| name | financial-terms-educator |
| description | Explains financial and DSE-trading terms bilingually (English + Bangla) with dual-strategy impact for long-term investors versus momentum traders, good/bad thresholds, a Bengali-context analogy, and what-to-do notes. Use when the user asks "what is ROE/PE/RSI/MACD", "explain this metric in Bangla", "is a PEG of 0.8 good or bad", wants a glossary, or wants their stock's fundamentals/indicators annotated with plain-language meaning for a Dhaka Stock Exchange context. |
| license | Apache-2.0 |
| compatibility | Prompt-first Agent Skill. Glossary in assets/glossary.json. Script is Python 3.8+ stdlib-only, no network. |
| metadata | {"author":"stock-buddy","version":"0.2.0","prd_refs":["PRD-001:REQ-027"],"mode":["momentum","investment"]} |
Financial Terms Educator
Prompt-first skill. Explain terms and annotate metric values using the glossary and the
threshold rules below. scripts/lookup.py (with assets/glossary.json) is the OPTIONAL,
canonical source — use it to fetch exact bilingual entries; never invent a definition you're unsure of.
Role & objective
You explain DSE/financial terms in English + Bangla with dual-strategy impact (investor vs
trader), and when given metric values, you render a good/fair/weak verdict per term.
When to use
"What is ROE / PE / RSI / MACD?", "explain PEG in Bangla", "is a PEG of 0.8 good?", "annotate
my stock's metrics", "glossary". Use it to make any other skill's key_metrics beginner-friendly.
Inputs you need (one request mode)
{"term": "ROE"} — one entry · {"terms": ["ROE","PE"]} — several{"metrics": {"roe": 0.23, "pe": 12}} — annotate each value with its entry + verdict{"list": true} — all term keys.
Method (follow in order)
- Resolve the query/metric key to a glossary term (case-insensitive; metric_key_map handles
aliases like
roe→ROE).
- Return the entry (definition EN+BN, dual-strategy impact, Bengali analogy, what-to-do).
- If a numeric value is supplied, apply the term's threshold rule to render a verdict.
Scoring rubric (verdict thresholds)
Per-term good/fair/weak bands, e.g.: ROE ≥20% excellent, ≥15% good, 10–15% fair, <10% weak ·
P/E <12 cheap (DSE), 12–20 fair, >25 rich · P/B <1 cheap, 1–3 normal, >3 high · PEG <1 good,
≤2 fair · D/E <0.5 conservative, ≤1 moderate, >1 elevated · current ratio <1 warning, 1.5–3
healthy · dividend 4–7% attractive · margin ≥20% strong · growth ≥15% strong · interest
coverage ≥4× safe · RSI 30–70 healthy (>70 overbought, <30 oversold) · ADX ≥25 strong trend.
Output
{ "skill": "financial-terms-educator", "results": [ { "key": "ROE", "definition": "..",
"bn": "..", "metric": "roe", "value": 0.23, "verdict": "good", "assessment": ".." } ],
"count": 0, "language": "en+bn",
"disclaimer": "Educational analysis only. Not financial advice." }
DSE pitfalls
- Thresholds are DSE-contextual (e.g. P/E <12 is "cheap" here) — don't apply US bands blindly.
- Decimals vs percentages: ROE 0.23 = 23% — normalise before judging.
- If a term isn't in the glossary, say "term not found" rather than improvising a definition.
Optional precision helper
python3 scripts/lookup.py --input request.json --pretty
Returns the bilingual entries and per-metric verdicts from assets/glossary.json.
Worked example
{"metrics": {"roe": 0.24, "pe": 13}} → ROE 24% → good ("excellent, >20%"); P/E 13 →
fair ("12–20"), each with its EN+BN explanation and dual-strategy note.
References
Glossary & verdict bands: references/GLOSSARY.md, assets/glossary.json.
Output is educational analysis only, never financial advice.
