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Explorer/MCP/angelo-leone/talent-augmenting-layer
✓ REGISTRY VERIFIEDREMOTE◉ LIVE SCAN

Talent-Augmenting Layer

angelo-leone/talent-augmenting-layer

Personalised AI augmentation system — makes you better at your work, not dependent on AI

15 tools available
angelo-leone/talent-augmenting-layerExternal link; availability not verified
The Journeyman with a Quirk
Some history here. A few things flagged; nothing serious, but we wanted you to know.
Time indexed (4mo)
15toolsRemote/ HTTP4moindexed2signals
Checked Aug 16, 2026
Quality Score
31/95
Emerging
Risk Score
12/100
Low
How is this calculated?
Quality Breakdown
Tenure13.1/20
120 days indexed
Capability18.1/25
Tools: 6.1/13 (15 tools)
Description: 5/5
Endpoint: 7/7
Adoption0/25
Use count: 0/20 (0 uses)
Multi-registry: 0/5 (1 registry)
Reliability0/25
Currently live: 0/10
Uptime history: 0/15 No checks yet
Security scan: 0 pts in v1.0; ready to weight when coverage improves
Incomplete Data Cap (60)
Usage data is not available for this server. Quality is capped until adoption can be measured.
Risk
12Low
See Security Signals section for details.
The scanner shows
15 tools. Registry verified. A few things worth knowing about. Check the details below.
First indexed Apr 18, 2026
Security Signals2 rule groups · 2 findings
Sensitive parameter names detectedHigh
"session_id" parameter in talent_parse_telemetry
No authentication requiredHigh
Server responded without authentication
Server Profile
Tools catalogued
15
15 tools available. Full list below.
Hosting
Remote / HTTP
Runs on the internet. No access to your filesystem, SSH keys, or environment variables.
Registry presence
Verified
Listed on the Official MCP Registry under Linux Foundation governance.
Liveness
No liveness checks recorded yet.
Publisher Verification
✓ Listed on the Official MCP Registry under Linux Foundation governance.
Published version: 1.0.0
angelo-leone/talent-augmenting-layerExternal link; availability not verified
Connect to this server
STREAMABLE-HTTP
https://proworker-hosted.onrender.com/mcp
Tools (15)We asked this server directly for its tool list on Apr 19.
talent_get_profile
Load a Talent-Augmenting Layer profile by name. Returns the full profile with expertise map, calibration settings, task classification, and red lines. Use this at the start of every conversation.
talent_get_calibration
Get the Talent-Augmenting Layer calibration settings for a user. Returns a compact JSON block suitable for injecting into any LLM system prompt. Includes friction levels, coaching domains, red lines, and interaction preferences.
talent_classify_task
Classify a task according to the user's Talent-Augmenting Layer profile. Returns one of: automate, augment, coach, protect, hands_off — along with the recommended AI behaviour for that task.
talent_log_interaction
Log an interaction for skill tracking. Call this after substantive AI interactions to track the user's engagement patterns and skill development.
talent_get_progression
Get skill progression analysis for a user. Shows interaction counts, engagement patterns, domain-level growth/atrophy signals, and warnings about potential de-skilling.
talent_list_profiles
List all available Talent-Augmenting Layer profiles.
Show all 15 tools ↓
talent_status
Get a comprehensive status report for a user: profile summary, current calibration, skill progression stats, trend direction, atrophy warnings, and recommended next actions. Use this for a quick overview at the start of a conversation.
talent_org_summary
Get an organisation-level summary across all profiles. Shows aggregate dependency risk, growth potential, expertise distribution, trend alerts, and per-domain skill breakdown. For org dashboards.
talent_delete_profile
Delete a user's profile and interaction logs.
talent_save_profile
Save or update a user's profile markdown content. Use this after running /talent-assess to write the generated profile, or after /talent-update to save changes.
talent_assess_start
Start a Talent-Augmenting Layer onboarding assessment. Returns the full assessment protocol with all questions, behavioural anchors, and instructions for how to run the assessment conversationally. The chatbot uses this to ask questions one at a time, collect answers, then call talent_assess_score and talent_assess_create_profile to compute scores and save the profile. Call this at the beginning of any onboarding conversation.
talent_assess_score
Compute all Talent-Augmenting Layer scores from raw assessment answers. Takes the numeric answers collected during the assessment (A1-A5, B1-B5, D1-D4 as integers 1-5) and domain expertise ratings. Returns computed ADR, GP, ALI, ESA, and composite TALRI scores with interpretations and recommended calibration.
talent_assess_create_profile
Generate and save a complete Talent-Augmenting Layer profile from assessment data. Call this after talent_assess_score to create the profile file. Takes the computed scores, demographic info, goals, task classifications, and preferences collected during the assessment conversation. Returns the generated profile and saves it to disk.
talent_suggest_domains
Suggest expertise domains for a user based on their role, industry, and responsibilities. Returns a curated list of domain suggestions with descriptions drawn from an industry-specific taxonomy. Use this during the assessment to help identify relevant domains for the Expertise Self-Assessment (ESA). The LLM has override authority and can add or remove domains from the suggestions.
talent_parse_telemetry
Parse <tal_log> telemetry blocks from an LLM response and record them. The system prompt instructs the LLM to emit <tal_log> JSON blocks after each substantive interaction. Call this tool with the full LLM response text to extract and log all telemetry entries. Each entry is saved to the local JSONL interaction log and optionally pushed to the hosted API.

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