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 commands/tommymorgan/claude-plugins/forecastgit clone --depth 1 https://github.com/tommymorgan/claude-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 | $0.00020 | $0.00507 |
| Opus 5 | $0.00010 | $0.00253 |
| Sonnet 5 | $0.00004 | $0.00101 |
| Haiku 4.5 | $0.00002 | $0.00051 |
Grade A, and why
forecast 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 3d 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.
What it actually says
Flow Forecast
Answer "when will it be done?" probabilistically.
Steps
-
Resolve project config and read flow data
-
Apply filters if provided
-
Check sample size: warn if fewer than 20 data points
-
Determine forecast type from arguments:
Single Item Forecast (default)
- Calculate percentiles at 50th, 70th, 85th, 95th from historical Cycle Time
- Display:
Single Item Forecast ({count} historical items) {filter_description if filtered} Based on historical data, the next work item has: - 50% chance of finishing within {n} days - 70% chance of finishing within {n} days - 85% chance of finishing within {n} days - 95% chance of finishing within {n} days Reforecast after significant process changes or every 2 weeks.
Multiple Item Forecast (--items N)
- Calculate weekly Throughput from historical data
- Warn if fewer than 10 throughput periods available
- Run Monte Carlo simulation (10,000 trials)
- Convert weeks to projected dates from today
- Display:
Multiple Item Forecast: {N} remaining items ({count} historical items, {weeks} weeks of throughput data) {filter_description if filtered} Probability of completing all {N} items: - 50% chance by {date} ({weeks} weeks) - 70% chance by {date} ({weeks} weeks) - 85% chance by {date} ({weeks} weeks) - 95% chance by {date} ({weeks} weeks) Reforecast when: items added/removed, throughput changes, or weekly as work progresses.
Arguments
--items N— Forecast for N remaining items (Monte Carlo)--author <name>— Use only this author's historical data--repo <owner/repo>— Use only this repo's historical data
Important
- Forecasts include a RANGE and PROBABILITY — never a single date
- Each forecast states when it should be regenerated
- Never divide remaining items by average throughput (Flaw of Averages)
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.
- 3d ago First seen · 69 lines · 20 tokens per session scan A c7389002182f
forecast is a command published in the GitHub repository tommymorgan/claude-plugins (4 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 507 once invoked, about $0.0001 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.
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