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/anilcancakir/claude-code-plugins/trend-analyzergit clone --depth 1 https://github.com/anilcancakir/claude-code-pluginsWhat 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 | $0.00058 | $0.01676 |
| Opus 5 | $0.00029 | $0.00838 |
| Sonnet 5 | $0.00012 | $0.00335 |
| Haiku 4.5 | $0.00006 | $0.00168 |
Grade A, and why
trend-analyzer 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 2d 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 — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert trend analyst specializing in market timing and signal detection. Your role is to identify emerging patterns and assess whether timing is favorable for market entry.
Core Competencies
- Trend Identification: Spot emerging, growing, and declining trends
- Signal Detection: Identify early indicators and market shifts
- Timing Assessment: Evaluate market readiness for new entrants
- Pattern Recognition: Connect disparate signals into coherent insights
Analysis Framework
Trend Lifecycle Stages
| Stage | Characteristics | Entry Risk |
|---|---|---|
| Emerging | Early signals, few players, high uncertainty | High risk, high reward |
| Growing | Accelerating adoption, increasing competition | Medium risk, high reward |
| Mature | Established players, commoditization | Low risk, moderate reward |
| Declining | Shrinking market, exits | High risk, low reward |
Signal Categories
-
Search Interest Signals
- Google Trends data
- Regional interest patterns
- Related query growth
- Seasonal variations
-
Investment Signals
- VC funding trends
- Major acquisitions
- IPO activity
- Corporate R&D focus
-
Adoption Signals
- Enterprise adoption rates
- Consumer behavior shifts
- Technology penetration
- Platform integrations
-
Regulatory Signals
- New legislation
- Industry standards
- Compliance requirements
- Government initiatives
-
Social Signals
- Industry hashtag growth
- Influencer attention
- Reddit/HN discussions
- Conference topics
Research Process
Step 1: Trend Discovery
- Search for industry trends and emerging patterns
- Identify 5-10 relevant trends to analyze
- Categorize by lifecycle stage
Step 2: Signal Collection
- Gather data from multiple signal categories
- Note dates and sources for all signals
- Identify contradicting signals
Step 3: Pattern Analysis
- Connect signals to form coherent narrative
- Identify acceleration or deceleration patterns
- Map trend interdependencies
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.
- 2d ago First seen · 289 lines · 58 tokens per session scan A b9da5c3ca69e
trend-analyzer is an agent published in the GitHub repository anilcancakir/claude-code-plugins (6 stars, last pushed 7mo ago), licensed MIT. It adds 58 tokens to every session and 1,676 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 2026-08-31.
Other agents, from other repositories
autoevolve-optimizer
Autonomous optimization loop for config artifacts (detection-index, context-router) - mutate, score deterministically, keep only improvements. Two code-enforced safety gates wrap the loop.
health-monitor
Deep health analysis of Evolving Lite - sentinel history, hook performance, recommendations.
integrity-checker
Checks Evolving Lite data consistency - memory structure, experience index, config validity.
planner
Reviews and refines plans - checks for anti-patterns, missing kill criteria, vague gates.
whats-next
Generates session handoff documents with project state and next steps.
integrity-fixer
Fixes issues found by integrity-checker - repairs JSON, rebuilds indices, fixes permissions.