channel-research-deployment
Deploy interviews, surveys, and diary studies via messaging channels (Telegram, Slack, WhatsApp, Google Chat) with adaptive questioning and real-time analytics.
Skill by henrique-simoes
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/henrique-simoes-channel-research-deployment
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: devops, data, research, productivity
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/channel-research-deployment
What's inside
- Skill name
channel-research-deployment- Description
- Deploy interviews, surveys, and diary studies via messaging channels (Telegram, Slack, WhatsApp, Google Chat) with adaptive questioning and real-time analytics.
- License
- MIT
- Instructions
- 53 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Channel Research Deployment ## Capabilities - Deploy research studies across Telegram, Slack, WhatsApp, and Google Chat - Adaptive questioning with LLM-powered follow-up probes - Real-time analytics dashboard (response rate, completion rate, per-question stats) - Automatic nugget extraction from participant responses into the Atomic Research chain - Conversation state machine: intro, questions, probing, wrap-up - Configurable rate limiting between questions - LLM-judged saturation detection to avoid over-probing - Cross-deployment overview with progress tracking ## Workflow 1. **Plan** -- Define research goals, generate question set with adaptive rules 2. **Configure** -- Set up channel instances and link to a project 3. **Deploy** -- Create and activate a deployment across selected channels 4. **Collect** -- Participants respond via messaging; adaptive probing deepens insights 5. **Monitor** -- Real-time analytics show progress, response rates, and per-question stats 6. **Analyze** -- Complete the deployment; synthesize findings into nuggets, insights, recommendations ## Input - Research goals and questions (text or generated via plan) - Channel instance IDs (configured Telegram/Slack/WhatsApp/Google Chat connections) - Deployment configuration (adaptive rules, rate limits, intro/outro messages) - Project context (auto-loaded from context layers) ## Output - Deployment plan with question set and adaptive rules (JSON) - Real-time analytics (response rate, completion rate, per-question breakdown) - Nuggets with source attribution (participant, channel, deployment) - Insights and recommendations synthesized from responses - Full conversation transcripts per participant ## Deployment Types - **Interview** -- Semi-structured conversational interview via messaging - **Survey** -- Structured question set with optional branching - **Diary Study** -- Longitudinal prompts sent on a schedule over days/weeks ## Best Practices - 5-15 questions per interview deployment; 3-5 for diary studies - Enable adaptive probing for interviews to capture deeper insights - Set rate limits (30-60s between questions) to avoid overwhelming participants - Use the intro message to set expectations and build trust - Monitor per-question skip rates to identify confusing questions - Complete deployments promptly to avoid participant fatigue
From henrique-simoes/Istara, MIT.
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- 0.637score
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Signals
- Stars
- 16
- Forks
- 5
- Open issues
- 15
- Last commit (this folder)
- Mar 30, 2026 (7 months ago)
- Last release
- Sep 27, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/henrique-simoes/Istara
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from henrique-simoes
- 0.637score
user-interviews
henrique-simoes
Plan, conduct, and analyze user interviews.
SkillAFreshness: A★ 16Claude Code +9 - 0.634score
empathy-mapping
henrique-simoes
Create Says/Thinks/Does/Feels quadrants from interview data, identify contradictions.
SkillAFreshness: A★ 16Claude Code +9 - 0.634score
hmw-statements
henrique-simoes
Generate 'How Might We' statements from insights, score by impact/feasibility.
SkillAFreshness: A★ 16Claude Code +9 - 0.634score
journey-mapping
henrique-simoes
Build end-to-end journey maps with stages, actions, emotions, pain points, opportunities — all linked to evidence.
SkillAFreshness: A★ 16Claude Code +9 - 0.634score
jtbd-analysis
henrique-simoes
Extract JTBD statements from research, identify functional/emotional/social jobs.
SkillAFreshness: A★ 16Claude Code +9 - 0.634score
affinity-mapping
henrique-simoes
Auto-cluster research nuggets and notes into themes.
SkillAFreshness: A★ 16Claude Code +9
Cite this page
channel-research-deployment skill. Agent Mart. https://agentmart.dev/skills/henrique-simoes-channel-research-deployment (last updated 2026-10-24)