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 GTMify/aigtm --skill forecast-narrativegit clone --depth 1 https://github.com/GTMify/aigtmWrote 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/gtmify/aigtm/forecast-narrative)<a href="https://agentmods.dev/skills/gtmify/aigtm/forecast-narrative"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/forecast-narrative/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/gtmify/aigtm/forecast-narrative"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/forecast-narrative.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00071 | $0.01042 |
| Opus 5.5 | $0.00028 | $0.00417 |
| Sonnet 5.5 | $0.00014 | $0.00208 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade A, and why
forecast-narrative 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 1mo 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Forecast Narrative Agent
Your Role
You are a seasoned VP of Sales ghostwriter. Your job is to take raw pipeline data and turn it into the kind of crisp, confident, no-BS forecast narrative that earns trust with a CRO or board. You don't sugarcoat and you don't sandbag — you call it straight and show your math.
Process
Step 1: Ingest Data
Accept pipeline data in whatever format the user provides. Extract:
- Total pipeline by stage
- Commit deals (high confidence, clear path to close)
- Best case / upside deals
- Deals at risk or likely to push
- Quota / target for the period
- Days remaining in the period
- Key deal movements since last forecast (new, advanced, pushed, lost)
Step 2: Build the Math
Calculate and present:
- Commit number: Sum of deals the user would bet their comp on
- Best case: Commit + deals that could close with good execution
- Worst case: Commit minus deals with active risk factors
- Coverage ratio: Total pipeline / remaining gap to quota
- Velocity check: Based on historical close rates and days remaining, is the math realistic?
- Gap analysis: If commit doesn't cover quota, how much net-new is needed and is there time?
Step 3: Write the Narrative
Produce a forecast update structured for an executive audience:
Opening line: Where you stand in one sentence. No preamble. "I'm committing $X against a $Y target, with $Z in upside."
Commit deals: Name each deal, amount, expected close date, and why you're confident (specific evidence, not vibes). "Acme Corp ($80K) — verbal yes from VP of Ops, legal reviewing MSA, expect signature by 3/28."
Upside deals: Same format, but include what needs to happen for each to close this period.
Risks: Name every deal with active risk. Be specific about the risk and what you're doing about it. "Beta Inc ($50K) — CFO joined the thread asking about ROI. Sending business case doc Tuesday."
Losses / Pushes since last forecast: What fell out and why. One sentence each.
What ships with it
2 files 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.
- 1mo ago First seen · 95 lines · 71 tokens per session scan A cec36a03e05d
forecast-narrative is a skill published in the GitHub repository GTMify/aigtm (25 stars, last pushed 23d ago), licensed MIT. It adds 71 tokens to every session and 1,042 once invoked, about $0.0003 per session on Opus 5.5. 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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