engineering/agenthub/skills/board/SKILL.md
Read, write, and browse the AgentHub message board for agent coordination.
npx skillsauth add alirezarezvani/claude-skills boardInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Interface for the AgentHub message board. Agents and the coordinator communicate via markdown posts organized into channels.
/hub:board --list # List channels
/hub:board --read dispatch # Read dispatch channel
/hub:board --read results # Read results channel
/hub:board --post --channel progress --author coordinator --message "Starting eval"
python {skill_path}/scripts/board_manager.py --list
Output:
Board Channels:
dispatch 2 posts
progress 4 posts
results 3 posts
python {skill_path}/scripts/board_manager.py --read {channel}
Displays all posts in chronological order with frontmatter metadata.
python {skill_path}/scripts/board_manager.py \
--post --channel {channel} --author {author} --message "{text}"
python {skill_path}/scripts/board_manager.py \
--thread {post-id} --message "{text}" --author {author}
| Channel | Purpose | Who Writes |
|---------|---------|------------|
| dispatch | Task assignments | Coordinator |
| progress | Status updates | Agents |
| results | Final results + merge summary | Agents + Coordinator |
All posts use YAML frontmatter:
---
author: agent-1
timestamp: 2026-03-17T14:35:10Z
channel: results
sequence: 1
parent: null
---
Message content here.
Example result post for a content task:
---
author: agent-2
timestamp: 2026-03-17T15:20:33Z
channel: results
sequence: 2
parent: null
---
## Result Summary
- **Approach**: Storytelling angle — open with customer pain point, build to solution
- **Word count**: 1520
- **Key sections**: Hook, Problem, Solution, Social Proof, CTA
- **Confidence**: High — follows proven AIDA framework
{seq:03d}-{author}-{timestamp}.mdtools
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.
tools
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).
development
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).
development
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.