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 perniemann/pnCore --skill pn-earnings-analysisgit clone --depth 1 https://github.com/perniemann/pnCoreWrote 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/perniemann/pncore/pn-earnings-analysis)<a href="https://agentmods.dev/skills/perniemann/pncore/pn-earnings-analysis"><img src="https://agentmods.dev/badge/skills/perniemann/pncore/pn-earnings-analysis/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/perniemann/pncore/pn-earnings-analysis"><img src="https://agentmods.dev/badge/skills/perniemann/pncore/pn-earnings-analysis.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 | $0.00071 | $0.01077 |
| Opus 5.5 | $0.00028 | $0.00431 |
| Sonnet 5.5 | $0.00014 | $0.00215 |
| Haiku 4.5 | $0.00007 | $0.00108 |
Grade A, and why
pn-earnings-analysis 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Earnings analysis
When to use
- After a company reports quarterly results: transcript available, filing released, or both.
- Portfolio monitoring pass after a position reports.
- As a workflow step in a coverage cadence (replace with fresh data each quarter).
Pre-requisites
Load get_skill("pn-fsi-analyst-discipline"). Confirm what was supplied: transcript, 10-Q/10-K excerpt, press release, or some combination. State source and filing date.
Instructions
1. Establish the consensus baseline
Before reading actuals, record the pre-print consensus (user-supplied or sourced from MCP connector if available):
| Metric | Consensus estimate | Source |
|---|---|---|
| Revenue | $Xm | [source] |
| EBITDA | $Xm | [source] |
| EPS | $X.XX | [source] |
| Key operational KPI (MAU, units, ARR, etc.) | X | [source] |
If no consensus is available, note: "No consensus baseline supplied — beat/miss analysis will be omitted; absolute actuals only."
2. Extract actuals from filing or transcript
Pull the same metrics from the supplied source. Cite the exact page, section, or timestamp for each figure. Flag any metric that requires adjustment (e.g. management-defined "Adjusted EBITDA" vs. GAAP EBITDA — show the bridge).
3. Beat / miss delta
For each metric:
Beat/Miss = Actual – Consensus | % delta = (Actual – Consensus) / |Consensus|
Label each: Beat, In-line (±1%), or Miss. Note any metric where consensus dispersion was unusually wide (>10% interquartile range) — the beat/miss signal is weaker there.
4. Model update
Identify which model line items change given the new print. For each change:
- What changes (e.g. FY revenue, margin guidance range, NWC assumption).
- Direction and magnitude (e.g. "FY revenue +2% vs. prior model; guided midpoint $Xm vs. prior $Ym").
- Whether the change comes from guidance, extrapolation, or agent estimate (
[est.]).
If no model exists in context, output the update as a list of line-item revisions to apply to the analyst's model.
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 · 115 lines · 71 tokens per session scan A 3b65d6a4bfc4
pn-earnings-analysis is a skill published in the GitHub repository perniemann/pnCore (0 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 1,077 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 2026-09-03.
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