Tag

#sse

144 results found

Verify Action
@Armada735

Verify AI agent tool calls with content-addressed, HMAC-attested receipts. Free third-party verification API for AI agents. Call verify_action(claim, evidence) to get an independent integrity check on whether your claimed action matches the actual evidence. Useful for catching silent failures: incorrect SQL operations, file-op mismatches, API call inconsistencies, and code-diff scope creep. Five specialized verifier kinds: - code_diff: verb / path / identifier coherence with unified diff - db_op: row delta + SQL operation + ID match - file_op: existence state + line/size delta - api_call: request body and response status coherence - generic: conservative fallback Returns: - aar_verdict: verified | contradicted | insufficient_evidence | unsafe_to_verify - verdict: ok | mismatch | uncertain (legacy 3-value alias) - reasoning, confidence - receipt: verify_action_receipt.v0 with HMAC-SHA256 signature, content-addressed via SHA-256 hashes of claim and evidence Cross-vendor: works with Claude Code, Cursor, Cline, Codex, Codeium, and any MCP-compatible harness. Stateless, per-request, no API key, no registration. Pure Python stdlib (no pip install). Anonymized telemetry only — no PII, no model fingerprint, no raw claim/evidence retention. Honest scope: this is a small reference implementation, not a canonical inter-vendor standard. v0 receipts use HMAC-SHA256 (symmetric, single-issuer); v1 with ed25519 + multi-issuer is on the roadmap. The hosted endpoint has no SLA — self-host for stability (git clone && ./start.sh). 90-day probe with explicit kill criteria. If adoption appears, v1 schema work begins. If response is null, the null is itself a publishable data point.

a month ago
Lexicon
@Nadine

2 months ago
Careerproof
@dontellu77

Career and workforce intelligence built on a deep HR ontology — skill taxonomies, role definitions and responsibilities, compensation and incentive structures, learning and development pathways, sourcing strategies, and role/skill evolution mapping. This structured foundation, combined with a RAG knowledge base curated from 50+ premium sources (HBR, McKinsey, BCG, Gartner, Forrester) and updated 3x daily with live web research, powers 6 guided skills and 42 MCP tools for two audiences: working professionals getting personalized career intelligence (CV optimization, salary benchmarking, career strategy), and HR/TA teams running structured talent evaluation, candidate shortlisting, compensation analysis, and consulting-grade workforce research reports. Example Use Cases (for HR/TA teams): 1. Custom Evaluation Models — Train CareerProof on your organization's existing assessment rubrics, scorecards, and evaluation criteria to build custom eval models that evaluate candidates through your specific lens. Upload your competency frameworks and historical assessments, then run inference on new candidates — scored and ranked exactly how your team would, at scale. 2. Candidate Evaluation & Shortlisting — Set up a hiring context with company profile and job description, upload candidate CVs, then batch-rank them with GEM competency scoring and JD-FIT matching. Apply your custom eval models for organization-specific scoring, or deep-dive any candidate with a 360-degree evaluation including tailored interview questions derived from skill taxonomy analysis. 3. Workforce Research Reports — Generate consulting-grade PDF reports across 16 types (salary benchmarking, skills gap analysis, org design, DEI assessment, succession planning, sourcing strategy, and more). Each report is grounded in real-time market data from premium sources and structured around HR ontology — role definitions, compensation structures, L&D pathways, and skill evolution mapping. 4. Compensation & Incentive Benchmarking — Get market-calibrated salary and total compensation intelligence for any role, location, and industry. Analysis is structured around compensation and incentive frameworks from the HR ontology, enriched with live web research and curated knowledge base data covering base salary, equity, bonuses, and benefits. Example Use Cases (for the working professional or career coach): 1. Career Intelligence Chat (Hyper-Personalized) — Ask career strategy questions and get hyper-personalized responses that fuse your CV context with deep insights from the career and workforce RAG knowledge base. Salary benchmarks calibrated to your function and location, industry disruption analysis mapped to your skill profile, and career pivot recommendations grounded in role evolution data — not surface-level answers, but intelligence drawn from the same sources that inform executive strategy. 2. CV Optimization (Hyper-Personalized) — Upload your CV and receive a hyper-personalized positioning pipeline that combines your actual experience with deep insights from our career and workforce RAG knowledge base. Market analysis calibrated to your industry and seniority, career opportunity identification grounded in role/skill evolution data, and targeted edits with trade-off analysis — not generic advice, but intelligence shaped by 50+ premium research sources and your unique career trajectory.

3 months ago
Mockd
@getmockd

3 months ago
Dictionary Server
@meowrain

JavaScript
a year ago