alirezarezvani/init
engineering/agenthub/skills/init/SKILL.md
Create a new AgentHub collaboration session with task, agent count, and evaluation criteria.
$ install --global
skillsauthnpx skillsauth add alirezarezvani/claude-skills initInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 16, 2026, 4:39 PM15.0s1 file scanned
SKILL.md
- name:
- init
- description:
- Create a new AgentHub collaboration session with task, agent count, and evaluation criteria.
- command:
- /hub:init
/hub:init — Create New Session
Initialize an AgentHub collaboration session. Creates the .agenthub/ directory structure, generates a session ID, and configures evaluation criteria.
Usage
/hub:init # Interactive mode
/hub:init --task "Optimize API" --agents 3 --eval "pytest bench.py" --metric p50_ms --direction lower
/hub:init --task "Refactor auth" --agents 2 # No eval (LLM judge mode)
What It Does
If arguments provided
Pass them to the init script:
python {skill_path}/scripts/hub_init.py \
--task "{task}" --agents {N} \
[--eval "{eval_cmd}"] [--metric {metric}] [--direction {direction}] \
[--base-branch {branch}]
If no arguments (interactive mode)
Collect each parameter:
- Task — What should the agents do? (required)
- Agent count — How many parallel agents? (default: 3)
- Eval command — Command to measure results (optional — skip for LLM judge mode)
- Metric name — What metric to extract from eval output (required if eval command given)
- Direction — Is lower or higher better? (required if metric given)
- Base branch — Branch to fork from (default: current branch)
Output
AgentHub session initialized
Session ID: 20260317-143022
Task: Optimize API response time below 100ms
Agents: 3
Eval: pytest bench.py --json
Metric: p50_ms (lower is better)
Base branch: dev
State: init
Next step: Run /hub:spawn to launch 3 agents
For content or research tasks (no eval command → LLM judge mode):
AgentHub session initialized
Session ID: 20260317-151200
Task: Draft 3 competing taglines for product launch
Agents: 3
Eval: LLM judge (no eval command)
Base branch: dev
State: init
Next step: Run /hub:spawn to launch 3 agents
Baseline Capture
If --eval was provided, capture a baseline measurement after session creation:
- Run the eval command in the current working directory
- Extract the metric value from stdout
- Append
baseline: {value}to.agenthub/sessions/{session-id}/config.yaml - Display:
Baseline captured: {metric} = {value}
This baseline is used by result_ranker.py --baseline during evaluation to show deltas. If the eval command fails at this stage, warn the user but continue — baseline is optional.
After Init
Tell the user:
- Session created with ID
{session-id} - Baseline metric (if captured)
- Next step:
/hub:spawnto launch agents - Or
/hub:spawn {session-id}if multiple sessions exist
Related Skills
alirezarezvani/code-reviewer
tools
VerifiedTrustedCommunity
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
16,546SKILL.mdUpdated May 10, 2026
alirezarezvani/research-ops-skills
tools
VerifiedTrustedCommunity
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
16,359SKILL.mdUpdated May 28, 2026
alirezarezvani/research-finance
development
VerifiedTrustedCommunity
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
16,359SKILL.mdUpdated May 28, 2026
alirezarezvani/product-research
development
VerifiedTrustedCommunity
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.
16,359SKILL.mdUpdated May 28, 2026