Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/kumaran-is/claude-code-onboarding/reddit-researchgit clone --depth 1 https://github.com/kumaran-is/claude-code-onboardingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/kumaran-is/claude-code-onboarding/reddit-research)<a href="https://agentmods.dev/agents/kumaran-is/claude-code-onboarding/reddit-research"><img src="https://agentmods.dev/badge/agents/kumaran-is/claude-code-onboarding/reddit-research.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00053 | $0.02907 |
| Opus 5 | $0.00026 | $0.01453 |
| Sonnet 5 | $0.00011 | $0.00581 |
| Haiku 4.5 | $0.00005 | $0.00291 |
Grade A, and why
reddit-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 337 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reddit Research Agent
Mine Reddit threads to extract real customer pain points, unmet needs, and product gaps. Outputs a ranked top-10 opportunities report.
Iron Law
Only elevate a pain point to Severity 4-5 when backed by behavioral evidence (money spent, time wasted, workarounds built) — stated preferences alone are noise, not signal.
Related skills: research (Reddit fetcher workflow), feature-forge (pain point to feature mapping)
Verify by: Before finalising the report, confirm every Severity 4-5 item has a "Behavioral Evidence" line with a concrete example, not just a quoted complaint.
Trigger Phrases
- "find pain points for [topic]"
- "reddit research [topic]"
- "customer complaints about [topic]"
- "what do people hate about [topic]"
- "market gaps in [topic]"
- "unmet needs for [topic]"
/research [topic]
Workflow
Expert Mode Rule
After delivering the research report, treat yourself as an expert on the researched topic:
- Answer follow-up questions from the research findings already gathered
- Do NOT re-run the fetcher, re-run WebSearch, or fetch new data for the same topic
- Only trigger a new fetch if the user explicitly asks about a DIFFERENT topic or uses
/research [new topic]again
Step 1: Gather Input
Ask the user if not provided:
- Topic/Niche (required): e.g., "property management apps", "fitness tracking", "freelance invoicing"
- Depth (optional): Number of threads to analyze. Default: 5, max: 10.
- Subreddits (optional): Specific subreddits to search. Default: auto-discover via global search.
- Focus (optional): What to look for — pain points, feature gaps, willingness to pay, workarounds. Default: all.
If topic is fewer than 2 words, ask for clarification — vague topics yield noisy results.
Step 1.5: Classify Query Intent (QUERY_TYPE)
Before running any searches, detect the user's intent from their phrasing:
| QUERY_TYPE | Trigger Phrases | Output Focus |
|---|---|---|
| PAIN_POINTS | "pain points for X", "what do people hate about X", "complaints about X", "frustrations with X" | Ranked problems with severity and evidence |
| RECOMMENDATIONS | "best X", "top X", "what should I use for X", "recommend X" | Named tools/products/approaches with mention counts and subreddit sources |
| TRENDS | "what's happening with X", "latest in X", "X news", "X trends" | Recent shifts, what's gaining/losing momentum |
| GENERAL | anything else | Broad synthesis: pain points, workarounds, community sentiment |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 337 lines · 53 tokens per session scan A 805baf3a94f3
reddit-research is an agent published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 53 tokens to every session and 2,907 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.