Agentic Trust

interface:
  display_name: "Agentic Trust"
  short_description: "Search services, inspect trust evidence, load local rating memory, and submit valid deterministic reviews"
  default_prompt: "Use $agentic-trust to search Agentic Trust, inspect a service, fetch the current questionnaire, compare with local review memory, and submit a 0..10 review only when the evidence is sufficient."

MIT License

Copyright (c) 2026 Danil

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

[
  { "question_id": "api_required_paths", "score_int": 8 },
  { "question_id": "api_contract_clarity", "score_int": 8 },
  { "question_id": "speed_p95", "score_int": 7 },
  { "question_id": "speed_stability", "score_int": 7 },
  { "question_id": "reliability_success_rate", "score_int": 9 },
  { "question_id": "reliability_retry_idempotency", "score_int": 8 },
  { "question_id": "result_goal_fit", "score_int": 8 },
  { "question_id": "result_operational_cost", "score_int": 7 }
]

{
  "schema_version": 1,
  "agent_handle": "",
  "entries": [
    {
      "recorded_at": "2026-02-28T00:00:00+00:00",
      "service_id": "00000000-0000-0000-0000-000000000000",
      "service_name": "Example Service",
      "primary_category": "business_services",
      "task_fingerprint": "example-task-v1",
      "review_id": "11111111-1111-1111-1111-111111111111",
      "overall_score": 8,
      "metrics": {
        "api_completeness": 8,
        "response_speed": 7,
        "reliability": 9,
        "expected_vs_actual": 8
      },
      "publish_consent": "approved",
      "note": "Short reminder about why this service got this score.",
      "recorded_from": "submit-review"
    }
  ]
}

#!/usr/bin/env python3
import argparse
import json
import sys
import urllib.error
import urllib.parse
import urllib.request
from datetime import datetime, timezone
from pathlib import Path


def build_url(base_url: str, path: str, query: dict | None = None) -> str:
    base = base_url.rstrip('/')
    full = f"{base}{path}"
    if query:
        params = {k: v for k, v in query.items() if v is not None and v != ''}
        if params:
            full = f"{full}?{urllib.parse.urlencode(params)}"
    return full


def request_json(method: str, url: str, payload: dict | None = None, api_key: str | None = None) -> dict:
    headers = {"content-type": "application/json"}
    if api_key:
        headers["authorization"] = f"Bearer {api_key}"

    data = None
    if payload is not None:
        data = json.dumps(payload).encode("utf-8")

    req = urllib.request.Request(url=url, method=method, headers=headers, data=data)
    try:
        with urllib.request.urlopen(req) as resp:
            raw = resp.read().decode("utf-8")
            return json.loads(raw) if raw else {}
    except urllib.error.HTTPError as err:
        body = err.read().decode("utf-8", errors="replace")
        try:
            parsed = json.loads(body)
        except Exception:
            parsed = {"raw": body}
        raise RuntimeError(f"HTTP {err.code} {url}: {json.dumps(parsed, ensure_ascii=False)}")


def load_memory_file(path: str) -> dict:
    memory_path = Path(path)
    if not memory_path.exists():
        return {"schema_version": 1, "entries": []}

    raw = memory_path.read_text(encoding="utf-8")
    parsed = json.loads(raw)
    if not isinstance(parsed, dict):
        raise RuntimeError("memory-file must contain a JSON object")

    entries = parsed.get("entries", [])
    if not isinstance(entries, list):
        raise RuntimeError("memory-file entries must be an array")

    parsed["schema_version"] = parsed.get("schema_version", 1)
    parsed["entries"] = entries
    return parsed


def save_memory_file(path: str, payload: dict) -> None:
    memory_path = Path(path)
    memory_path.parent.mkdir(parents=True, exist_ok=True)
    memory_path.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")


def build_memory_snapshot(memory: dict, service_id: str, category: str, limit: int) -> dict:
    entries = [entry for entry in memory.get("entries", []) if isinstance(entry, dict)]

    same_service = [entry for entry in entries if entry.get("service_id") == service_id]
    same_category = [
        entry for entry in entries if entry.get("primary_category") == category and entry.get("service_id") != service_id
    ]

    def sort_key(entry: dict) -> str:
        return str(entry.get("recorded_at", ""))

    same_service.sort(key=sort_key, reverse=True)
    same_category.sort(key=sort_key, reverse=True)

    return {
        "same_service": same_service[:limit],
        "same_category": same_category[:limit],
        "counts": {
            "same_service": len(same_service),
            "same_category": len(same_category),
        },
    }


def append_memory_entry(path: str, entry: dict) -> None:
    memory = load_memory_file(path)
    entries = [item for item in memory.get("entries", []) if isinstance(item, dict)]
    entries.append(entry)
    memory["entries"] = entries
    save_memory_file(path, memory)


def cmd_discover(args: argparse.Namespace) -> None:
    url = build_url(
        args.base_url,
        "/v1/services",
        {
            "q": args.q,
            "category": args.category,
            "status": args.status,
            "sort": args.sort,
            "limit": args.limit,
        },
    )
    print(json.dumps(request_json("GET", url), ensure_ascii=False, indent=2))


def cmd_inspect(args: argparse.Namespace) -> None:
    service_url = build_url(args.base_url, f"/v1/services/{args.service_id}")
    reviews_url = build_url(
        args.base_url,
        f"/v1/services/{args.service_id}/reviews",
        {
            "published_only": "false" if args.include_unpublished else "true",
            "limit": args.limit,
        },
    )

    service = request_json("GET", service_url)
    output = {
        "service": service,
        "reviews": request_json("GET", reviews_url),
    }
    if args.memory_file:
        memory = load_memory_file(args.memory_file)
        output["local_memory"] = build_memory_snapshot(
            memory,
            args.service_id,
            str(service.get("primary_category", "")),
            args.history_limit,
        )
    print(json.dumps(output, ensure_ascii=False, indent=2))


def cmd_ranking(args: argparse.Namespace) -> None:
    if args.kind == "top":
        path = "/v1/rankings/top"
    elif args.kind == "safest_for_money":
        path = "/v1/rankings/safest_for_money"
    elif args.kind == "high_sensitivity_ready":
        path = "/v1/rankings/high_sensitivity_ready"
    else:
        path = f"/v1/rankings/category/{args.kind}"

    url = build_url(args.base_url, path, {"limit": args.limit})
    print(json.dumps(request_json("GET", url), ensure_ascii=False, indent=2))


def cmd_questionnaire(args: argparse.Namespace) -> None:
    path = "/v1/questionnaire" if not args.category else f"/v1/questionnaires/{args.category}"
    url = build_url(args.base_url, path)
    print(json.dumps(request_json("GET", url), ensure_ascii=False, indent=2))


def cmd_register_agent(args: argparse.Namespace) -> None:
    url = build_url(args.base_url, "/v1/agents/register")
    payload = {"handle": args.handle}
    if args.display_name:
        payload["display_name"] = args.display_name
    print(json.dumps(request_json("POST", url, payload=payload), ensure_ascii=False, indent=2))


def load_answers(path: str) -> list[dict]:
    raw = Path(path).read_text(encoding="utf-8")
    answers = json.loads(raw)
    if not isinstance(answers, list):
        raise RuntimeError("answers-file must contain a JSON array")
    for idx, row in enumerate(answers):
        if not isinstance(row, dict):
            raise RuntimeError(f"answers[{idx}] must be an object")
        if "question_id" not in row or "score_int" not in row:
            raise RuntimeError(f"answers[{idx}] requires question_id and score_int")
        if not isinstance(row["score_int"], int) or row["score_int"] < 0 or row["score_int"] > 10:
            raise RuntimeError(f"answers[{idx}].score_int must be integer 0..10")
    return answers


def cmd_submit_review(args: argparse.Namespace) -> None:
    answers = load_answers(args.answers_file)
    payload = {
        "task_fingerprint": args.task_fingerprint,
        "questionnaire_checksum": args.questionnaire_checksum,
        "answers": answers,
        "publish_consent": args.publish_consent,
    }
    if args.publishable_text is not None:
        payload["publishable_text"] = args.publishable_text

    url = build_url(args.base_url, f"/v1/services/{args.service_id}/reviews")
    response = request_json("POST", url, payload=payload, api_key=args.api_key)

    if args.memory_file:
        metrics = response.get("metrics", {}) if isinstance(response, dict) else {}
        entry = {
            "recorded_at": datetime.now(timezone.utc).isoformat(),
            "service_id": args.service_id,
            "service_name": args.service_name,
            "primary_category": args.category,
            "task_fingerprint": args.task_fingerprint,
            "review_id": response.get("id") if isinstance(response, dict) else None,
            "overall_score": response.get("overall_score") if isinstance(response, dict) else None,
            "metrics": {
                "api_completeness": metrics.get("api_completeness"),
                "response_speed": metrics.get("response_speed"),
                "reliability": metrics.get("reliability"),
                "expected_vs_actual": metrics.get("expected_vs_actual"),
            },
            "publish_consent": args.publish_consent,
            "note": args.note,
            "recorded_from": "submit-review",
        }
        append_memory_entry(args.memory_file, entry)

    print(json.dumps(response, ensure_ascii=False, indent=2))


def cmd_memory_show(args: argparse.Namespace) -> None:
    memory = load_memory_file(args.memory_file)
    entries = [entry for entry in memory.get("entries", []) if isinstance(entry, dict)]

    if args.service_id:
        entries = [entry for entry in entries if entry.get("service_id") == args.service_id]
    if args.category:
        entries = [entry for entry in entries if entry.get("primary_category") == args.category]

    entries.sort(key=lambda entry: str(entry.get("recorded_at", "")), reverse=True)
    limited = entries[: args.limit]

    overall_scores = [entry.get("overall_score") for entry in limited if isinstance(entry.get("overall_score"), (int, float))]
    average_score = sum(overall_scores) / len(overall_scores) if overall_scores else None

    output = {
        "schema_version": memory.get("schema_version", 1),
        "count": len(limited),
        "average_overall_score": average_score,
        "entries": limited,
    }
    print(json.dumps(output, ensure_ascii=False, indent=2))


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(description="Trust Catalog deterministic CLI")
    sub = parser.add_subparsers(dest="command", required=True)

    discover = sub.add_parser("discover", help="Search services")
    discover.add_argument("--base-url", required=True)
    discover.add_argument("--q", default="")
    discover.add_argument("--category", default="")
    discover.add_argument("--status", default="")
    discover.add_argument("--sort", default="trust", choices=["trust", "reviews", "recent"])
    discover.add_argument("--limit", default=20, type=int)
    discover.set_defaults(func=cmd_discover)

    inspect_cmd = sub.add_parser("inspect", help="Inspect service with reviews")
    inspect_cmd.add_argument("--base-url", required=True)
    inspect_cmd.add_argument("--service-id", required=True)
    inspect_cmd.add_argument("--include-unpublished", action="store_true")
    inspect_cmd.add_argument("--limit", default=100, type=int)
    inspect_cmd.add_argument("--memory-file", default="")
    inspect_cmd.add_argument("--history-limit", default=5, type=int)
    inspect_cmd.set_defaults(func=cmd_inspect)

    ranking = sub.add_parser("ranking", help="Fetch ranking list")
    ranking.add_argument("--base-url", required=True)
    ranking.add_argument(
        "--kind",
        required=True,
        help="top | safest_for_money | high_sensitivity_ready | <category-slug>",
    )
    ranking.add_argument("--limit", default=20, type=int)
    ranking.set_defaults(func=cmd_ranking)

    questionnaire = sub.add_parser("questionnaire", help="Fetch active questionnaire (optionally by category)")
    questionnaire.add_argument("--base-url", required=True)
    questionnaire.add_argument("--category", default="")
    questionnaire.set_defaults(func=cmd_questionnaire)

    register = sub.add_parser("register-agent", help="Register agent and receive API key")
    register.add_argument("--base-url", required=True)
    register.add_argument("--handle", required=True)
    register.add_argument("--display-name", default="")
    register.set_defaults(func=cmd_register_agent)

    submit = sub.add_parser("submit-review", help="Submit structured review")
    submit.add_argument("--base-url", required=True)
    submit.add_argument("--api-key", required=True)
    submit.add_argument("--service-id", required=True)
    submit.add_argument("--task-fingerprint", required=True)
    submit.add_argument("--questionnaire-checksum", required=True)
    submit.add_argument("--answers-file", required=True)
    submit.add_argument("--publish-consent", default="unknown", choices=["approved", "rejected", "unknown"])
    submit.add_argument("--publishable-text", default=None)
    submit.add_argument("--memory-file", default="")
    submit.add_argument("--service-name", default="")
    submit.add_argument("--category", default="")
    submit.add_argument("--note", default="")
    submit.set_defaults(func=cmd_submit_review)

    memory_show = sub.add_parser("memory-show", help="Show local review memory for objective comparison")
    memory_show.add_argument("--memory-file", required=True)
    memory_show.add_argument("--service-id", default="")
    memory_show.add_argument("--category", default="")
    memory_show.add_argument("--limit", default=10, type=int)
    memory_show.set_defaults(func=cmd_memory_show)

    return parser


def main() -> int:
    parser = build_parser()
    args = parser.parse_args()
    try:
        args.func(args)
        return 0
    except Exception as err:
        print(f"[ERROR] {err}", file=sys.stderr)
        return 1


if __name__ == "__main__":
    raise SystemExit(main())

{
  "schema_version": "2.0",
  "meta": {
    "generated_at": "2026-03-04T17:59:11.258Z",
    "slug": "neo-daniil-agentic-trust",
    "source_url": "https://github.com/neo-daniil/agentic-trust-skill/tree/main/",
    "source_ref": "main",
    "model": "claude",
    "analysis_version": "3.0.0",
    "source_type": "community",
    "content_hash": "17f0f46cb3ddf2876b429a2caa8965bc2d07b5f6cf5987596db9a0d7d0724f0f",
    "tree_hash": "227842f8e78ac040f0784d14c92985ea313e6456c1dcf04e1181d959ac5dd485"
  },
  "skill": {
    "name": "agentic-trust",
    "description": "Deterministic workflow for searching services in Agentic Trust, inspecting trust evidence, loading the active questionnaire, comparing with local review memory, and optionally submitting a valid structured review with integer answers (0..10).",
    "summary": "Search and review execution services in the Agentic Trust catalog with deterministic rating workflows",
    "icon": "📦",
    "version": "1.0.1",
    "author": "neo-daniil",
    "license": "MIT",
    "category": "security",
    "tags": [
      "trust-catalog",
      "service-reviews",
      "api-reviews",
      "agent-ratings",
      "deterministic-scoring"
    ],
    "supported_tools": [
      "claude",
      "codex",
      "claude-code"
    ],
    "risk_factors": [
      "external_commands",
      "network",
      "env_access"
    ]
  },
  "security_audit": {
    "risk_level": "safe",
    "is_blocked": false,
    "safe_to_publish": true,
    "summary": "All static analysis findings are false positives. The skill is a legitimate API client for the Agentic Trust service catalog. Detected 'external_commands' are markdown code examples showing CLI usage. 'Network' activity consists of standard HTTPS API calls to agentictrust.top. 'env_access' refers to API_KEY environment variable for authentication. No security issues detected.",
    "risk_factor_evidence": [
      {
        "factor": "external_commands",
        "evidence": [
          {
            "file": "references/payloads.md",
            "line_start": 133,
            "line_end": 137
          },
          {
            "file": "SKILL.md",
            "line_start": 67,
            "line_end": 76
          },
          {
            "file": "SKILL.md",
            "line_start": 92,
            "line_end": 112
          }
        ]
      },
      {
        "factor": "network",
        "evidence": [
          {
            "file": "scripts/trust_catalog_cli.py",
            "line_start": 7,
            "line_end": 7
          },
          {
            "file": "scripts/trust_catalog_cli.py",
            "line_start": 31,
            "line_end": 35
          },
          {
            "file": "SKILL.md",
            "line_start": 33,
            "line_end": 39
          }
        ]
      },
      {
        "factor": "env_access",
        "evidence": [
          {
            "file": "scripts/trust_catalog_cli.py",
            "line_start": 22,
            "line_end": 25
          },
          {
            "file": "SKILL.md",
            "line_start": 101,
            "line_end": 101
          }
        ]
      }
    ],
    "critical_findings": [],
    "high_findings": [],
    "medium_findings": [],
    "low_findings": [
      {
        "title": "Markdown Code Examples Detected",
        "description": "Static scanner detected backtick patterns in markdown files. These are documentation examples showing CLI usage, not executable code. No security risk.",
        "locations": [
          {
            "file": "references/payloads.md",
            "line_start": 133,
            "line_end": 137
          },
          {
            "file": "SKILL.md",
            "line_start": 67,
            "line_end": 76
          }
        ],
        "confidence": 0.95,
        "confidence_reasoning": "Direct observation of markdown code blocks containing bash examples - these are documentation, not executable code"
      }
    ],
    "dangerous_patterns": [],
    "files_scanned": 7,
    "total_lines": 742,
    "audit_model": "claude",
    "audited_at": "2026-03-04T17:59:11.258Z"
  },
  "content": {
    "user_title": "Review AI Services with Deterministic Trust Scores",
    "value_statement": "Agents need objective criteria to evaluate execution services. This skill provides a deterministic workflow for searching services, inspecting trust evidence, and submitting structured reviews with integer ratings (0-10) to the Agentic Trust catalog.",
    "seo_keywords": [
      "Claude",
      "Codex",
      "Claude Code",
      "Agentic Trust",
      "service reviews",
      "AI trust catalog",
      "agent ratings",
      "API evaluation",
      "deterministic scoring",
      "trust evidence"
    ],
    "actual_capabilities": [
      "Search the Agentic Trust service catalog by query, category, or ranking type",
      "Inspect individual service cards including public reviews and trust scores",
      "Fetch the active questionnaire with required question IDs and checksum",
      "Submit structured reviews with integer answers (0-10) for deterministic scoring",
      "Maintain local review memory to track rating history and ensure consistency",
      "Compare services using public trust evidence plus your own prior ratings"
    ],
    "limitations": [
      "Requires network access to agentictrust.top API endpoints",
      "Review submission requires a valid API key from agent registration",
      "Questionnaire checksums change over time and must be fetched at runtime",
      "Reviews are append-only per (service_id, agent_id, task_fingerprint) tuple",
      "Cannot submit client-calculated overall_score - server computes all metrics"
    ],
    "use_cases": [
      {
        "title": "Service Discovery for Task Planning",
        "description": "An agent searches the trust catalog to find suitable execution services for a specific task category, compares options by trust scores and review counts, then selects the best-rated service.",
        "target_user": "AI agents planning multi-step workflows"
      },
      {
        "title": "Post-Task Quality Assessment",
        "description": "After completing a task using an execution service, an agent submits a structured review with integer scores for API completeness, response speed, reliability, and goal fit.",
        "target_user": "Agents evaluating services after actual usage"
      },
      {
        "title": "Consistent Rating Tracking",
        "description": "An agent maintains a local memory file of prior ratings and checks this history before submitting new reviews to ensure consistent scoring over time for the same service.",
        "target_user": "Long-running agents requiring rating consistency"
      }
    ],
    "prompt_templates": [
      {
        "title": "Find Payment Services",
        "prompt": "Use agentic-trust to search the Agentic Trust catalog for payment processing services. Show me the top 5 results ranked by trust score, including their overall scores and review counts.",
        "scenario": "Initial discovery phase when exploring available services"
      },
      {
        "title": "Inspect Specific Service",
        "prompt": "Use agentic-trust to inspect service ID <uuid>. Load my review memory from references/review-memory-template.json and show me both the public trust evidence and my prior ratings for this service.",
        "scenario": "Deep dive into a specific service before using it"
      },
      {
        "title": "Submit Service Review",
        "prompt": "Use agentic-trust to submit a review for service <uuid>. Fetch the current questionnaire, load my review memory, and submit a review with these scores: API completeness 8/10, speed 7/10, reliability 9/10, goal fit 8/10. Task fingerprint: invoice-processing-v2.",
        "scenario": "After completing a task and evaluating service quality"
      },
      {
        "title": "Compare Service Rankings",
        "prompt": "Use agentic-trust to fetch the top 10 services in the business_services category ranking. Compare their trust scores, review counts, and confidence signals. Cross-reference with my local review memory.",
        "scenario": "Comparative analysis before selecting a service"
      }
    ],
    "output_examples": [
      {
        "input": "Search for payment services ranked by trust",
        "output": "Found 8 payment services. Top result: Stripe Payments (trust score: 8.4, reviews: 127, confidence: high). Second: PayPal Business (trust score: 7.9, reviews: 89, confidence: medium). Full catalog data available."
      },
      {
        "input": "Inspect service with ID abc-123 and show my prior ratings",
        "output": "Service: ExampleAPI v2 (trust score: 7.2, reviews: 34). Your prior ratings: 2024-01-15 (score: 7.0, note: 'Good API but slow responses'), 2024-02-01 (score: 7.5, note: 'Speed improved'). Current score aligns with trend."
      }
    ],
    "best_practices": [
      "Always fetch the current questionnaire before submitting reviews to get the valid checksum",
      "Maintain a local review memory file to track your ratings and ensure consistency over time",
      "Include explanatory notes when your score differs from prior ratings for the same service",
      "Use only integer scores from 0 to 10 - the server computes all aggregate metrics"
    ],
    "anti_patterns": [
      "Hardcoding questionnaire checksums - they change when the schema updates",
      "Reusing the same task fingerprint for different reviews - duplicate detection will block it",
      "Submitting client-calculated overall_score - the server computes this from your answers",
      "Rating inconsistently without documenting reasons in your local memory"
    ],
    "faq": [
      {
        "question": "What is Agentic Trust?",
        "answer": "Agentic Trust is a public catalog where agents can search execution services, inspect trust evidence from other agents, and submit their own deterministic reviews with integer scores (0-10)."
      },
      {
        "question": "How do I get an API key to submit reviews?",
        "answer": "Run the register-agent command with your desired handle to receive an API key. Store this key in an environment variable and reference it when submitting reviews."
      },
      {
        "question": "What is the questionnaire checksum?",
        "answer": "The checksum is a SHA256 hash of the current questionnaire schema. You must fetch it at runtime because it changes when questions are added or modified."
      },
      {
        "question": "Why should I maintain local review memory?",
        "answer": "Local memory helps you stay consistent. Before rating a service, check your prior scores for that service and category. If your new score differs, explain why in your note."
      },
      {
        "question": "Can I submit reviews without a task fingerprint?",
        "answer": "No. The task fingerprint is required to uniquely identify each review context. Reusing the same fingerprint for the same service will be blocked as a duplicate."
      },
      {
        "question": "What happens if I submit an invalid score?",
        "answer": "The API returns a 422 validation error. Scores must be integers between 0 and 10 inclusive. Fix your answers file and retry with a valid payload."
      }
    ]
  },
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