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 agentii-ai/agentii-investment-intelligence --skill earnings-previewgit clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligenceWrote 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/agentii-ai/agentii-investment-intelligence/earnings-preview)<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/earnings-preview"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/earnings-preview/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/agentii-ai/agentii-investment-intelligence/earnings-preview"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/earnings-preview.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 | $0.00046 | $0.02378 |
| Opus 5.5 | $0.00018 | $0.00951 |
| Sonnet 5.5 | $0.00009 | $0.00476 |
| Haiku 4.5 | $0.00005 | $0.00238 |
Grade A, and why
earnings-preview 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 yesterday.
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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preflight
Run canonical pre-flight per contracts/preflight.md.
Run the canonical pre-flight sequence — MCP health probe, ticker resolution, workspace
style.md override, memory load, and coverage check. See contracts/preflight.md.
Office dependency probe (FR-043) — this skill produces .pptx via Bash + python-pptx:
- Live Office session? If
mcp__office__*tools are present (Cowork), drive the live document instead of headless Python. - Python library:
Bash: python3 -c "import pptx"— if exit ≠ 0, fall back to.mdslide spec withdata_availability: degraded+python_pptx_missing: true. - LibreOffice:
Bash: which sofficefor structural validation and PDF export.
If the Python library is absent, report the exact remediation (install the
python-pptx package) and produce the .md degraded fallback per
contracts/office-tooling.md.
Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md — carry the _run_id from your first tool result and name yourself (and your parent, if you were spawned).
Use the returned temporal_frame and bind guidance/estimates to target_period_id.
Triggers
- generate earnings preview deck
- build earnings preview presentation
- create quarterly earnings slides
- earnings preview pptx
- earnings summary presentation
- consensus estimates presentation
- earnings surprise summary deck
- quarterly results presentation
- earnings catalyst calendar slides
- pre-earnings analyst deck
Defaults
| Parameter | Default | Notes |
|---|---|---|
| slide_count | 4-6 | Title, Company Overview, Consensus Estimates, Historical Surprises, Catalysts, Outlook |
| lookback_quarters | 4 | Trailing 4 quarters for trend analysis |
| peer_count | 3-5 | From search_companies sector peers |
| source_footers | required | Every slide has standard agentii citation footer |
| template | institutional-default | Dark header bar, agentii blue accent, 12pt body |
What ships with it
8 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.
- yesterday Changed · +3 lines bfea71373779
- 1mo ago Changed · +1 lines 5f8b4f645feb
- 1mo ago Changed · +8 lines cafb504b1bef
- 1mo ago Changed · +1 lines · -3 tokens per session e25d64a117b0
- 1mo ago Changed · +2 lines · +3 tokens per session ae1f3e703a12
- 1mo ago First seen · 143 lines · 46 tokens per session scan A 427057328b3c
earnings-preview is a skill published in the GitHub repository agentii-ai/agentii-investment-intelligence (209 stars, last pushed yesterday), licensed Apache-2.0. It adds 46 tokens to every session and 2,378 once invoked, about $0.0002 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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