trend-forecaster

trend-forecaster is an agent for Claude Code from hoangsonww/Claude-Code-Agent-Monitor. It costs 82 tokens per session (965 once invoked), scanned A, original, MIT.

A forecasting agent that uses up to a year of Agent Monitor activity and cost data to estimate near-future Claude Code usage. It also looks for points where the trend changes or speeds up.

In plain words
What is it for?
Use it to project usage for the next 7, 14, or 30 days and flag possible trend inflection points.
Why use it?
It helps you anticipate rising or falling activity and costs instead of reacting only after the change happens.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter; mentions Claude Code.

Part of the ccam-insights plugin — 6 skills, 3 commands, 2 agents shipped together

Good fit Use it to project usage for the next 7, 14, or 30 days and flag possible trend inflection points.

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Install with agentmods
npx agentmods add agents/hoangsonww/claude-code-agent-monitor/trend-forecaster
Install

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.

Clone the repo
git clone --depth 1 https://github.com/hoangsonww/Claude-Code-Agent-Monitor

Made for: Claude Code.

Or install ccam-insights, the plugin that ships this one along with the rest of its 6 skills, 3 commands, 2 agents.

Wrote 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.

agentmods badge for trend-forecaster

README.md
[![agentmods](https://agentmods.dev/badge/agents/hoangsonww/claude-code-agent-monitor/trend-forecaster/github.svg)](https://agentmods.dev/agents/hoangsonww/claude-code-agent-monitor/trend-forecaster)
Your own site
<a href="https://agentmods.dev/agents/hoangsonww/claude-code-agent-monitor/trend-forecaster"><img src="https://agentmods.dev/badge/agents/hoangsonww/claude-code-agent-monitor/trend-forecaster/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.

agentmods 80×15 button for trend-forecaster

Your own site · 80×15
<a href="https://agentmods.dev/agents/hoangsonww/claude-code-agent-monitor/trend-forecaster"><img src="https://agentmods.dev/badge/agents/hoangsonww/claude-code-agent-monitor/trend-forecaster.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 82 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 965 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00082 $0.00965
Opus 5 $0.00041 $0.00483
Sonnet 5 $0.00016 $0.00193
Haiku 4.5 $0.00008 $0.00097

Measured 8d ago against content hash 066d94aa8fdd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

trend-forecaster scanned grade A with 1 finding 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 8d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

`http://localhost:4820` using `curl -s http://localhost:4820/api/...` to project
plugins/ccam-insights/agents/trend-forecaster.md · 70 lines

How it starts

The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Trend Forecaster

You are a usage and cost forecaster. You query the Agent Monitor dashboard API at http://localhost:4820 using curl -s http://localhost:4820/api/... to project near-future activity from historical daily trends and to flag inflection points.

Available Data Sources

Endpoint Returns
GET /api/analytics daily_sessions (365d), daily_events (365d), tokens (total_input, total_output, total_cache_read, total_cache_write — baselines pre-summed), event_types, tool_usage, avg_events_per_session
GET /api/pricing/cost { total_cost, breakdown:[{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] } — anchors cost-per-event/session
GET /api/sessions?limit=N Recent sessions with cost, started_at, ended_at, model, metadata — used to validate the daily series against per-session cost
GET /api/stats total_sessions, events_today — current-day sanity check against the series

Analysis Framework

  1. Pull the seriesGET /api/analytics; read daily_sessions and daily_events (each a 365-day { date, count } array). Sort by date and fill missing days with zero so the windows are evenly spaced.
  2. Smooth — compute a trailing simple moving average (SMA) at windows 7 and 30 for both series. The 7-day SMA is the short-term signal; the 30-day SMA is the baseline.
  3. Slope — fit a least-squares line over the last 30 days: slope = Σ((i-ī)(y-ȳ)) / Σ((i-ī)²) in units per day. Report slope for sessions/day and events/day.
  4. Project — extrapolate the last SMA value forward by the slope for horizons of 7, 14, and 30 days: projected(t) = last_SMA + slope × t. Floor projections at zero.
  5. Cost-anchor — from GET /api/pricing/cost, derive cost-per-event = total_cost / total_events (use /api/analytics total_events) and cost-per-session = total_cost / total_sessions. Multiply the projected event/session counts to get projected USD spend per horizon.
  6. Inflection points — flag dates where the 7-day SMA crosses the 30-day SMA (regime change), or where the rolling slope flips sign, or where week-over-week change exceeds ±50% (acceleration/collapse). Report the date and magnitude.

Read the full file on GitHub · 70 lines

Changes

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.

  1. 8d ago First seen · 70 lines · 82 tokens per session scan A 066d94aa8fdd

Subscribe to this mod's changes

trend-forecaster is an agent published in the GitHub repository hoangsonww/Claude-Code-Agent-Monitor (989 stars, last pushed 2d ago), licensed MIT. It adds 82 tokens to every session and 965 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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