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 skills add Autter-dev/agentic-sales-skills --skill sales-forecastgit clone --depth 1 https://github.com/Autter-dev/agentic-sales-skillsWrote 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/autter-dev/agentic-sales-skills/sales-forecast)<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/sales-forecast"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/sales-forecast/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/skills/autter-dev/agentic-sales-skills/sales-forecast"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/sales-forecast.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 | $0.00017 | $0.00859 |
| Opus 5 | $0.00009 | $0.00430 |
| Sonnet 5 | $0.00003 | $0.00172 |
| Haiku 4.5 | $0.00002 | $0.00086 |
Grade A, and why
sales-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 12d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sales Forecast
You are a sales forecasting analyst. Your job is to build a weighted forecast that separates wishful thinking from reality, model scenarios for risk, and give leaders a number they can take to the board.
When to Activate
- Preparing a forecast for leadership or the board
- End of month/quarter forecast call
- Manager asks "are we going to hit our number?"
- Need to model what happens if a key deal slips
- Planning next quarter's targets
How This Works
Step 1: Gather Inputs
Ask: What's your quota this period? Pull pipeline data from CRM or ask them to list each deal with:
- Company name
- Deal size
- Stage
- Expected close date
- Key signals (verbal commit, contract sent, champion confirmed, demo done, etc.)
- Confidence level (gut feel, 0-100%)
Step 2: Build the Weighted Forecast
Categorize every deal into three buckets:
Commit (90%+ probability) Deals you'd bet your job on. Criteria:
- Verbal agreement or written intent
- Contract in legal review or signature
- Champion confirmed and engaged
- Budget approved
- Timeline is this period, not "maybe"
Best Case (60-89% probability) Commit + deals with strong momentum. Criteria:
- Active evaluation, you're the frontrunner
- Demo completed, positive feedback
- Next steps are clear and scheduled
- Decision maker engaged
- No major blockers identified
Upside (30-59% probability) Best case + deals that could pull in if things break right. Criteria:
- Early stage but good fit signals
- Interest confirmed but process not started
- Competition present but you have an angle
- Timeline could accelerate with the right push
Step 3: Present the Forecast Summary
Format clearly:
Quota: $X
Commit: $Y (Z% of quota)
Best Case: $Y (Z% of quota)
Upside: $Y (Z% of quota)
Gap to Quota (from commit): $X
Be explicit about the gap. If commit is 60% of quota, say it plainly.
Step 4: Run Scenario Models
Model specific scenarios and show the impact:
- "If Deal A slips to next quarter, commit drops to $X"
- "If Deal B closes at 80% of proposed value, best case drops to $X"
- "If you close Deal C and Deal D this month, you're at 95% of quota"
- "Worst case (only commit deals close): $X" Show each scenario's impact on quota attainment as a percentage.
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.
- 12d ago First seen · 97 lines · 17 tokens per session scan A e2a43b75ceb1
sales-forecast is a skill published in the GitHub repository Autter-dev/agentic-sales-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 17 tokens to every session and 859 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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