New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Humanrail Escalation

by automateyournetwork in automateyournetwork/netclaw

Human-in-the-loop escalation via HumanRail — route low-confidence agent decisions, pre-destructive operation approvals, and ambiguous incident tickets to real human engineers.

Apache-2.0Auto-check: notesAgent Workflows

Install Humanrail Escalation

skills CLI
$ npx skills add automateyournetwork/netclaw --skill humanrail-escalation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install automateyournetwork/netclaw humanrail-escalation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/automateyournetwork/netclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/workspace/skills/humanrail-escalation .claude/skills/humanrail-escalation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
humanrail-escalation
GitHub stars
676
Token cost
~3.3k tokens
SKILL.md length
971 words
Files
1
Skills in repo
120
Repo updated
First seen
Licence
Apache-2.0

At a glance

Human-in-the-loop escalation via HumanRail — route low-confidence agent decisions, pre-destructive operation approvals, and ambiguous incident tickets to real human engineers.

  • Works in 10 steps: Assess Uncertainty → Frame the Decision Neutrally → Create and Wait → …
  • The agent is uncertain
  • SKILL.md covers Safety — Non-Negotiable Rules, When to Use, MCP Server and How to Run (Local), plus 8 more sections
  • Calls python3 and pip3; reaches humanrail.dev and api.humanrail.dev; needs HUMANRAIL_API_KEY

What it does

Humanrail Escalation is an agent skill from automateyournetwork/netclaw. Human-in-the-loop escalation via HumanRail — route low-confidence agent decisions, pre-destructive operation approvals, and ambiguous incident tickets to real human engineers. Human answers are verified and returned as structured output. Workers are paid via Lightning Network. Use when the agent is uncertain, when a destructive change needs explicit human sign-off beyond a ServiceNow CR, or when an ambiguous ticket requires human triage before automated handling.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Structured output and tool calling and Human-in-the-loop approvals. It works with ServiceNow and Bitcoin. The repository describes itself as: An AI agent that claws through your network. The licence is Apache-2.0.

When your agent uses it

  • The agent is uncertain
  • A destructive change needs explicit human sign-off beyond a ServiceNow CR
  • An ambiguous ticket requires human triage before automated handling

Example prompts

  • “/humanrail-escalation”

Requirements

  • Python 3
  • A credential in HUMANRAIL_API_KEY

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Assess Uncertainty
  2. Frame the Decision Neutrally
  3. Create and Wait
  4. Act on Verified Output
  5. Baseline and Summarize
  6. Build Approval Request
  7. Gate on Response
  8. Extract What's Known
  9. Route to Human Triage
  10. Apply Triage Output

What it can do on your machine

Read from SKILL.md and the folder at commit aa90e7d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3
    • pip3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • humanrail.dev
    • api.humanrail.dev

    Also links to:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • HUMANRAIL_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Humanrail Escalation loads about 3.3k tokens when it runs. Until then it costs about 122 tokens; SKILL.md has 971 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~122
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:55
    https://humanrail.dev/mcp in ~/.openclaw/.env

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from automateyournetwork/netclaw at commit aa90e7d, republished under its Apache-2.0 licence (© automateyournetwork). 971 words, ~3,302 tokens.

Download SKILL.mdSave it as .claude/skills/humanrail-escalation/SKILL.md (or your agent's skills folder).
name
humanrail-escalation
description
Human-in-the-loop escalation via HumanRail — route low-confidence agent decisions, pre-destructive operation approvals, and ambiguous incident tickets to real human engineers. Human answers are verified and returned as structured output. Workers are paid via Lightning Network. Use when the agent is uncertain, when a destructive change needs explicit human sign-off beyond a ServiceNow CR, or when an ambiguous ticket requires human triage before automated handling.
license
Apache-2.0
user-invokable
true

HumanRail Escalation

Safety — Non-Negotiable Rules

NEVER route tasks that expose internal tooling or infrastructure details to workers. HumanRail workers are external humans. Task descriptions must be neutral: describe the decision needed without referencing agent names, internal hostnames, file paths, or credentials. Frame the task as a domain question a knowledgeable engineer would answer — not as "an AI agent needs help."

NEVER use HumanRail as a substitute for ServiceNow Change Management. A HumanRail approval is an engineering judgment call, not a formal ITSM change record. Pre-destructive approvals via HumanRail are a second safety layer — the ServiceNow CR must still exist and be in Implement state before any config push.

NEVER expose PII, device credentials, or configuration secrets in task payloads. Sanitize payloads: use device roles ("core router") not hostnames, use interface descriptions not IP addresses where possible.

ALWAYS wait for verified status before acting. A task with status submitted has been answered by a worker but not yet verified. Only act on the output when status == "verified".

When to Use

SituationUse HumanRailUse ServiceNow CRUse Slack Escalation
Agent confidence low — multiple valid interpretations✓——
Pre-destructive op beyond normal ITSM gating✓Required (both)—
Ambiguous incident ticket needs triage✓—Notify after
P1/P2 in-flight, need immediate human judgment✓—✓ (both)
Standard config change with approved CR—✓—
Routine maintenance window—✓—
Auto-quarantine endpoint (ISE)——✓ (ise-incident-response)

MCP Server

PropertyValue
Sourceprime001/humanrail-mcp-server
TransportStreamable HTTP (FastMCP, default port 8100)
LanguagePython 3.10+
Tools7 (create_task, get_task, wait_for_task, cancel_task, list_tasks, get_usage, health_check)
AuthHUMANRAIL_API_KEY (Bearer token — ek_live_... or ek_test_...)
Public endpointhttps://humanrail.dev/mcp (no local install required)

How to Run (Local)

bash
# Install dependencies
pip3 install "mcp[cli]>=1.0.0" httpx

# Start the MCP server (runs on http://127.0.0.1:8100/mcp)
HUMANRAIL_API_KEY=ek_live_your_key python3 $HUMANRAIL_MCP_SCRIPT

# Or use the public endpoint directly (no local install needed):
# Set HUMANRAIL_MCP_URL=https://humanrail.dev/mcp in ~/.openclaw/.env

Get your free API key at humanrail.dev.

Available Tools

ToolParametersWhat It Does
create_tasktask_type, payload, output_schema, risk_tier?, sla_seconds?, payout_currency?, payout_max_amount?, callback_url?, metadata?Post a task to the human worker pool and return a task ID
get_tasktask_idGet current status and output of a task
wait_for_tasktask_id, poll_interval_seconds?, timeout_seconds?Block until task reaches terminal state (verified, failed, cancelled, expired)
cancel_tasktask_idCancel a non-terminal task
list_tasksstatus?, task_type?, limit?, created_after?, created_before?List tasks with optional filters
get_usage—Org-level usage stats and billing summary
health_check—Verify the HumanRail API is reachable

Workflow 1: Low-Confidence Decision Escalation

When the agent reaches a decision point where two or more interpretations are equally valid and the wrong choice has significant consequences:

Phase 1: Assess Uncertainty

Identify what the agent cannot determine autonomously:

  • Does the context admit multiple valid network design choices?
  • Would an experienced engineer need site-specific knowledge the agent doesn't have?
  • Is the downstream impact large enough that getting it wrong matters?

If confidence is high enough to proceed, do so — don't over-escalate. HumanRail has an SLA cost.

Phase 2: Frame the Decision Neutrally

Write the task payload without referencing agent internals. The worker is an engineer; give them the facts they need.

task_type: "technical_decision"
payload:
  title:       "BGP policy clarification needed"
  description: |
    A network change is in progress on a dual-homed edge router.
    The router has two upstream transit providers. Policy options:
      A) Prefer Provider-A for all prefixes (lower latency, higher cost)
      B) Prefer Provider-B for all prefixes (higher latency, lower cost)
      C) Split: prefer Provider-A for /24s and shorter, Provider-B for longer prefixes
    Current traffic: ~8 Gbps mixed. No documented preference in the change ticket.
    What is the recommended policy and why?
  context:     "Standard dual-homed BGP edge, no MPLS, IXP peering not in scope."
output_schema:
  type: object
  required: [recommendation, rationale]
  properties:
    recommendation: {type: string, enum: [A, B, C]}
    rationale:      {type: string, minLength: 20}
risk_tier:     "medium"
sla_seconds:   900
payout_currency: "USD"
payout_max_amount: 1.00
Phase 3: Create and Wait
task = create_task(...)
result = wait_for_task(task_id=task["id"], timeout_seconds=900)
Phase 4: Act on Verified Output

Only proceed when result["status"] == "verified". The result["output"] field contains the worker's structured response matching your output_schema.

*** HUMAN DECISION IN FLIGHT ***

While wait_for_task polls, do not proceed with the ambiguous action. Post a Slack status update:

"⏳ Waiting for human engineer input on [topic]. Task ID: [id]. ETA: [sla_seconds]s."

If the task expires or fails, fall back to the conservative option and create a ServiceNow incident for manual follow-up.

Workflow 2: Pre-Destructive Operation Approval

For operations the agent is authorized to perform but that are irreversible or high-blast-radius — beyond the standard ServiceNow CR gate:

Examples: device reload, write erase, BGP peer teardown affecting production traffic, ISE policy purge.

Show full SKILL.md (390 more words)Show less
Phase 1: Baseline and Summarize

Before asking for approval, gather the current state so the human reviewer has complete context.

bash
# Capture pre-change state first
python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" get_interface_brief '{"device":"core-rtr-01"}'
python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" get_bgp_summary '{"device":"core-rtr-01"}'
Phase 2: Build Approval Request
task_type: "change_approval"
payload:
  title:       "Production reload approval — core edge router"
  description: |
    A device reload is requested to apply a software upgrade from IOS-XE 17.9 to 17.12.
    Pre-change state:
      - BGP peers: 4 active (2 transit, 2 iBGP) — all ESTABLISHED
      - Active sessions: 847 flows
      - Estimated downtime: 3-5 minutes
      - Rollback: IOS-XE 17.9 image retained in flash
    ServiceNow CR: [CR number] — in Implement state.
    Should this reload proceed NOW or be deferred to the next maintenance window?
  urgency:     "high"
output_schema:
  type: object
  required: [decision, notes]
  properties:
    decision: {type: string, enum: [proceed, defer]}
    notes:    {type: string}
risk_tier:     "high"
sla_seconds:   300
payout_currency: "USD"
payout_max_amount: 2.00
Phase 3: Gate on Response
result = wait_for_task(task_id=task["id"], timeout_seconds=300)
if result["output"]["decision"] == "proceed":
    # Execute the destructive operation
else:
    # Defer — log in GAIT, update ServiceNow CR
*** HUMAN APPROVAL GATE ***

STOP. The destructive operation MUST NOT execute until result["status"] == "verified" AND result["output"]["decision"] == "proceed".

If wait_for_task times out: default to DEFER. Update the ServiceNow CR with status "awaiting human authorization" and notify the Slack incident channel.

Workflow 3: Ambiguous Incident Ticket Triage

When an inbound ServiceNow incident has insufficient information to determine severity, affected CIs, or the correct response team:

Phase 1: Extract What's Known

Pull the raw incident from ServiceNow and identify the gaps:

  • Missing: affected device? Affected service? Symptom details?
  • Conflicting: multiple possible root causes with different response paths?
  • Stale: ticket opened hours ago with no updates — still valid?
Phase 2: Route to Human Triage
task_type: "incident_triage"
payload:
  title:       "Triage: ambiguous network incident ticket"
  description: |
    An incident ticket has insufficient detail for automated handling.
    Ticket summary: [short_description from ServiceNow]
    Known facts:
      - Reported by: [caller_id role/team, no PII]
      - Opened: [timestamp]
      - Impact field: [value]
      - Description: [full description, sanitized]
    Missing or conflicting:
      - [list gaps]
    Please determine: severity (P1/P2/P3/P4), affected CI(s) if identifiable,
    and recommended response team (NOC/NetEng/Security/Application).
output_schema:
  type: object
  required: [severity, team, notes]
  properties:
    severity: {type: string, enum: [P1, P2, P3, P4]}
    team:     {type: string, enum: [NOC, NetEng, Security, Application, Unknown]}
    notes:    {type: string, minLength: 10}
risk_tier:     "medium"
sla_seconds:   600
payout_currency: "USD"
payout_max_amount: 0.50
Phase 3: Apply Triage Output

Use the verified output to:

  1. Update the ServiceNow incident severity and assignment group
  2. Post a Slack alert at the appropriate level (#netclaw-alerts for P1/P2)
  3. Trigger the relevant response workflow

Escalation Summary Report Template

Use this when closing a HumanRail-escalated session:

HUMANRAIL ESCALATION SUMMARY
==============================
Task ID:       [task_id]
Task Type:     [task_type]
Opened:        [ISO 8601 timestamp]
Resolved:      [ISO 8601 timestamp]
Worker SLA:    [sla_seconds]s / actual: [elapsed]s

Question Summary:
  [One-paragraph neutral description of what was asked]

Human Decision:
  [Paste result["output"] here]

Action Taken:
  [What the agent did based on the decision]

Outcome:
  [ ] Operation completed successfully
  [ ] Operation deferred — scheduled for [date/window]
  [ ] Escalated to [team] — ServiceNow [INC/CHG number]

Checklist

Use before closing any HumanRail-escalated action:

  • Task created with neutral, non-internal-leaking payload
  • status == "verified" confirmed before acting (not just "submitted")
  • Human decision applied exactly — no agent interpretation of ambiguous output
  • Slack status posted during wait period
  • ServiceNow CR updated with escalation record (if change-related)
  • GAIT audit trail records the task ID, human decision, and action taken
  • If task expired/failed: conservative fallback applied and INC created

Integration with Other Skills

SkillIntegration
servicenow-change-workflowHumanRail approval is a second-layer gate — ServiceNow CR must still be in Implement state; HumanRail output is added as a work note
ise-incident-responseUse HumanRail for low-confidence risk assessments before the *** HUMAN DECISION POINT *** gate when the on-call engineer is not immediately available
slack-incident-workflowPost a Slack update when a HumanRail task is created; post result when resolved
gait-session-trackingAlways record HumanRail task IDs, decisions, and resulting actions in the GAIT audit trail
pyats-config-mgmtUse pre-destructive approval workflow before executing high-risk config pushes

Environment Variables

VariableRequiredExampleDescription
HUMANRAIL_API_KEYYesek_live_abc123...API key from humanrail.dev
HUMANRAIL_MCP_URLYeshttp://127.0.0.1:8100/mcpMCP endpoint (local server or https://humanrail.dev/mcp)
HUMANRAIL_MCP_SCRIPTNo/path/to/server.pyPath to local server.py (set by install.sh; not needed if using public endpoint)
HUMANRAIL_BASE_URLNohttps://api.humanrail.dev/v1REST API base URL (default: https://api.humanrail.dev/v1)

© automateyournetwork, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in workspace/skills/humanrail-escalation of automateyournetwork/netclaw.

Open the folder on GitHubat commit aa90e7d

Compare with similar skills

Humanrail Escalation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Humanrail Escalation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Humanrail Escalation this skillautomateyournetwork/netclaw676—~3.3kAutomated safety check: NotesApache-2.0
Workflow AutomationJoelLewis/finance_skills206—~7.3kAutomated safety check: PassMIT
Tools UIaiskillstore/marketplace4331 repos~990Automated safety check: PassNone
n8n AI Agent Designczlonkowski/n8n-skills6.4k—~6.8kAutomated safety check: PassMIT
Langchain Middlewarelangchain-ai/langchain-skills1.3k—~2.7kAutomated safety check: PassMIT
Project Developmentguanyang/open-agent-hub9772 repos~4.7kAutomated safety check: PassMIT

Similar skills

  • Workflow Automation

    JoelLewis/finance_skills

    Design human-in-the-loop workflow orchestration for securities operations: task routing, approval chains, and SLA monitoring.

    206 GitHub stars~7.3k tokensUpdated 2 mo ago
    Agent WorkflowsAuto-check passed

  • Tools UI

    aiskillstore/marketplace

    Tool lifecycle UI components for React/Next.js from ui.inference.sh.

    433 GitHub starsUsed in 1 repo~990 tokens
    AI & LLM EngineeringAuto-check passed

  • n8n AI Agent Design

    czlonkowski/n8n-skills

    Guide to designing n8n AI agents: choosing between Agent, chain, classifier and extractor nodes, wiring model, memory, tools and parser, plus RAG and human review.

    6.4k GitHub stars~6.8k tokensUpdated yesterday
    AI & LLM EngineeringAuto-check passed

  • Langchain Middleware

    langchain-ai/langchain-skills

    Official

    INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output.

    1.3k GitHub stars~2.7k tokensUpdated 2 days ago
    AI & LLM EngineeringAuto-check passed

  • Project Development

    guanyang/open-agent-hub

    This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token…

    977 GitHub starsUsed in 2 repos~4.7k tokens
    Agent WorkflowsAuto-check passed

  • Documentation Server

    andrea9293/mcp-documentation-server

    A skill your agent uses when you need to store, retrieve, search, or manage documents in a local knowledge base with semantic search and hybrid (vector + full-text) retrieval.

    343 GitHub stars~2.3k tokensUpdated 1 mo ago
    Agent WorkflowsAuto-check passed

More from automateyournetwork/netclaw

All 120 skills in this repo

  • EVE-NG Lab Topology Design

    automateyournetwork/netclaw

    Entry point for designing EVE-NG network labs: classifies the request, gathers missing requirements, proposes options and validates the resulting topology.

    676 GitHub stars~612 tokensUpdated yesterday
    Auto-check passed

  • ACI Policy Change Deployment

    automateyournetwork/netclaw

    Deploys Cisco ACI policy changes only behind an approved ServiceNow Change Request, capturing pre and post-change fault baselines and rolling back automatically on a fault delta.

    676 GitHub stars~4.2k tokensUpdated yesterday
    Auto-check passed

  • Cisco ACI Fabric Health Audit

    automateyournetwork/netclaw

    Runs a phased health audit of a Cisco ACI fabric through MCP tools: node status, links, tenant and policy review, faults and endpoint learning.

    676 GitHub stars~2.9k tokensUpdated yesterday
    Auto-check passed

  • Anta Validation

    automateyournetwork/netclaw

    Validate Arista EOS network state against ANTA's pre-built 208-test catalogue, with structured pass/fail verdicts.

    676 GitHub stars~1.2k tokensUpdated yesterday
    Auto-check passed

  • Arista Cvp

    automateyournetwork/netclaw

    Arista CloudVision Portal (CVP) automation via REST API — device inventory, events, connectivity monitoring, tag management (4 tools).

    676 GitHub stars~2.2k tokensUpdated yesterday
    Auto-check: notes

  • AWS Cloud Monitoring

    automateyournetwork/netclaw

    AWS CloudWatch monitoring — metrics, alarms, log queries, VPC flow log analysis, network performance.

    676 GitHub stars~1k tokensUpdated yesterday
    Auto-check passed

Works with

Categories

Questions about Humanrail Escalation

What does Humanrail Escalation do?

Human-in-the-loop escalation via HumanRail — route low-confidence agent decisions, pre-destructive operation approvals, and ambiguous incident tickets to real human engineers. Humanrail Escalation is an agent skill from automateyournetwork/netclaw. Human-in-the-loop escalation via HumanRail — route low-confidence agent decisions, pre-destructive operation approvals, and ambiguous incident tickets to real human engineers.

When should I use Humanrail Escalation?

Humanrail Escalation fits situations like: the agent is uncertain; A destructive change needs explicit human sign-off beyond a ServiceNow CR; an ambiguous ticket requires human triage before automated handling.

How do I install Humanrail Escalation in Claude Code?

Run `npx skills add automateyournetwork/netclaw --skill humanrail-escalation -a claude-code`. Or copy the skill folder (workspace/skills/humanrail-escalation in automateyournetwork/netclaw) into .claude/skills/humanrail-escalation in your project. Claude Code loads it when a task matches its description.

How do I install Humanrail Escalation in Codex?

Run `npx skills add automateyournetwork/netclaw --skill humanrail-escalation -a codex`. Or copy the skill folder (workspace/skills/humanrail-escalation in automateyournetwork/netclaw) into .agents/skills/humanrail-escalation in your project. Codex loads it when a task matches its description.

Can I use Humanrail Escalation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add automateyournetwork/netclaw --skill humanrail-escalation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/humanrail-escalation, .gemini/skills/humanrail-escalation, .github/skills/humanrail-escalation and .opencode/skills/humanrail-escalation in your project.

What does Humanrail Escalation need to run?

Going by SKILL.md and its folder, Humanrail Escalation needs the command-line tools its instructions call (python3 and pip3) and credentials named HUMANRAIL_API_KEY. Our summary lists: Python 3; A credential in HUMANRAIL_API_KEY.

Does Humanrail Escalation access the network?

SKILL.md names 3 domains. In commands or code: humanrail.dev and api.humanrail.dev; the agent is likely to contact these when it follows the instructions. As links in the text: github.com. This is read from the text; nothing was executed.

Is Humanrail Escalation safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Humanrail Escalation use?

Humanrail Escalation is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Humanrail Escalation use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Humanrail Escalation?

Skills that share tags, products or a category with Humanrail Escalation: Workflow Automation (JoelLewis/finance_skills, 206 stars), Tools UI (aiskillstore/marketplace, 433 stars), n8n AI Agent Design (czlonkowski/n8n-skills, 6.4k stars) and Langchain Middleware (langchain-ai/langchain-skills, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Humanrail Escalation?

automateyournetwork (a GitHub user) maintains it in automateyournetwork/netclaw, which has 676 GitHub stars. The repository holds 120 skills in this directory. The repository was last updated on October 9, 2026.

Source: automateyournetwork/netclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.