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 mphinance/alpha-skills --skill earnings-trade-analyzergit clone --depth 1 https://github.com/mphinance/alpha-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/mphinance/alpha-skills/earnings-trade-analyzer)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/earnings-trade-analyzer"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/earnings-trade-analyzer/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/mphinance/alpha-skills/earnings-trade-analyzer"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/earnings-trade-analyzer.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 | $0.00083 | $0.00712 |
| Opus 5 | $0.00042 | $0.00356 |
| Sonnet 5 | $0.00017 | $0.00142 |
| Haiku 4.5 | $0.00008 | $0.00071 |
Grade A, and why
earnings-trade-analyzer 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 11d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Earnings Trade Analyzer - Post-Earnings 5-Factor Scoring
Analyze recent post-earnings stocks using a 5-factor weighted scoring system to identify the strongest earnings reactions for potential momentum trades.
When to Use
- User asks for post-earnings trade analysis or earnings gap screening
- User wants to find the best recent earnings reactions
- User requests earnings momentum scoring or grading
- User asks about post-earnings accumulation day (PEAD) candidates
Prerequisites
- FMP API key (set
FMP_API_KEYenvironment variable or pass--api-key) - Free tier (250 calls/day) is sufficient for default screening (lookback 2 days, top 20)
- Paid tier recommended for larger lookback windows or full screening
Workflow
Step 1: Run the Earnings Trade Analyzer
Execute the analyzer script:
# Default: last 2 days of earnings, top 20 results
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py --output-dir reports/
# Custom lookback and market cap filter
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
--lookback-days 5 \
--min-market-cap 1000000000 \
--top 30 \
--output-dir reports/
# With entry quality filter
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
--apply-entry-filter \
--output-dir reports/
Step 2: Review Results
- Read the generated JSON and Markdown reports
- Load
references/scoring_methodology.mdfor scoring interpretation context - Focus on Grade A and B stocks for actionable setups
Step 3: Present Analysis
For each top candidate, present:
- Composite score and letter grade (A/B/C/D)
- Earnings gap size and direction
- Pre-earnings 20-day trend
- Volume ratio (20-day vs 60-day average)
- Position relative to 200-day and 50-day moving averages
- Weakest and strongest scoring components
Step 4: Provide Actionable Guidance
Based on grades:
- Grade A (85+): Strong earnings reaction with institutional accumulation - consider entry
- Grade B (70-84): Good earnings reaction worth monitoring - wait for pullback or confirmation
- Grade C (55-69): Mixed signals - use caution, additional analysis needed
- Grade D (<55): Weak setup - avoid or wait for better conditions
What ships with it
15 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.
- README.md 2.9 KB
- references/scoring_methodology.md 3.8 KB
- scripts/analyze_earnings_trades.py 14 KB runs code
- scripts/calculators/__init__.py 38 B runs code
- scripts/calculators/gap_size_calculator.py 4.6 KB runs code
- scripts/calculators/ma200_calculator.py 2.4 KB runs code
- scripts/calculators/ma50_calculator.py 2.2 KB runs code
- scripts/calculators/pre_earnings_trend_calculator.py 3.0 KB runs code
- scripts/calculators/volume_trend_calculator.py 3.7 KB runs code
- scripts/fmp_client.py 13 KB runs code
- scripts/report_generator.py 8.7 KB runs code
- scripts/scorer.py 3.9 KB runs code
- scripts/tests/conftest.py 307 B runs code
- scripts/tests/test_earnings_trade_analyzer.py 41 KB runs code
- scripts/tests/test_fmp_client_historical.py 2.6 KB runs code
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.
- 11d ago First seen · 78 lines · 83 tokens per session scan A 07f037dde0ee
earnings-trade-analyzer is a skill published in the GitHub repository mphinance/alpha-skills (22 stars, last pushed 13d ago), licensed MIT. It adds 83 tokens to every session and 712 once invoked, about $0.0004 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 2026-08-30.
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