Agent skill
source-credibility-analyzer
Install this agent skill to your Project
npx add-skill https://github.com/DNYoussef/context-cascade/tree/main/skills/research/source-credibility-analyzer
SKILL.md
/============================================================================/ /* SOURCE-CREDIBILITY-ANALYZER SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/
name: source-credibility-analyzer version: 2.0 description: | [assert|neutral] Standalone tool for automated source evaluation using program-of-thought scoring rubrics. Outputs credibility (1-5), bias (1-5), and priority (1-5) scores with transparent explanations. Use when evalu [ground:given] [conf:0.95] [state:confirmed] category: research tags:
- general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute source-credibility-analyzer workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic research processes"
/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/
[define|neutral] SKILL := { name: "source-credibility-analyzer", category: "research", version: "2.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/
[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/
[define|neutral] TRIGGER_POSITIVE := { keywords: ["source-credibility-analyzer", "research", "workflow"], context: "user needs source-credibility-analyzer capability" } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/
Source Credibility Analyzer
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
Purpose
Automate evaluation of research sources using transparent program-of-thought rubrics. Outputs structured JSON with credibility, bias, and priority scores (1-5) plus explanations showing calculation logic. Can be used as standalone tool OR integrated into general-research-workflow Step 3.
When to Use This Tool
Use this tool when:
- ✅ Evaluating research sources for academic projects
- ✅ Automating source classification (general-research-workflow Step 3)
- ✅ Scoring large batches of sources consistently
- ✅ Getting objective second opinion on source quality
Do NOT use for:
- ❌ Entertainment content (movies, novels) - not designed for this
- ❌ Source quality already obvious (Nature paper = high, random blog = low)
- ❌ Unique/irreplaceable source (only source on obscure topic) - read anyway
Decision Tree: If manual source evaluation takes >10 min → use this tool (saves 15-45 min per source)
Quick Reference
| Step | Objective | Deliverable | Duration | Quality Gate |
|---|---|---|---|---|
| 0 | Validate inputs | Confirmed metadata | 30 sec | Required fields present |
| 0.5 | Classify source type | Source category | 1 min | Type assigned |
| 1 | Calculate credibility | Score 1-5 + explanation | 2-5 min | Score justified |
| 2 | Calculate bias | Score 1-5 + explanation | 2-5 min | Score justified |
| 3 | Calculate priority | Score 1-5 + explanation | 1-3 min | Score justified |
| 4 | Resolve conflicts | Final recommendation | 1 min | Logic correct |
| 5 | Generate output | JSON + storage | 1 min | Complete + stored |
Agent Coordination Protocol
Single Agent Execution
- Agent: analyst
- Role: Evaluate source using program-of-thought rubrics
- Workflow: Sequential steps 0 → 0.5 → 1 → 2 → 3 → 4 → 5
Input Format
{
"title": "[Required]",
"author": "[Required]",
"year": [Required, 1500-2025],
"venue": "[Required]",
"type": "[Required]",
"citations": [Optional],
"doi": "[Optional]",
"url": "[Optional]",
"institution": "[Optional]",
"credentials": "[Optional]"
}
Output Format
{
"source": { ... },
"scores": {
"credibility": {"score": [1-5], "explanation": "..."},
"bias": {"score": [1-5], "explanation": "..."},
"priority": {"score": [1-5], "explanation": "..."}
},
"recommendation": {
"action": "[READ_FIRST | READ_LATER | VERIFY_CLAIMS | SKIP]",
"reason": "...",
"conflicts": "..."
},
"metadata": { ... }
}
Memory MCP Tags
Store with: WHO=analyst, WHEN=[timestamp], PROJECT=[topic], WHY=source-scoring, CREDIBILITY=[score], BIAS=[score], PRIORITY=[score], RECOMMENDATION=[action]
Step-by-Step Workflow
STEP 0: Validate Input Metadata
Agent: analyst Objective: Ensure required metadata is present and valid
Procedure:
-
Check for ✅ required fields:
title(string, non-empty)author(string, non-empty)year(integer, 1500-2025)venue(string, non-empty)type(string, non-empty)
-
Note ⚠️ optional fields if present:
citations(improves credibility scoring)doi(improves credibility scoring)institution(improves credibility scoring)credentials(improves credibility scoring)url(for reference)
-
Validate data types and ranges:
- Year must be integer 1500-2025
- All required strings non-empty
-
If validation fails → Return error with missing/invalid field name
Deliverable: Validated metadata object
Quality Gate 0:
- GO: All required fields present, year valid (1500-2025)
- NO-GO: Missing/invalid field → Return error to user
STEP 0.5: Classify Source Type (Edge Case Handling)
Agent: analyst Objective: Assign source to appropriate category for rubric baseline
Edge Case Decision Tree:
/----------------------------------------------------------------------------/ /* S4 SUCCESS CRITERIA / /----------------------------------------------------------------------------*/
[define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S5 MCP INTEGRATION / /----------------------------------------------------------------------------*/
[define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]
/----------------------------------------------------------------------------/ /* S6 MEMORY NAMESPACE / /----------------------------------------------------------------------------*/
[define|neutral] MEMORY_NAMESPACE := { pattern: "skills/research/source-credibility-analyzer/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]
[define|neutral] MEMORY_TAGGING := { WHO: "source-credibility-analyzer-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S7 SKILL COMPLETION VERIFICATION / /----------------------------------------------------------------------------*/
[direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S8 ABSOLUTE RULES / /----------------------------------------------------------------------------*/
[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]
[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]
[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* PROMISE / /----------------------------------------------------------------------------*/
[commit|confident] SOURCE_CREDIBILITY_ANALYZER_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
cognitive-mode
Comprehensive cognitive mode management skill for the VERILINGUA x VERIX x DSPy x GlobalMOO integration. Enables automatic mode selection, frame configuration, VERIX epistemic notation, and GlobalMOO optimization. Use this skill when configuring AI behavior for specific task types, optimizing prompt engineering, or ensuring epistemic consistency in responses.
bootstrap-loop
fix-bug
Fix bug command
clarity-linter
dependencies
when-mapping-dependencies-use-dependency-mapper
Didn't find tool you were looking for?