GITLAB_AI_CATALOGgitlab-mcp
Rank
83
A Model Context Protocol (MCP) server for GitLab
Traction
No public download signal
Freshness
Updated 2d ago
MCP
Crawler Summary
agentic-learning-loop answer-first brief
Agentic Learning Loop — Autonomous Skill Improvement Agentic Learning Loop — Autonomous Skill Improvement **Version:** 1.0 **Author:** Orosha **License:** MIT --- What This Skill Does Agentic Learning Loop automatically instruments agent skills, measures their performance, and generates AGENTS.md updates with empirical findings. It creates a continuous feedback loop for skill improvement without human intervention. How It Works Installation Usage Run a learning cycle C Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Freshness
Last checked 4/15/2026
Best For
agentic-learning-loop is best for general automation workflows where MCP and OpenClaw compatibility matters.
Not Ideal For
Contract metadata is missing or unavailable for deterministic execution.
Evidence Sources Checked
editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack
Agent DossierGitHubSafety: 94/100
agentic-learning-loop
Agentic Learning Loop — Autonomous Skill Improvement Agentic Learning Loop — Autonomous Skill Improvement **Version:** 1.0 **Author:** Orosha **License:** MIT --- What This Skill Does Agentic Learning Loop automatically instruments agent skills, measures their performance, and generates AGENTS.md updates with empirical findings. It creates a continuous feedback loop for skill improvement without human intervention. How It Works Installation Usage Run a learning cycle C
MCPself-declared
OpenClawself-declared
Public facts
4
Change events
1
Artifacts
0
Freshness
Apr 15, 2026
Verifiededitorial-contentNo verified compatibility signals
Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Trust evidence available
Trust score
Unknown
Compatibility
MCP, OpenClaw
Freshness
Apr 15, 2026
Vendor
Orosha Ai
Artifacts
0
Benchmarks
0
Last release
Unpublished
Executive Summary
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Setup snapshot
git clone https://github.com/orosha-ai/agentic-learning-loop.git- 1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
- 2
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Evidence Ledger
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor (1)
Vendor
Orosha Ai
profilemedium
Compatibility (1)
Protocol compatibility
MCP, OpenClaw
contractmedium
Security (1)
Handshake status
UNKNOWN
trustmedium
Integration (1)
Crawlable docs
6 indexed pages on the official domain
search_documentmedium
Release & Crawl Timeline
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Artifacts Archive
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
5
Snippets
0
Languages
typescript
Parameters
Executable Examples
text
1. Run a skill with instrumentation 2. Measure: execution time, token usage, task completion 3. Compare to baseline metrics 4. Generate AGENTS.md with performance findings 5. Update skill recommendations based on data
bash
# Copy to your workspace cp -r /path/to/agentic-learning-loop ~/.openclaw/workspace/skills/ # Enable (add to your agent's config or skills directory)
bash
# Run full learning loop on a skill cd ~/.openclaw/workspace/skills/agentic-learning-loop ./learn.sh --skill ../your-skill --iterations 5
bash
# View accumulated learnings cat ~/.openclaw/workspace/skills/agentic-learning-loop/learning-log.md
bash
# Auto-generate AGENTS.md from learning data ./generate-agentsmd.sh --repo /path/to/repo
Docs & README
Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB OPENCLEW
Editorial quality
ready
Agentic Learning Loop — Autonomous Skill Improvement Agentic Learning Loop — Autonomous Skill Improvement **Version:** 1.0 **Author:** Orosha **License:** MIT --- What This Skill Does Agentic Learning Loop automatically instruments agent skills, measures their performance, and generates AGENTS.md updates with empirical findings. It creates a continuous feedback loop for skill improvement without human intervention. How It Works Installation Usage Run a learning cycle C
Full README
Agentic Learning Loop — Autonomous Skill Improvement
Version: 1.0 Author: Orosha License: MIT
What This Skill Does
Agentic Learning Loop automatically instruments agent skills, measures their performance, and generates AGENTS.md updates with empirical findings. It creates a continuous feedback loop for skill improvement without human intervention.
How It Works
1. Run a skill with instrumentation
2. Measure: execution time, token usage, task completion
3. Compare to baseline metrics
4. Generate AGENTS.md with performance findings
5. Update skill recommendations based on data
Installation
# Copy to your workspace
cp -r /path/to/agentic-learning-loop ~/.openclaw/workspace/skills/
# Enable (add to your agent's config or skills directory)
Usage
Run a learning cycle
# Run full learning loop on a skill
cd ~/.openclaw/workspace/skills/agentic-learning-loop
./learn.sh --skill ../your-skill --iterations 5
Check current learning state
# View accumulated learnings
cat ~/.openclaw/workspace/skills/agentic-learning-loop/learning-log.md
Generate AGENTS.md update
# Auto-generate AGENTS.md from learning data
./generate-agentsmd.sh --repo /path/to/repo
Key Features
- Automatic Instrumentation: No code changes needed to skills
- Baseline Comparison: Measures improvement over time
- Multi-metric Tracking: Execution time, tokens, success rate
- AGENTS.md Generation: Creates compliant format
- Learning Log: Records all iterations
Learning Metrics
| Metric | Baseline | Current | Improvement | |---------|-----------|---------|-------------| | Execution time | ~ | ~ | ~ | | Token usage | ~ | ~ | ~ | | Success rate | ~ | ~ | ~ | | Task completion | ~ | ~ | ~ |
Integration with Other Skills
This skill integrates with:
- Agentic Compass: Reflects on learning effectiveness
- Agent Observability Dashboard: Provides metrics
- MCP Registry Manager: Discovers instrumented MCP servers
Philosophy
"The best AGENTS.md is generated from real data, not assumptions."
Every AGENTS.md should be based on:
- Multiple executions (not single runs)
- Controlled comparisons (baseline vs. current)
- Quantitative metrics (time, tokens, success)
- Context-aware analysis (task type, complexity)
Contributing
This skill is part of the Orosha agent ecosystem. Contributions welcome!
License
MIT License - See LICENSE file for details.
Contract & API
Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
MCP: self-declaredOpenClaw: self-declared
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/snapshot"
curl -s "https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/contract"
curl -s "https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/trust"
Reliability & Benchmarks
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
Trust signals
Handshake
UNKNOWN
Confidence
unknown
Attempts 30d
unknown
Fallback rate
unknown
Runtime metrics
Observed P50
unknown
Observed P95
unknown
Rate limit
unknown
Estimated cost
unknown
Do not use if
Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.
Media & Demo
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
No screenshots, media assets, or demo links are available.
Related Agents
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
GITLAB_PUBLIC_PROJECTSgitlab-mcp
Rank
80
A Model Context Protocol (MCP) server for GitLab
Traction
No public download signal
Freshness
Updated 2d ago
MCP
GITLAB_AI_CATALOGrmcp-openapi
Rank
74
Expose OpenAPI definition endpoints as MCP tools using the official Rust SDK for the Model Context Protocol (https://github.com/modelcontextprotocol/rust-sdk)
Traction
No public download signal
Freshness
Updated 2d ago
MCP
GITLAB_AI_CATALOGrmcp-actix-web
Rank
72
An actix_web backend for the official Rust SDK for the Model Context Protocol (https://github.com/modelcontextprotocol/rust-sdk)
Traction
No public download signal
Freshness
Updated 2d ago
MCP
Machine Appendix
Contract JSON
{
"contractStatus": "missing",
"authModes": [],
"requires": [],
"forbidden": [],
"supportsMcp": false,
"supportsA2a": false,
"supportsStreaming": false,
"inputSchemaRef": null,
"outputSchemaRef": null,
"dataRegion": null,
"contractUpdatedAt": null,
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Invocation Guide
{
"preferredApi": {
"snapshotUrl": "https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/snapshot",
"contractUrl": "https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/contract",
"trustUrl": "https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/trust"
},
"curlExamples": [
"curl -s \"https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/snapshot\"",
"curl -s \"https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/contract\"",
"curl -s \"https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"MCP",
"OPENCLEW"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_OPENCLEW",
"generatedAt": "2026-04-16T23:30:26.611Z"
}
},
"retryPolicy": {
"maxAttempts": 3,
"backoffMs": [
500,
1500,
3500
],
"retryableConditions": [
"HTTP_429",
"HTTP_503",
"NETWORK_TIMEOUT"
]
}
}Trust JSON
{
"status": "unavailable",
"handshakeStatus": "UNKNOWN",
"verificationFreshnessHours": null,
"reputationScore": null,
"p95LatencyMs": null,
"successRate30d": null,
"fallbackRate": null,
"attempts30d": null,
"trustUpdatedAt": null,
"trustConfidence": "unknown",
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Capability Matrix
{
"rows": [
{
"key": "MCP",
"type": "protocol",
"support": "unknown",
"confidenceSource": "profile",
"notes": "Listed on profile"
},
{
"key": "OPENCLEW",
"type": "protocol",
"support": "unknown",
"confidenceSource": "profile",
"notes": "Listed on profile"
}
],
"flattenedTokens": "protocol:MCP|unknown|profile protocol:OPENCLEW|unknown|profile"
}Facts JSON
[
{
"factKey": "docs_crawl",
"category": "integration",
"label": "Crawlable docs",
"value": "6 indexed pages on the official domain",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
},
{
"factKey": "vendor",
"category": "vendor",
"label": "Vendor",
"value": "Orosha Ai",
"href": "https://github.com/orosha-ai/agentic-learning-loop",
"sourceUrl": "https://github.com/orosha-ai/agentic-learning-loop",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-04-15T03:12:19.393Z",
"isPublic": true
},
{
"factKey": "protocols",
"category": "compatibility",
"label": "Protocol compatibility",
"value": "MCP, OpenClaw",
"href": "https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/contract",
"sourceUrl": "https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-04-15T03:12:19.393Z",
"isPublic": true
},
{
"factKey": "handshake_status",
"category": "security",
"label": "Handshake status",
"value": "UNKNOWN",
"href": "https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/trust",
"sourceUrl": "https://xpersona.co/api/v1/agents/orosha-ai-agentic-learning-loop/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true
}
]Change Events JSON
[
{
"eventType": "docs_update",
"title": "Docs refreshed: Sign in to GitHub · GitHub",
"description": "Fresh crawlable documentation was indexed for the official domain.",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
}
]