{
  "schema_version": "2.0",
  "generated_at": "2026-10-20",
  "candidate": "Sneh Sinha",
  "schema": {
    "status": "exactly one string from evidence_labels.values",
    "qualifiers": "optional list of further evidence_labels.values",
    "checked": "ISO date of the last verification, YYYY-MM-DD",
    "verification_url": "URL that evidences the claim",
    "ids": "roles use role-* · capabilities capability-* · projects project-* · experience experience-*",
    "roles_match": "target_roles in agent-card.json must project exactly to candidate.json#roles"
  },
  "note": "Each capability points to the evidence that supports it. Skills referenced are exactly those shown in Human Mode; no capability asserts expertise beyond what the portfolio documents. Evidence carries qualitative status labels (public repo · external validation · documented experience · self-reported · experimental · repository unavailable); no numeric scores are assigned.",
  "capabilities": [
    {
      "id": "capability-agents",
      "name": "AI Agents & Computer Use",
      "claim": "Builds agents that operate real software, including failure handling rather than only happy paths.",
      "strength": "primary",
      "skills_used": [
        "LangGraph",
        "OpenCode",
        "LLM Inference",
        "Multi-Agent Systems"
      ],
      "evidence": [
        {
          "project": "project-opencode-cua",
          "implementation": "MCP servers, screen capture, macOS Accessibility APIs, browser automation, verification and recovery engines",
          "result": "End-to-end computer-use loop with explicit verification and recovery stages; no usage metric published",
          "verification_url": "https://github.com/Sneh30/opencode-cua-platform",
          "status": "public_repo",
          "checked": "2026-09-30"
        }
      ],
      "related_experience": []
    },
    {
      "id": "capability-multagent",
      "name": "Multi-Agent Orchestration",
      "claim": "Designs multi-agent systems with separation of concerns, verification stages, persistence and observability.",
      "strength": "primary",
      "skills_used": [
        "LangGraph",
        "FastAPI",
        "PostgreSQL",
        "Prometheus",
        "Multi-Agent Systems"
      ],
      "evidence": [
        {
          "project": "project-orchestra",
          "implementation": "5 specialised LangGraph agents (search, extract, verify, synthesize, critique), confidence scoring, Prometheus instrumentation, deterministic CI mode, Docker Compose packaging",
          "result": "5 cooperating agents, verified reports, one-command deployment",
          "verification_url": "https://github.com/Sneh30/Orchestra",
          "status": "public_repo",
          "checked": "2026-09-30"
        }
      ],
      "related_experience": []
    },
    {
      "id": "capability-ai-infra",
      "name": "AI Infrastructure & Autonomous Operations",
      "claim": "Builds AI that operates infrastructure, and has operated real infrastructure.",
      "strength": "primary",
      "skills_used": [
        "LangGraph",
        "FastAPI",
        "Observability",
        "Incident Response",
        "Prometheus",
        "AWS",
        "Docker",
        "Linux"
      ],
      "evidence": [
        {
          "project": "project-deepops",
          "implementation": "Natural-language incident interpretation → root-cause analysis → remediation planning with cost optimisation",
          "result": "Early prototype: the incident-interpretation and remediation-planning modules exist, but the end-to-end pipeline is not yet wired together (not claimed as production)",
          "verification_url": "https://github.com/Sneh30/deepops",
          "status": "public_repo",
          "checked": "2026-09-30"
        }
      ],
      "related_experience": [
        "experience-infrastructure"
      ]
    },
    {
      "id": "capability-llm",
      "name": "LLM Systems & Cost/Latency Engineering",
      "claim": "Treats prompts and inference as an engineering problem with measurable cost outcomes.",
      "strength": "primary",
      "skills_used": [
        "Llama.cpp",
        "LLM Inference",
        "Prompt Engineering",
        "WASM",
        "Metal (GPU)"
      ],
      "evidence": [
        {
          "project": "project-promptos",
          "implementation": "Rust lexer/parser/AST, ten optimisation passes, four compilation modes, WASM plugins, local Llama.cpp inference on Metal, SwiftUI shell",
          "result": "Up to 80–85% lower inference cost in Economy mode (résumé PDF)",
          "verification_url": "https://github.com/Sneh30/promptos",
          "status": "public_repo",
          "checked": "2026-09-30"
        }
      ],
      "related_experience": []
    },
    {
      "id": "capability-ops",
      "name": "Real-World Infrastructure Operations",
      "claim": "Has improved reliability of a live, scaled environment with a measured outcome.",
      "strength": "primary",
      "skills_used": [
        "Observability",
        "Incident Response",
        "Linux",
        "AWS",
        "Docker",
        "Prometheus"
      ],
      "evidence": [
        {
          "project": null,
          "implementation": "One-month on-site engagement strengthening operations on a 400+ node exam-facility network",
          "result": "~40% reduction in incidents and average recovery time versus the prior comparable period",
          "verification_url": "https://sneh30.github.io/portfolio/#experience",
          "status": "documented_experience",
          "checked": "2026-09-30",
          "status_note": "Self-published on the portfolio; employer and raw incident data are not disclosed, so the ~40% figure is the candidate's own account."
        }
      ],
      "related_experience": [
        "experience-infrastructure"
      ]
    },
    {
      "id": "capability-embedded",
      "name": "Embedded Systems & Edge Computer Vision",
      "claim": "Ships computer vision and firmware on constrained hardware, choosing algorithms to fit the resource budget.",
      "strength": "primary",
      "skills_used": [
        "ESP32",
        "Edge Impulse",
        "Raspberry Pi",
        "Edge AI"
      ],
      "evidence": [
        {
          "project": "project-esp32",
          "implementation": "HSV quantisation and centroid tracking, on-device RGB565→RGB888→HSV conversion, L298N motor control, MJPEG streaming across 15+ board variants",
          "result": "3 projects, 60 FPS streaming, 15+ board variants, 512 KB RAM ceiling",
          "verification_url": "https://github.com/Sneh30/esp32-cam-vision",
          "status": "public_repo",
          "checked": "2026-09-30"
        }
      ],
      "related_experience": [
        "experience-team-stratos"
      ]
    },
    {
      "id": "capability-safety",
      "name": "Safety-Critical Systems Engineering",
      "claim": "Has owned a safety-critical embedded subsystem through physical validation.",
      "strength": "supported",
      "skills_used": [
        "ESP32",
        "Edge Impulse"
      ],
      "evidence": [
        {
          "project": null,
          "implementation": "ABS (Anti-lock Braking System) firmware for a 50-member team's competition vehicle; software and electronics integration lead",
          "result": "Validated through track testing, SAE India scrutineering and competition; 11-month tenure",
          "verification_url": "https://sneh30.github.io/portfolio/#experience",
          "status": "documented_experience",
          "checked": "2026-09-30",
          "status_note": "Team project; the code is not released, so only the documented outcome can be checked."
        }
      ],
      "related_experience": [
        "experience-team-stratos"
      ]
    },
    {
      "id": "capability-oss",
      "name": "Software Engineering & Open-Source Contribution",
      "claim": "Has landed reviewed, tested code in an externally maintained project.",
      "strength": "primary",
      "skills_used": [
        "Git",
        "GitHub Actions",
        "CI/CD"
      ],
      "evidence": [
        {
          "project": "project-webcmd",
          "implementation": "Single source of truth for -f/--format parsing across builtin commands; 16 tests added; docs updated",
          "result": "PR #190 merged 2026-08-13 with 16 commits; 15+ commands fixed; upstream 2,620 stars",
          "verification_url": "https://github.com/agentrhq/webcmd/pull/190",
          "status": "external_validation",
          "checked": "2026-09-30",
          "status_note": "Merged upstream pull request, verified against the GitHub API on 2026-09-30."
        }
      ],
      "related_experience": []
    },
    {
      "id": "capability-backend",
      "name": "Backend & Full-Stack Delivery",
      "claim": "Delivers complete applications with persistence, APIs and integrations.",
      "strength": "supported",
      "skills_used": [
        "Python",
        "Java",
        "SQL",
        "FastAPI",
        "PostgreSQL",
        "REST APIs"
      ],
      "evidence": [
        {
          "project": "project-catalyst",
          "implementation": "Resume audit, job scraping, ATS resume generation, weekly tracker backed by SQLite",
          "result": "Working end-to-end product flow; no usage metrics published",
          "verification_url": "https://github.com/Sneh30/Catalyst",
          "status": "public_repo",
          "checked": "2026-09-30"
        },
        {
          "project": "project-portfolio",
          "implementation": "Canvas rendering, GSAP scroll choreography, live GitHub and LeetCode API integration, Agent Mode record",
          "result": "Publicly deployed site",
          "verification_url": "https://github.com/Sneh30/portfolio",
          "status": "public_repo",
          "checked": "2026-09-30"
        }
      ],
      "related_experience": []
    }
  ],
  "explicit_non_claims": [
    "No claim of published research or peer-reviewed papers.",
    "No claim of model training, fine-tuning, or MLOps at scale.",
    "No claim of employer-scale production deployment of DeepOps or Orchestra.",
    "No claim of maintainership, funding, or stars on the candidate's own repositories.",
    "No claim of Kubernetes experience (Kubernetes is not listed in the Human Mode skills section).",
    "No certifications, awards, or rankings are claimed."
  ],
  "evidence_labels": {
    "note": "Qualitative labels only — no numeric evidence scores, fit percentages, or rankings are computed. `status` always holds exactly one value from `values`; `qualifiers` holds any additional ones.",
    "values": [
      "public_repo",
      "private_available_on_request",
      "external_validation",
      "documented_experience",
      "self_reported",
      "experimental"
    ],
    "definitions": {
      "public_repo": "Repository exists and is publicly readable without authentication (HTTP 200 on 2026-10-20), so the claim can be checked by anyone.",
      "private_available_on_request": "Repository exists but is private: anonymous visitors get HTTP 404. The candidate can provide access to recruiters on request, so the work is real and inspectable on request, but it is not independently verifiable without that access.",
      "external_validation": "Reviewed and accepted by someone outside this portfolio (for example a merged upstream pull request).",
      "documented_experience": "Described in the résumé published with this portfolio; not independently verifiable here.",
      "self_reported": "A metric stated by the candidate (for example the PromptOS cost reduction); no raw data is published.",
      "experimental": "Early prototype or active development; not a claimed production deployment."
    }
  }
}