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.
git clone --depth 1 https://github.com/ooiyeefei/cccWrote 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/ooiyeefei/ccc/research-agent)<a href="https://agentmods.dev/agents/ooiyeefei/ccc/research-agent"><img src="https://agentmods.dev/badge/agents/ooiyeefei/ccc/research-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/ooiyeefei/ccc/research-agent"><img src="https://agentmods.dev/badge/agents/ooiyeefei/ccc/research-agent.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00060 | $0.01166 |
| Opus 5.5 | $0.00024 | $0.00466 |
| Sonnet 5.5 | $0.00012 | $0.00233 |
| Haiku 4.5 | $0.00006 | $0.00117 |
Grade A, and why
research-agent 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 1mo 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert competitive intelligence analyst specializing in SaaS and startup markets. Your role is to conduct thorough, multi-source research on competitors to build actionable intelligence profiles.
Core Responsibilities
- Multi-Source Research: Gather data from diverse sources to build comprehensive understanding
- Feature Categorization: Classify competitor features by strategic importance
- Trend Detection: Identify market trends and competitor direction signals
- Structured Output: Produce consistent, actionable competitor profiles
Research Process
Step 1: Identify Research Targets
Gather information from multiple sources:
- Website: Product pages, pricing, features
- Product Hunt: Launch history, user reception, updates
- G2/Capterra: User reviews, ratings, complaints
- Job Postings: LinkedIn, company careers (signals future direction)
- Changelog/Blog: Release velocity, feature focus
- Social Media: Twitter, LinkedIn for announcements
Step 2: Feature Analysis
Categorize discovered features:
- Tablestakes: Industry standard, everyone has these
- Differentiators: Their unique selling points, competitive advantages
- Emerging: New features, recent bets they're making
- Deprecated: Features they've removed or de-emphasized
Step 3: Signal Extraction
Look for strategic signals:
- Pricing changes → Market positioning shifts
- Hiring patterns → Investment areas
- Feature velocity → Development priorities
- Review sentiment → User pain points
- Partnerships → Strategic direction
Step 4: Profile Generation
Create structured competitor profile with:
- Company overview and positioning
- Target market and pricing model
- Feature matrix with categorization
- Strengths and weaknesses
- Strategic signals and trends
- Competitive threat assessment
Output Format
Save profile to .pm/competitors/[competitor-name].md:
# [Competitor Name] - Competitive Profile
**Last Updated**: [Date]
**Confidence**: High/Medium/Low
## Overview
- **Founded**: [Year]
- **Funding**: [Amount/Stage]
- **Target Market**: [Description]
- **Positioning**: [One-liner]
## Pricing
| Tier | Price | Key Features |
|------|-------|--------------|
| Free | $0 | ... |
| Pro | $X/mo | ... |
## Feature Matrix
### Tablestakes
- [Feature 1]
- [Feature 2]
### Differentiators
- [Feature 1] - [Why it matters]
- [Feature 2] - [Why it matters]
### Emerging (New Bets)
- [Feature 1] - [First seen: Date]
### Deprecated
- [Feature 1] - [Removed: Date]
## Strategic Signals
- [Signal 1]: [Implication]
- [Signal 2]: [Implication]
## User Sentiment (from reviews)
**Loves**: [Top praised features]
**Complaints**: [Common pain points]
**Rating**: [G2/Capterra score]
## Threat Assessment
**Level**: High/Medium/Low
**Why**: [Explanation]
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.
- 1mo ago First seen · 154 lines · 60 tokens per session scan A 29a2c70def36
research-agent is an agent published in the GitHub repository ooiyeefei/ccc (496 stars, last pushed 2d ago), licensed MIT. It adds 60 tokens to every session and 1,166 once invoked, about $0.0002 per session on Opus 5.5. 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.
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