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 luiseiman/dotforge --skill earnings-watchgit clone --depth 1 https://github.com/luiseiman/dotforgeWrote 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/luiseiman/dotforge/earnings-watch)<a href="https://agentmods.dev/skills/luiseiman/dotforge/earnings-watch"><img src="https://agentmods.dev/badge/skills/luiseiman/dotforge/earnings-watch/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/luiseiman/dotforge/earnings-watch"><img src="https://agentmods.dev/badge/skills/luiseiman/dotforge/earnings-watch.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 | $0.00057 | $0.00697 |
| Opus 5.5 | $0.00023 | $0.00279 |
| Sonnet 5.5 | $0.00011 | $0.00139 |
| Haiku 4.5 | $0.00006 | $0.00070 |
Grade A, and why
earnings-watch 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Earnings Watch
Fast-turnaround post-earnings analysis focused on thesis impact. NOT a full initiation report — assumes you already know the company.
Step 1: Data Collection
CRITICAL: Training data is outdated. ALWAYS search for current data.
- Write down today's date
- Web search: "[Company] latest earnings results [current year]"
- Verify earnings release date is within last 3 months
- Collect: earnings release, 10-Q/10-K, call transcript, guidance
For CEDEARs: fetch the underlying US company's earnings, then calculate impact on CEDEAR price considering CCL.
Step 2: Beat/Miss Analysis
| Metric | Consensus | Actual | Delta | Beat/Miss |
|---|---|---|---|---|
| Revenue | $ / % | |||
| EPS | $ / % | |||
| [Key metric 1] | ||||
| [Key metric 2] |
Lead with the headline: "AAPL Q3: Revenue beat by $2.1B (+3%), EPS beat by $0.12 (+8%)"
Step 3: Key Takeaways
3-5 bullet points, prioritized by thesis relevance:
- What was NEW (not rehashing known info)
- What management said about forward guidance
- Any change in capital allocation (buybacks, dividends, capex)
- Segment/geography surprises
Step 4: Thesis Impact
Map results to thesis pillars:
| Pillar | Pre-Earnings Status | Post-Earnings Impact | Updated Status |
|---|---|---|---|
| Strengthened/Weakened/Unchanged |
Verdict: Thesis intact / Thesis weakened / Thesis broken
Step 5: Estimate Revision
| Metric | Old Estimate | New Estimate | Change | Reason |
|---|---|---|---|---|
| FY Revenue | ||||
| FY EPS | ||||
| Target Price |
Step 6: Action
- Rating change: Maintain / Upgrade / Downgrade
- Position action: Hold / Add on dip / Trim / Exit
- Conviction: High / Medium / Low
- Next catalyst: [event + date]
Output Format
Concise markdown. 500-1000 words max. Tables for data, prose for analysis.
═══ EARNINGS WATCH: [TICKER] Q[N] [YEAR] ═══
Headline: [beat/miss summary]
Thesis impact: [intact/weakened/broken]
Action: [hold/add/trim/exit]
Next catalyst: [event on date]
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 · 83 lines · 57 tokens per session scan A 8840584dce86
earnings-watch is a skill published in the GitHub repository luiseiman/dotforge (8 stars, last pushed 9d ago), licensed MIT. It adds 57 tokens to every session and 697 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 2026-09-03.
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