-architect/analysis', promptAnalysis);
131	```
132
133	Step 1.2: Detect Anti-Patterns
134	```javascript
135	const antiPatterns = [
136	{
137	name: 'Vague Instructions',
138	pattern: /please\|try to\|maybe\|possibly/gi,
139	severity: 'HIGH',
140	fix: 'Use imperative commands: "Analyze...", "Generate...", "Create..."'
141	},
142	{
143	name: 'Missing Context',
144	pattern: absence of background info,
145	severity: 'HIGH',
146	fix: 'Add sy

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when-optimizing-prompts-use-prompt-architect

32stars6forks

View on GitHub

How to install when-optimizing-prompts-use-prompt-architect?

dnyoussef/context-cascade/when-optimizing-prompts-use-prompt-architect

$npx -y skills add DNYoussef/context-cascade --skill when-optimizing-prompts-use-prompt-architect

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	/============================================================================/
2	/* WHEN-OPTIMIZING-PROMPTS-USE-PROMPT-ARCHITECT SKILL :: VERILINGUA x VERIX EDITION */
3	/============================================================================/
4
5	---
6	name: when-optimizing-prompts-use-prompt-architect
7	version: 1.0.0
8	description: \|
9	[assert\|neutral] Comprehensive framework for analyzing, creating, and refining prompts for AI systems using evidence-based techniques [ground:given] [conf:0.95] [state:confirmed]
10	category: utilities
11	tags:
12	- prompt-engineering
13	- optimization
14	- ai-systems
15	- llm
16	author: ruv
17	cognitive_frame:
18	primary: compositional
19	goal_analysis:
20	first_order: "Execute when-optimizing-prompts-use-prompt-architect workflow"
21	second_order: "Ensure quality and consistency"
22	third_order: "Enable systematic utilities processes"
23	---
24
25	/----------------------------------------------------------------------------/
26	/* S0 META-IDENTITY */
27	/----------------------------------------------------------------------------/
28
29	[define\|neutral] SKILL := {
30	name: "when-optimizing-prompts-use-prompt-architect",
31	category: "utilities",
32	version: "1.0.0",
33	layer: L1
34	} [ground:given] [conf:1.0] [state:confirmed]
35
36	/----------------------------------------------------------------------------/
37	/* S1 COGNITIVE FRAME */
38	/----------------------------------------------------------------------------/
39
40	[define\|neutral] COGNITIVE_FRAME := {
41	frame: "Compositional",
42	source: "German",
43	force: "Build from primitives?"
44	} [ground:cognitive-science] [conf:0.92] [state:confirmed]
45
46	## Kanitsal Cerceve (Evidential Frame Activation)
47	Kaynak dogrulama modu etkin.
48
49	/----------------------------------------------------------------------------/
50	/* S2 TRIGGER CONDITIONS */
51	/----------------------------------------------------------------------------/
52
53	[define\|neutral] TRIGGER_POSITIVE := {
54	keywords: ["when-optimizing-prompts-use-prompt-architect", "utilities", "workflow"],
55	context: "user needs when-optimizing-prompts-use-prompt-architect capability"
56	} [ground:given] [conf:1.0] [state:confirmed]
57
58	/----------------------------------------------------------------------------/
59	/* S3 CORE CONTENT */
60	/----------------------------------------------------------------------------/
61
62	# Prompt Architect - Evidence-Based Prompt Engineering
63
64	## Kanitsal Cerceve (Evidential Frame Activation)
65	Kaynak dogrulama modu etkin.
66
67
68
69	## Overview
70
71	Comprehensive framework for analyzing, creating, and refining prompts for AI systems (Claude, GPT, etc.). Applies structural optimization, self-consistency patterns, and anti-pattern detection to transform prompts into highly effective versions.
72
73	## When to Use This Skill
74
75	- Creating new prompts for AI systems
76	- Existing prompts produce poor results
77	- Inconsistent AI outputs
78	- Need to improve prompt clarity
79	- Applying evidence-based prompt engineering
80	- Optimizing agent instructions
81	- Building prompt libraries
82
83	## Theoretical Foundation
84
85	### Evidence-Based Techniques
86
87	1. Chain-of-Thought (CoT): Explicit reasoning steps
88	2. Self-Consistency: Multiple reasoning paths
89	3. ReAct: Reasoning + Acting pattern
90	4. Program-of-Thought: Structured logic
91	5. Plan-and-Solve: Decomposition strategy
92	6. Role-Playing: Persona assignment
93	7. Few-Shot Learning: Example-based instruction
94
95	### Prompt Structure Principles
96
97	```
98	[System Context] → [Role Definition] → [Task Description] →
99	[Constraints] → [Format Specification] → [Examples] → [Quality Criteria]
100	```
101
102	## Phase 1: Analyze Current Prompt
103
104	### Objective
105	Identify weaknesses and improvement opportunities
106
107	### Agent: Researcher
108
109	Step 1.1: Structural Analysis
110	```javascript
111	const promptAnalysis = {
112	components: {
113	hasSystemContext: checkForContext(prompt),
114	hasRoleDefinition: checkForRole(prompt),
115	hasTaskDescription: checkForTask(prompt),
116	hasConstraints: checkForConstraints(prompt),
117	hasFormatSpec: checkForFormat(prompt),
118	hasExamples: checkForExamples(prompt),
119	hasQualityCriteria: checkForCriteria(prompt)
120	},
121	metrics: {
122	length: prompt.length,
123	clarity: calculateClarity(prompt),
124	specificity: calculateSpecificity(prompt),
125	completeness: calculateCompleteness(prompt)
126	},
127	antiPatterns: detectAntiPatterns(prompt)
128	};
129
130	await memory.store('prompt-architect/analysis', promptAnalysis);
131	```
132
133	Step 1.2: Detect Anti-Patterns
134	```javascript
135	const antiPatterns = [
136	{
137	name: 'Vague Instructions',
138	pattern: /please\|try to\|maybe\|possibly/gi,
139	severity: 'HIGH',
140	fix: 'Use imperative commands: "Analyze...", "Generate...", "Create..."'
141	},
142	{
143	name: 'Missing Context',
144	pattern: absence of background info,
145	severity: 'HIGH',
146	fix: 'Add sy

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6 picks

Type