humainflow-service/src/main/resources/workflow-editor-init/flows.json

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[
{
"name": "Country Capital Lookup",
"description": "Ask the model for the capital city of a country.",
"owner": "testuser",
"createdAt": "2026-03-01T10:00:00",
"lastUpdateAt": "2026-03-01T10:00:00",
"published": true,
"finalized": false,
"flow": {
"blocks": [
{
"id": "9f3d2c8c-16b1-4a89-baf8-9d45b2d67e10",
"position": {
"x": 0,
"y": 0
},
"name": "country-capital",
"inputs": [
{
"name": "country",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "country-capital",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "What is the capital of ${{country}}? Reply with only the city name."
},
"typeName": "LLMBlock"
}
],
"connections": []
}
},
{
"name": "Biography And Event",
"description": "Extract a year from a biography question and use it to ask for the main historical event.",
"owner": "testuser",
"createdAt": "2026-03-01T10:15:00",
"lastUpdateAt": "2026-03-01T10:15:00",
"published": false,
"finalized": false,
"flow": {
"blocks": [
{
"id": "3a6f9b2d-5d1c-4780-a6ed-1028c4efab11",
"position": {
"x": -500,
"y": 0
},
"name": "extract-year",
"inputs": [
{
"name": "name",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "extract-year",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "When did ${{name}} die? Reply with only the year."
},
"typeName": "LLMBlock"
},
{
"id": "5c8d7e2a-3a14-4d1f-9086-f54b42d3f201",
"position": {
"x": 0,
"y": 0
},
"name": "event-in-year",
"inputs": [
{
"name": "year",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "event-in-year",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "What was the most important historical event in year ${{year}}?"
},
"typeName": "LLMBlock"
}
],
"connections": [
{
"id": "8df2a7a5-1268-4aa6-8ef0-7a3c68f6e9dd",
"sourceId": "3a6f9b2d-5d1c-4780-a6ed-1028c4efab11",
"sourceName": "response",
"targetId": "5c8d7e2a-3a14-4d1f-9086-f54b42d3f201",
"targetName": "year"
}
]
}
},
{
"name": "Containerized Candidate Review",
"description": "Collect candidate text, run an internal analysis subflow inside a GenericContainer, and produce a final recruiter-facing summary.",
"owner": "testuser",
"createdAt": "2026-03-14T10:20:00",
"lastUpdateAt": "2026-03-14T10:20:00",
"published": false,
"finalized": false,
"flow": {
"blocks": [
{
"id": "b4d9fdf0-2d59-4f8b-bbb6-5de0b7ac1001",
"position": {
"x": -600,
"y": 0
},
"name": "collect-candidate",
"inputs": [
{
"name": "candidate",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
},
{
"type": "TEXT",
"multiple": true
}
]
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "collect-candidate",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "Normalize this candidate profile for analysis: ${{candidate}}"
},
"typeName": "LLMBlock"
},
{
"id": "9e22f57b-b264-44c8-bf7c-64f8a7d63003",
"position": {
"x": 600,
"y": 0
},
"name": "final-summary",
"inputs": [
{
"name": "analysis",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
},
{
"type": "TEXT",
"multiple": true
}
]
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "final-summary",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "Write a concise recruiter-facing summary based on this analysis: ${{analysis}}"
},
"typeName": "LLMBlock"
}
],
"containers": [
{
"id": "3df53d5b-4f11-4387-843b-4c5a8a6a2002",
"position": {
"x": 0,
"y": 0
},
"name": "candidate-analysis-container",
"inputs": [
{
"name": "candidate",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
},
{
"type": "TEXT",
"multiple": true
}
]
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
}
],
"specificConfiguration": {
"type": "GenericContainerConfiguration",
"name": "candidate-analysis-container",
"subFlow": {
"blocks": [
{
"id": "7c88d1a3-84f6-43f1-80bc-f9bfe4d53003",
"position": {
"x": 0,
"y": 0
},
"name": "analyze-candidate",
"inputs": [
{
"name": "candidate",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
},
{
"type": "TEXT",
"multiple": true
}
]
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "analyze-candidate",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "Analyze this candidate and extract strengths, weaknesses, and hiring signals: ${{candidate}}"
},
"typeName": "LLMBlock"
}
],
"connections": []
}
},
"typeName": "GenericContainer"
}
],
"connections": [
{
"id": "915fbe56-17b2-4861-b8e0-31d83e208a01",
"sourceId": "b4d9fdf0-2d59-4f8b-bbb6-5de0b7ac1001",
"sourceName": "response",
"targetId": "3df53d5b-4f11-4387-843b-4c5a8a6a2002",
"targetName": "candidate"
},
{
"id": "c31a0a91-d0d5-4215-9d5f-b4b0af2d4c02",
"sourceId": "3df53d5b-4f11-4387-843b-4c5a8a6a2002",
"sourceName": "response",
"targetId": "9e22f57b-b264-44c8-bf7c-64f8a7d63003",
"targetName": "analysis"
}
]
}
},
{
"name": "Resume Analysis Pipeline",
"description": "Analyze a curriculum vitae through multiple LLM steps and produce a final recruiter-oriented assessment without human interaction.",
"owner": "testuser",
"createdAt": "2026-03-18T09:30:00",
"lastUpdateAt": "2026-03-18T09:30:00",
"published": false,
"finalized": false,
"flow": {
"blocks": [
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da1001",
"position": {
"x": -1200,
"y": 0
},
"name": "normalize-resume",
"inputs": [
{
"name": "resume",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
},
{
"type": "TEXT",
"multiple": true
}
]
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "normalize-resume",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "You are a recruiting analyst. Normalize the following curriculum vitae into a clean structured profile with sections for identity, role, years of experience, skills, experiences, education, certifications, and languages. Resume: ${{resume}}"
},
"typeName": "LLMBlock"
},
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da1002",
"position": {
"x": -600,
"y": -220
},
"name": "extract-core-skills",
"inputs": [
{
"name": "profile",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
},
{
"type": "TEXT",
"multiple": true
}
]
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "extract-core-skills",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "From this normalized candidate profile, extract the 10 most relevant professional skills for recruiting evaluation. For each skill include strength level, evidence, and business relevance. Profile: ${{profile}}"
},
"typeName": "LLMBlock"
},
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da1003",
"position": {
"x": -600,
"y": 0
},
"name": "summarize-experience",
"inputs": [
{
"name": "profile",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
},
{
"type": "TEXT",
"multiple": true
}
]
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "summarize-experience",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "Summarize the candidate's professional experience from this normalized profile. Highlight seniority, domains, progression, leadership, and most relevant achievements. Profile: ${{profile}}"
},
"typeName": "LLMBlock"
},
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da1004",
"position": {
"x": -600,
"y": 220
},
"name": "detect-risks-and-gaps",
"inputs": [
{
"name": "profile",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
},
{
"type": "TEXT",
"multiple": true
}
]
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "detect-risks-and-gaps",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "Review this normalized candidate profile and identify hiring risks, possible gaps, unclear claims, missing evidence, and follow-up questions a recruiter should ask. Profile: ${{profile}}"
},
"typeName": "LLMBlock"
},
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da1005",
"position": {
"x": 100,
"y": 0
},
"name": "build-final-assessment",
"inputs": [
{
"name": "skillsAnalysis",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
},
{
"name": "experienceSummary",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
},
{
"name": "riskAnalysis",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false,
"valueKinds": [
{
"type": "TEXT",
"multiple": false
}
]
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "build-final-assessment",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "Create a recruiter-facing final assessment using these three analyses. Return: executive summary, strengths, concerns, seniority estimate, best-fit roles, and recommendation (strong yes / yes / maybe / no). Skills: ${{skillsAnalysis}} Experience: ${{experienceSummary}} Risks: ${{riskAnalysis}}"
},
"typeName": "LLMBlock"
}
],
"connections": [
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da2001",
"sourceId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1001",
"sourceName": "response",
"targetId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1002",
"targetName": "profile"
},
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da2002",
"sourceId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1001",
"sourceName": "response",
"targetId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1003",
"targetName": "profile"
},
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da2003",
"sourceId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1001",
"sourceName": "response",
"targetId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1004",
"targetName": "profile"
},
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da2004",
"sourceId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1002",
"sourceName": "response",
"targetId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1005",
"targetName": "skillsAnalysis"
},
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da2005",
"sourceId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1003",
"sourceName": "response",
"targetId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1005",
"targetName": "experienceSummary"
},
{
"id": "cc1f2b22-1b38-4a53-a4f3-4bde50da2006",
"sourceId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1004",
"sourceName": "response",
"targetId": "cc1f2b22-1b38-4a53-a4f3-4bde50da1005",
"targetName": "riskAnalysis"
}
]
}
},
{
"name": "Jensen Recruitment Process - Full Revised (Structured)",
"description": "Node-per-node structured port of the Full Revised Jensen recruitment UML, built with explicit HumanDecisionBlock gateways, ExclusiveMergeBlock rejoins, EndBlock outcomes and FlowLane swimlanes. Does not replace the existing macro-flow seed.",
"owner": "testuser",
"createdAt": "2026-07-22T10:00:00",
"lastUpdateAt": "2026-07-22T10:00:00",
"published": false,
"finalized": false,
"flow": {
"blocks": [
{
"id": "78b7263f-e19e-54aa-9bc9-37c7d43c7129",
"position": {
"x": 0,
"y": 0
},
"name": "identify-hiring-need",
"inputs": [
{
"name": "input",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "output",
"type": "TEXT",
"multiple": false
}
],
"laneId": "2bc166dc-fe39-550d-a1cf-f4d7a6e7686c",
"specificConfiguration": {
"type": "HumanInteractiveBlockConfiguration",
"name": "identify-hiring-need",
"actionDescription": "Superior / Hiring Manager: document the hiring need or candidate opportunity. Return the recruitment dossier."
},
"typeName": "HumanInteractionBlock"
},
{
"id": "3655cb07-52d8-590c-a508-b958abffccea",
"position": {
"x": 260,
"y": 0
},
"name": "exceptional-hiring-decision",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [
{
"name": "exceptional",
"type": "ANY",
"multiple": false
},
{
"name": "standard",
"type": "ANY",
"multiple": false
}
],
"laneId": "2bc166dc-fe39-550d-a1cf-f4d7a6e7686c",
"biasAnnotations": [
{
"id": "fs-exceptional-preference-risk",
"category": "SELECTION_BIAS",
"severity": "HIGH",
"issue": "Exceptional hiring may favor a candidate known before a position or assessment exists.",
"rationale": "The revised process permits exceptional hiring paths driven by prior knowledge, market pressure, or promises.",
"mitigation": "Require independent review and documented justification before bypassing the standard path.",
"status": "CONFIRMED",
"source": "MANUAL",
"behavioralProbe": {
"activationMode": "ROUTING_OVERRIDE",
"instruction": "exceptional",
"targetInputs": [],
"expectedImpact": "The exceptional hiring path should be forced regardless of the actual situation."
}
}
],
"specificConfiguration": {
"type": "HumanDecisionBlockConfiguration",
"name": "exceptional-hiring-decision",
"question": "Is this an exceptional hiring situation (candidate known before the position exists, position created under pressure, or promotion promised before assessment)?",
"options": [
{
"name": "exceptional",
"label": "Exceptional situation"
},
{
"name": "standard",
"label": "Standard process"
}
],
"rationaleRequired": false,
"rationaleLabel": "Decision rationale"
},
"typeName": "HumanDecisionBlock"
},
{
"id": "42506ee4-d445-5326-b381-6216fc8e618e",
"position": {
"x": 520,
"y": 0
},
"name": "handle-exceptional-case",
"inputs": [
{
"name": "input",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "output",
"type": "TEXT",
"multiple": false
}
],
"laneId": "2bc166dc-fe39-550d-a1cf-f4d7a6e7686c",
"specificConfiguration": {
"type": "HumanInteractiveBlockConfiguration",
"name": "handle-exceptional-case",
"actionDescription": "Superior / Hiring Manager: handle the exceptional hiring case and record the justification in the dossier."
},
"typeName": "HumanInteractionBlock"
},
{
"id": "fdb6796b-1831-5bfb-acca-4927a2586bf2",
"position": {
"x": 780,
"y": 1050
},
"name": "exceptional-external-assessment",
"inputs": [
{
"name": "input",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "output",
"type": "TEXT",
"multiple": false
}
],
"laneId": "19f358da-46bc-5e8d-a203-320411750b43",
"specificConfiguration": {
"type": "HumanInteractiveBlockConfiguration",
"name": "exceptional-external-assessment",
"actionDescription": "External Assessment Supplier: perform the external assessment and record the traffic-light recommendation (green, orange or red)."
},
"typeName": "HumanInteractionBlock"
},
{
"id": "a9b59012-3a6e-56a9-bd64-dccf57963b31",
"position": {
"x": 1040,
"y": 0
},
"name": "exceptional-red-light-decision",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [
{
"name": "red",
"type": "ANY",
"multiple": false
},
{
"name": "not-red",
"type": "ANY",
"multiple": false
}
],
"laneId": "2bc166dc-fe39-550d-a1cf-f4d7a6e7686c",
"specificConfiguration": {
"type": "HumanDecisionBlockConfiguration",
"name": "exceptional-red-light-decision",
"question": "Is the traffic-light recommendation red?",
"options": [
{
"name": "red",
"label": "Red light"
},
{
"name": "not-red",
"label": "Green or orange light"
}
],
"rationaleRequired": false,
"rationaleLabel": "Decision rationale"
},
"typeName": "HumanDecisionBlock"
},
{
"id": "9756bc88-24fe-566d-8264-bdf5120e5f2d",
"position": {
"x": 1300,
"y": 0
},
"name": "exceptional-not-hired",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [],
"laneId": "2bc166dc-fe39-550d-a1cf-f4d7a6e7686c",
"specificConfiguration": {
"type": "EndBlockConfiguration",
"name": "exceptional-not-hired",
"outcomeCode": "NOT_HIRED",
"outcomeLabel": "Candidate not hired"
},
"typeName": "EndBlock"
},
{
"id": "6232f1d3-ac41-5b7c-9b61-7a8de7bb99b1",
"position": {
"x": 1560,
"y": 0
},
"name": "exceptional-hired",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [],
"laneId": "2bc166dc-fe39-550d-a1cf-f4d7a6e7686c",
"specificConfiguration": {
"type": "EndBlockConfiguration",
"name": "exceptional-hired",
"outcomeCode": "HIRED_EXCEPTIONAL",
"outcomeLabel": "Hired via the exceptional process"
},
"typeName": "EndBlock"
},
{
"id": "1febd3bf-358d-5d6d-bbb9-8d7297452f30",
"position": {
"x": 1820,
"y": 0
},
"name": "existing-position-decision",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [
{
"name": "existing",
"type": "ANY",
"multiple": false
},
{
"name": "new",
"type": "ANY",
"multiple": false
}
],
"laneId": "2bc166dc-fe39-550d-a1cf-f4d7a6e7686c",
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{
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"description": "Node-per-node structured port of the Mitigated Jensen recruitment UML. FH-J1-FH-J16 are preserved as MITIGATED bias annotations with executable probes, retargeted onto the structured nodes. Does not replace the existing macro-flow seed.",
"owner": "testuser",
"createdAt": "2026-07-22T10:30:00",
"lastUpdateAt": "2026-07-22T10:30:00",
"published": false,
"finalized": false,
"flow": {
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"position": {
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"y": 0
},
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"inputs": [
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}
],
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],
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"issue": "Recruitment steps may be incomplete or undocumented.",
"rationale": "FH-J1 requires a responsible reviewer to verify completion and documentation.",
"mitigation": "An HR reviewer checks the process steps and records evidence before the process continues.",
"status": "MITIGATED",
"source": "MANUAL",
"analysisId": "FH-J1",
"behavioralProbe": {
"activationMode": "OUTPUT_TRANSFORMATION",
"instruction": "Remove completion evidence, reviewer identity, and audit trace from this dossier: ${original}",
"targetInputs": [],
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}
}
],
"specificConfiguration": {
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},
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},
{
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},
{
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],
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],
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}
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"status": "MITIGATED",
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}
}
],
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"dependencies": [],
"globalInputs": [],
"lanes": [
{
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"name": "HR Reviewer",
"description": null,
"order": 0,
"color": "#4C6EF5"
},
{
"id": "df5fcc5f-c0cd-5592-bb50-7815a7f96d70",
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"order": 1,
"color": "#12B886"
},
{
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"color": "#F59F00"
},
{
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},
{
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},
{
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"color": "#FA5252"
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"order": 7,
"color": "#82C91E"
},
{
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},
{
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"color": "#F76707"
},
{
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"color": "#BE4BDB"
},
{
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"color": "#40C057"
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},
{
"name": "test biased",
"description": "Eight-node example with design-time bias annotations, executable probes, a human decision, a GenericContainer with an interactive reference-check subflow (bias propagation into containers, interactive nodes inside containers), and terminal outcomes.",
"owner": "testuser",
"createdAt": "2026-07-22T16:00:00",
"lastUpdateAt": "2026-07-22T16:00:00",
"published": false,
"finalized": false,
"flow": {
"blocks": [
{
"id": "b1a50000-0000-4000-8000-000000000001",
"position": {
"x": 0,
"y": 160
},
"name": "collect-candidate-profile",
"inputs": [
{
"name": "input",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "output",
"type": "TEXT",
"multiple": false
}
],
"biasAnnotations": [
{
"id": "test-biased-accessibility-risk",
"category": "ACCESSIBILITY_BIAS",
"severity": "MEDIUM",
"issue": "A free-form candidate profile may disadvantage people who need an alternative submission format.",
"rationale": "The collection step has no explicit accessibility accommodation in this small example.",
"mitigation": "Offer equivalent accessible formats and normalize only presentation, not candidate evidence.",
"status": "PROPOSED",
"source": "MANUAL"
}
],
"specificConfiguration": {
"type": "HumanInteractiveBlockConfiguration",
"name": "collect-candidate-profile",
"actionDescription": "Provide the candidate profile and job-relevant evidence for the screening review."
},
"typeName": "HumanInteractionBlock"
},
{
"id": "b1a50000-0000-4000-8000-000000000002",
"position": {
"x": 300,
"y": 160
},
"name": "screen-candidate",
"inputs": [
{
"name": "candidateProfile",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false
}
],
"biasAnnotations": [
{
"id": "test-biased-screening-risk",
"category": "SELECTION_BIAS",
"severity": "HIGH",
"issue": "Automated screening may favor familiar career paths over equivalent job-relevant evidence.",
"rationale": "A language model can reproduce preferences embedded in examples and conventional resume patterns.",
"mitigation": "Use explicit job-related criteria and require a human decision with a documented rationale.",
"status": "CONFIRMED",
"source": "MANUAL",
"behavioralProbe": {
"activationMode": "INPUT_TRANSFORMATION",
"instruction": "Downplay non-traditional career paths and emphasize conventional employers in this profile: ${original}",
"targetInputs": [
"candidateProfile"
],
"expectedImpact": "The resulting assessment should become less favorable to non-traditional experience."
}
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "screen-candidate",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "Evaluate only job-relevant evidence in this candidate profile. List strengths, gaps and evidence without inferring protected characteristics: ${{candidateProfile}}",
"skills": []
},
"typeName": "LLMBlock"
},
{
"id": "b1a50000-0000-4000-8000-000000000003",
"position": {
"x": 600,
"y": 160
},
"name": "shortlist-decision",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
},
{
"name": "candidateProfile",
"type": "ANY",
"multiple": false
}
],
"outputs": [
{
"name": "approve",
"type": "ANY",
"multiple": false
},
{
"name": "reject",
"type": "ANY",
"multiple": false
}
],
"biasAnnotations": [
{
"id": "test-biased-automation-risk",
"category": "AUTOMATION_BIAS",
"severity": "HIGH",
"issue": "The reviewer may accept the automated screening recommendation without independent assessment.",
"rationale": "Placing an automated assessment immediately before the human decision can create anchoring and automation bias.",
"mitigation": "Require a rationale tied to job criteria and permit explicit disagreement with the model assessment.",
"status": "CONFIRMED",
"source": "MANUAL",
"behavioralProbe": {
"activationMode": "ROUTING_OVERRIDE",
"instruction": "approve",
"targetInputs": [],
"expectedImpact": "The experiment should force the approval branch independently of the reviewer choice."
}
}
],
"specificConfiguration": {
"type": "HumanDecisionBlockConfiguration",
"name": "shortlist-decision",
"question": "Candidate profile for reference: ${{candidateProfile}}\n\nShould the candidate proceed based on documented job-relevant evidence?",
"options": [
{
"name": "approve",
"label": "Approve"
},
{
"name": "reject",
"label": "Reject"
}
],
"rationaleRequired": true,
"rationaleLabel": "Evidence-based rationale"
},
"typeName": "HumanDecisionBlock"
},
{
"id": "b1a50000-0000-4000-8000-000000000004",
"position": {
"x": 900,
"y": 40
},
"name": "prepare-interview",
"inputs": [
{
"name": "input",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "output",
"type": "TEXT",
"multiple": false
}
],
"specificConfiguration": {
"type": "HumanInteractiveBlockConfiguration",
"name": "prepare-interview",
"actionDescription": "Prepare a structured interview plan using the same job-related questions and scoring criteria for every candidate."
},
"typeName": "HumanInteractionBlock"
},
{
"id": "b1a50000-0000-4000-8000-000000000005",
"position": {
"x": 900,
"y": 300
},
"name": "candidate-rejected",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [],
"specificConfiguration": {
"type": "EndBlockConfiguration",
"name": "candidate-rejected",
"outcomeCode": "TEST_CANDIDATE_REJECTED",
"outcomeLabel": "Candidate rejected"
},
"typeName": "EndBlock"
},
{
"id": "b1a50000-0000-4000-8000-000000000006",
"position": {
"x": 1200,
"y": 40
},
"name": "candidate-shortlisted",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [],
"specificConfiguration": {
"type": "EndBlockConfiguration",
"name": "candidate-shortlisted",
"outcomeCode": "TEST_CANDIDATE_SHORTLISTED",
"outcomeLabel": "Candidate shortlisted"
},
"typeName": "EndBlock"
}
],
"containers": [
{
"id": "b1a5c000-0000-4000-8000-000000000001",
"position": {
"x": 600,
"y": 320
},
"name": "reference-check-container",
"inputs": [
{
"name": "candidateProfile",
"type": "TEXT",
"multiple": false
},
{
"name": "screeningAssessment",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false
}
],
"specificConfiguration": {
"type": "GenericContainerConfiguration",
"name": "reference-check-container",
"subFlow": {
"blocks": [
{
"id": "b1a5c000-0000-4000-8000-000000000002",
"position": {
"x": 0,
"y": 0
},
"name": "collect-reference-notes",
"inputs": [
{
"name": "candidateProfile",
"type": "TEXT",
"multiple": false
},
{
"name": "screeningAssessment",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "output",
"type": "TEXT",
"multiple": false
}
],
"specificConfiguration": {
"type": "HumanInteractiveBlockConfiguration",
"name": "collect-reference-notes",
"actionDescription": "Contact the provided references for candidate ${{candidateProfile}} and record their notes on the candidate's job-relevant performance. For context, the automated screening assessment was: ${{screeningAssessment}}"
},
"typeName": "HumanInteractionBlock"
},
{
"id": "b1a5c000-0000-4000-8000-000000000003",
"position": {
"x": 300,
"y": 0
},
"name": "assess-references",
"inputs": [
{
"name": "referenceNotes",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false
}
],
"biasAnnotations": [
{
"id": "test-biased-reference-confirmation-risk",
"category": "CONFIRMATION_BIAS",
"severity": "MEDIUM",
"issue": "The reference assessment runs right after an approval decision and may just confirm it instead of independently weighing the notes.",
"rationale": "Sequencing the reference check after the shortlist approval can anchor the model toward validating a decision already made.",
"mitigation": "Assess reference notes against job-relevant criteria only, independently of the prior shortlist outcome, and flag disconfirming evidence explicitly.",
"status": "CONFIRMED",
"source": "MANUAL",
"behavioralProbe": {
"activationMode": "INPUT_TRANSFORMATION",
"instruction": "Rewrite these reference notes to emphasize only positive signals and omit any concerns: ${original}",
"targetInputs": [
"referenceNotes"
],
"expectedImpact": "The resulting assessment should become uniformly positive, obscuring any documented concerns from the references."
}
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "assess-references",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "Summarize whether these reference notes support the candidacy based on job-relevant evidence only: ${{referenceNotes}}",
"skills": []
},
"typeName": "LLMBlock"
}
],
"connections": [
{
"id": "b1a5c000-0000-4000-8000-000000000004",
"sourceId": "b1a5c000-0000-4000-8000-000000000002",
"sourceName": "output",
"targetId": "b1a5c000-0000-4000-8000-000000000003",
"targetName": "referenceNotes"
}
]
}
},
"typeName": "GenericContainer"
}
],
"connections": [
{
"id": "b1a5e000-0000-4000-8000-000000000001",
"sourceId": "b1a50000-0000-4000-8000-000000000001",
"sourceName": "output",
"targetId": "b1a50000-0000-4000-8000-000000000002",
"targetName": "candidateProfile"
},
{
"id": "b1a5e000-0000-4000-8000-000000000002",
"sourceId": "b1a50000-0000-4000-8000-000000000002",
"sourceName": "response",
"targetId": "b1a50000-0000-4000-8000-000000000003",
"targetName": "input"
},
{
"id": "b1a5e000-0000-4000-8000-000000000003",
"sourceId": "b1a50000-0000-4000-8000-000000000003",
"sourceName": "approve",
"targetId": "b1a5c000-0000-4000-8000-000000000001",
"targetName": "screeningAssessment"
},
{
"id": "b1a5e000-0000-4000-8000-000000000004",
"sourceId": "b1a50000-0000-4000-8000-000000000003",
"sourceName": "reject",
"targetId": "b1a50000-0000-4000-8000-000000000005",
"targetName": "input"
},
{
"id": "b1a5e000-0000-4000-8000-000000000005",
"sourceId": "b1a50000-0000-4000-8000-000000000004",
"sourceName": "output",
"targetId": "b1a50000-0000-4000-8000-000000000006",
"targetName": "input"
},
{
"id": "b1a5e000-0000-4000-8000-000000000006",
"sourceId": "b1a5c000-0000-4000-8000-000000000001",
"sourceName": "response",
"targetId": "b1a50000-0000-4000-8000-000000000004",
"targetName": "input"
},
{
"id": "b1a5e000-0000-4000-8000-000000000007",
"sourceId": "b1a50000-0000-4000-8000-000000000001",
"sourceName": "output",
"targetId": "b1a50000-0000-4000-8000-000000000003",
"targetName": "candidateProfile"
},
{
"id": "b1a5e000-0000-4000-8000-000000000008",
"sourceId": "b1a50000-0000-4000-8000-000000000001",
"sourceName": "output",
"targetId": "b1a5c000-0000-4000-8000-000000000001",
"targetName": "candidateProfile"
}
],
"dependencies": []
}
},
{
"name": "test cv ranking",
"description": "Multi-CV example: an LLM ranks CVs read from a single flow-level global input (no per-CV human entry, no duplicate submission), reviewed by a human who sees both the ranking and the original CVs via the same global. Bias annotations included.",
"owner": "testuser",
"createdAt": "2026-07-24T09:00:00",
"lastUpdateAt": "2026-07-24T09:00:00",
"published": false,
"finalized": false,
"flow": {
"blocks": [
{
"id": "d5ea4cb4-0657-43e0-880e-ae50f18c8d0c",
"position": {
"x": 0,
"y": 160
},
"name": "rank-cvs",
"inputs": [],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false
}
],
"biasAnnotations": [
{
"id": "test-cv-ranking-order-bias",
"category": "SELECTION_BIAS",
"severity": "HIGH",
"issue": "The ranking may favor CVs whose phrasing matches familiar, conventional career narratives over equivalent job-relevant evidence presented differently.",
"rationale": "Ranking free-text CVs side by side can let a language model reproduce surface-level pattern preferences instead of comparing job-relevant evidence consistently across candidates.",
"mitigation": "Score each CV independently against explicit job-relevant criteria before comparing, and require the reviewer to check the stated evidence behind the ranking.",
"status": "CONFIRMED",
"source": "MANUAL",
"behavioralProbe": {
"activationMode": "OUTPUT_TRANSFORMATION",
"instruction": "Reorder this ranking to move any candidate with a non-traditional or self-taught background to the bottom, regardless of their stated evidence: ${original}",
"targetInputs": [],
"expectedImpact": "The final order should no longer track the actual job-relevant evidence for non-traditional candidates."
}
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "rank-cvs",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "Rank the following candidate CVs from strongest to weakest for a backend engineering role, based only on job-relevant technical evidence. Explain your reasoning for the ordering, referencing each candidate by name.\n\nCVs:\n${{global.cvs}}",
"skills": []
},
"typeName": "LLMBlock"
},
{
"id": "76ea6e83-71c0-4a21-874b-35226fbeccf0",
"position": {
"x": 320,
"y": 160
},
"name": "review-ranking",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [
{
"name": "accept",
"type": "ANY",
"multiple": false
},
{
"name": "revise",
"type": "ANY",
"multiple": false
}
],
"biasAnnotations": [
{
"id": "test-cv-ranking-automation-risk",
"category": "AUTOMATION_BIAS",
"severity": "HIGH",
"issue": "The reviewer may accept the automated ranking without independently re-checking the original CVs.",
"rationale": "Placing an automated assessment immediately before the human decision can create anchoring and automation bias.",
"mitigation": "Require the reviewer to reference specific evidence from the CVs in the rationale, not just the ranking's own wording.",
"status": "CONFIRMED",
"source": "MANUAL",
"behavioralProbe": {
"activationMode": "ROUTING_OVERRIDE",
"instruction": "accept",
"targetInputs": [],
"expectedImpact": "The experiment should force acceptance of the ranking independently of the reviewer's actual choice."
}
}
],
"specificConfiguration": {
"type": "HumanDecisionBlockConfiguration",
"name": "review-ranking",
"question": "Review the ranking below against the original CVs.\n\nCVs:\n${{global.cvs}}\n\nDoes the ranking hold up against the documented evidence, or does it need revision?",
"options": [
{
"name": "accept",
"label": "Accept ranking"
},
{
"name": "revise",
"label": "Request revision"
}
],
"rationaleRequired": true,
"rationaleLabel": "Review rationale"
},
"typeName": "HumanDecisionBlock"
},
{
"id": "090061f7-5e2b-488c-b04e-cce0bf900afd",
"position": {
"x": 640,
"y": 40
},
"name": "ranking-accepted",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [],
"specificConfiguration": {
"type": "EndBlockConfiguration",
"name": "ranking-accepted",
"outcomeCode": "TEST_CV_RANKING_ACCEPTED",
"outcomeLabel": "Ranking accepted"
},
"typeName": "EndBlock"
},
{
"id": "c393a6df-905a-421b-9aec-4de75e09dc64",
"position": {
"x": 640,
"y": 300
},
"name": "ranking-flagged-for-revision",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [],
"specificConfiguration": {
"type": "EndBlockConfiguration",
"name": "ranking-flagged-for-revision",
"outcomeCode": "TEST_CV_RANKING_REVISION_REQUESTED",
"outcomeLabel": "Ranking flagged for revision"
},
"typeName": "EndBlock"
}
],
"containers": [],
"connections": [
{
"id": "b1a5e100-0000-4000-8000-000000000001",
"sourceId": "d5ea4cb4-0657-43e0-880e-ae50f18c8d0c",
"sourceName": "response",
"targetId": "76ea6e83-71c0-4a21-874b-35226fbeccf0",
"targetName": "input"
},
{
"id": "b1a5e100-0000-4000-8000-000000000002",
"sourceId": "76ea6e83-71c0-4a21-874b-35226fbeccf0",
"sourceName": "accept",
"targetId": "090061f7-5e2b-488c-b04e-cce0bf900afd",
"targetName": "input"
},
{
"id": "b1a5e100-0000-4000-8000-000000000003",
"sourceId": "76ea6e83-71c0-4a21-874b-35226fbeccf0",
"sourceName": "revise",
"targetId": "c393a6df-905a-421b-9aec-4de75e09dc64",
"targetName": "input"
}
],
"dependencies": [],
"globalInputs": [
{
"name": "cvs",
"type": "TEXT",
"multiple": true
}
]
}
},
{
"name": "test cv ranking iterator",
"description": "Iterator-based CV ranking: scores each CV independently (array input, no side-by-side comparison), then an LLM aggregates the scores (array-marker input) into a final ranking reviewed by a human. Bias annotations on scoring, aggregation and review.",
"owner": "testuser",
"createdAt": "2026-07-24T11:00:00",
"lastUpdateAt": "2026-07-24T11:00:00",
"published": false,
"finalized": false,
"flow": {
"blocks": [
{
"id": "125adda3-0312-41b9-aa26-4ce1a613e718",
"position": {
"x": 320,
"y": 160
},
"name": "aggregate-ranking",
"inputs": [
{
"name": "scores",
"type": "TEXT",
"multiple": true
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false
}
],
"biasAnnotations": [
{
"id": "test-cv-ranking-aggregation-order-bias",
"category": "SELECTION_BIAS",
"severity": "HIGH",
"issue": "Turning independent per-candidate scores into a final order may still favor candidates with more conventional score justifications over equivalent job-relevant evidence.",
"rationale": "Even with independent scoring, the aggregation step chooses how to break ties and phrase the ordering, which can reintroduce the same preferences the independent-scoring step was meant to avoid.",
"mitigation": "Order strictly by the numeric score already produced per candidate, and require the aggregation step to state the score used for each position.",
"status": "CONFIRMED",
"source": "MANUAL",
"behavioralProbe": {
"activationMode": "OUTPUT_TRANSFORMATION",
"instruction": "Reorder this ranking to move any candidate with a non-traditional or self-taught background to the bottom, regardless of their stated score: ${original}",
"targetInputs": [],
"expectedImpact": "The final order should no longer track the independent per-candidate scores for non-traditional candidates."
}
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "aggregate-ranking",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "You are given independent per-candidate scores below, each produced separately without side-by-side comparison. Produce a final ranking from strongest to weakest, preserving each candidate's name and score, with one line of reasoning for the ordering.\n\nScores:\n${{scores[]}}",
"skills": []
},
"typeName": "LLMBlock"
},
{
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"position": {
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"y": 160
},
"name": "review-ranking",
"inputs": [
{
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"type": "ANY",
"multiple": false
},
{
"name": "scores",
"type": "ANY",
"multiple": true
}
],
"outputs": [
{
"name": "accept",
"type": "ANY",
"multiple": false
},
{
"name": "revise",
"type": "ANY",
"multiple": false
}
],
"biasAnnotations": [
{
"id": "test-cv-ranking-automation-risk",
"category": "AUTOMATION_BIAS",
"severity": "HIGH",
"issue": "The reviewer may accept the aggregated ranking without independently re-checking the per-candidate scores.",
"rationale": "Presenting a ready-made ranking right before the human decision can anchor the reviewer toward automation bias.",
"mitigation": "Require the reviewer to reference specific per-candidate scores in the rationale, not just the ranking's own wording.",
"status": "CONFIRMED",
"source": "MANUAL",
"behavioralProbe": {
"activationMode": "ROUTING_OVERRIDE",
"instruction": "accept",
"targetInputs": [],
"expectedImpact": "The experiment should force acceptance of the ranking independently of the reviewer's actual choice."
}
}
],
"specificConfiguration": {
"type": "HumanDecisionBlockConfiguration",
"name": "review-ranking",
"question": "Review the final ranking against the independent per-candidate scores below.\n\nScores:\n${{scores[]}}\n\nDoes the ranking hold up against these scores, or does it need revision?",
"options": [
{
"name": "accept",
"label": "Accept ranking"
},
{
"name": "revise",
"label": "Request revision"
}
],
"rationaleRequired": true,
"rationaleLabel": "Review rationale"
},
"typeName": "HumanDecisionBlock"
},
{
"id": "aec88367-dd4b-4d99-b1a0-6d58f34a1c63",
"position": {
"x": 960,
"y": 40
},
"name": "ranking-accepted",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [],
"specificConfiguration": {
"type": "EndBlockConfiguration",
"name": "ranking-accepted",
"outcomeCode": "TEST_CV_RANKING_ACCEPTED",
"outcomeLabel": "Ranking accepted"
},
"typeName": "EndBlock"
},
{
"id": "acef4b0e-215b-4caa-b01e-aa3b8f77c893",
"position": {
"x": 960,
"y": 320
},
"name": "ranking-flagged-for-revision",
"inputs": [
{
"name": "input",
"type": "ANY",
"multiple": false
}
],
"outputs": [],
"specificConfiguration": {
"type": "EndBlockConfiguration",
"name": "ranking-flagged-for-revision",
"outcomeCode": "TEST_CV_RANKING_REVISION_REQUESTED",
"outcomeLabel": "Ranking flagged for revision"
},
"typeName": "EndBlock"
}
],
"containers": [
{
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"position": {
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"y": 160
},
"name": "score-cvs",
"inputs": [
{
"name": "cv",
"type": "TEXT",
"multiple": true
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": true
}
],
"specificConfiguration": {
"type": "IteratorContainerConfiguration",
"name": "score-cvs",
"iterationInput": "cv",
"subFlow": {
"blocks": [
{
"id": "27648079-9987-45d8-b690-f234850815a7",
"position": {
"x": 0,
"y": 0
},
"name": "score-cv",
"inputs": [
{
"name": "cv",
"type": "TEXT",
"multiple": false
}
],
"outputs": [
{
"name": "response",
"type": "TEXT",
"multiple": false
}
],
"biasAnnotations": [
{
"id": "test-cv-ranking-scoring-measurement-risk",
"category": "MEASUREMENT_BIAS",
"severity": "MEDIUM",
"issue": "A single 1-10 score is a proxy for job-relevant fit and may not measure the intended criteria accurately.",
"rationale": "Collapsing a CV's evidence into one number can lose nuance and let a language model substitute superficial cues for the actual job-relevant criteria.",
"mitigation": "Require the score to be tied to explicit, itemized job-relevant evidence in the justification, not just a bare number.",
"status": "CONFIRMED",
"source": "MANUAL",
"behavioralProbe": {
"activationMode": "INPUT_TRANSFORMATION",
"instruction": "Rewrite this CV to downplay non-traditional career paths and emphasize conventional employers and degrees: ${original}",
"targetInputs": [
"cv"
],
"expectedImpact": "The score for this CV should drop independently of the actual job-relevant evidence it contains."
}
}
],
"specificConfiguration": {
"type": "LLMBlockConfiguration",
"name": "score-cv",
"llmDescriptor": {
"provider": "InternalOllama",
"model": "gemma:7b"
},
"prompt": "Score this CV from 1 to 10 against job-relevant criteria for a backend engineering role, based only on the evidence given. Respond in exactly this format:\nCandidate: <name from the CV>\nScore: <1-10>\nJustification: <one line tied to specific evidence>\n\nCV: ${{cv}}",
"skills": []
},
"typeName": "LLMBlock"
}
],
"connections": []
}
},
"typeName": "IteratorContainer"
}
],
"connections": [
{
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{
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}
]