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 skills/hoavdc/codexkit/codexkit-fpa-rolling-forecastnpx skills add hoavdc/CodexKit --skill codexkit-fpa-rolling-forecastgit clone --depth 1 https://github.com/hoavdc/CodexKitWrote 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/skills/hoavdc/codexkit/codexkit-fpa-rolling-forecast)<a href="https://agentmods.dev/skills/hoavdc/codexkit/codexkit-fpa-rolling-forecast"><img src="https://agentmods.dev/badge/skills/hoavdc/codexkit/codexkit-fpa-rolling-forecast.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 | $0.00068 | $0.01313 |
| Opus 5 | $0.00034 | $0.00656 |
| Sonnet 5 | $0.00014 | $0.00263 |
| Haiku 4.5 | $0.00007 | $0.00131 |
Grade A, and why
codexkit-fpa-rolling-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 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rolling Forecast Builder
When to Use
- Monthly or quarterly when updating financial projections
- When replacing static annual budgets with continuous planning
- When management needs a forward-looking 12–18 month view at all times
- When actuals deviate significantly and re-forecasting is required
Procedure
Step 1 — Lock Actuals
- Import actuals for completed periods from the ERP or accounting system
- Freeze these periods — no further edits allowed
- Calculate YTD variance vs prior forecast for each line item
Step 2 — Reforecast Remaining Periods
- For each P&L line, update the driver assumptions:
- Revenue: units × price, or pipeline × conversion rate
- COGS: volume × unit cost, or % of revenue
- OpEx: headcount × avg cost, or run-rate with known changes
- Apply known one-time items (restructuring, capex, etc.)
- Document every assumption change vs prior forecast
Step 3 — Extend Horizon
- Add new periods to maintain the 12–18 month rolling window
- Use trailing actuals + seasonality patterns to seed new periods
- Flag any new periods with lower confidence level
Step 4 — Sensitize Scenarios
- Apply Base / Bull / Bear assumptions to key drivers
- Show range around the forecast (not a single number)
Step 5 — Generate Waterfall Bridge
- Start with Prior Forecast for the full period
- Add variance buckets: Volume | Price/Mix | Timing | Cost | FX | One-offs
- Arrive at New Forecast
- Summarize the top 5 variance drivers in narrative form
Inputs
| Input | Required | Format |
|---|---|---|
| Actuals YTD | Yes | P&L by month |
| Prior forecast | Yes | P&L by month for forecast period |
| Driver assumptions updates | Yes | Line-by-line changes with rationale |
| Seasonality pattern | Recommended | Historical % distribution by month |
Output
## Rolling Forecast — [Period] Update
### P&L Summary (in $000s)
| Line | YTD Actual | Remaining Forecast | Full Year | vs Prior | Δ% |
|------|-----------|-------------------|-----------|---------|-----|
| Revenue | 12,400 | 19,200 | 31,600 | +1,600 | +5.3% |
| COGS | (5,200) | (8,100) | (13,300) | (400) | +3.1% |
| Gross Profit | 7,200 | 11,100 | 18,300 | +1,200 | +7.0% |
| OpEx | (4,800) | (7,600) | (12,400) | (200) | +1.6% |
| EBITDA | 2,400 | 3,500 | 5,900 | +1,000 | +20.4% |
### Waterfall Bridge (Revenue)
Prior Forecast: $30,000
+ Volume: +$800 (higher unit sales in Q3)
+ Price/Mix: +$500 (premium tier adoption)
+ Timing: +$300 (deal pulled forward)
= New Forecast: $31,600
### Assumption Log
| Driver | Prior | New | Rationale |
|--------|-------|-----|-----------|
| Q3 unit sales | 1,200 | 1,350 | Pipeline confirmed |
| Premium mix | 15% | 18% | Q2 trend extrapolated |
| Headcount | 45 | 47 | 2 new hires approved |
### Confidence Level: HIGH (months 1–6) / MEDIUM (months 7–12) / LOW (months 13–18)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 140 lines · 68 tokens per session scan A ba97fb349042
codexkit-fpa-rolling-forecast is a skill published in the GitHub repository hoavdc/CodexKit (21 stars, last pushed 3mo ago), licensed MIT. It adds 68 tokens to every session and 1,313 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.
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