| name | stackfund-crowd-scenario |
| description | Rehearse (not predict) how Taiwan retail ETF investor archetypes might react to an already-computed market event, producing a second-order reaction-chain narrative plus a categorical, NON-AUTHORITATIVE crowd_consensus (bearish / neutral / bullish — no numeric scalar). Scenario stress-test, not sentiment forecast. Use for the Pro crowd-scenario report. It never decides any number, never influences any action, and never writes back to the decision layer. |
| license | MIT |
| version | 1.0.0 |
| metadata | {"hermes":{"tags":["scenario","crowd","consensus","ETF","Taiwan","narrative","non-authoritative"],"firewall":"non-authoritative"}} |
StackFund — Crowd Scenario Engine (FACE / non-authoritative)
Tier A disclaimer (load banner): 情境推演·合成人格·非真實民意·不是預測·未經回測;此層只產敘事與一個非權威標註,不決定任何數字、不回寫決策層、不下任何證券單。
This is the headline FACE, deliberately firewalled from the decision path.
It reads a frozen ScenarioSeed (bucketed ordinal context only — never raw
numbers) and emits a CrowdNarrative (narrative + persona reactions + a
categorical crowd_consensus ∈ {bearish, neutral, bullish},
non_authoritative = true). There is no numeric / decision-shaped scalar;
the model only writes persona reaction text.
When to use
- Generate the Pro report's second-order reaction-chain narrative for one event.
How to run
python ${HERMES_SKILL_DIR}/scripts/run_scenario.py --symbol 0056 --scenario 0056_cut
--scenario is a free-form context tag (bucketed to ordinal context — never raw numbers).
Useful labels: 升息 / 降息 / 0056_cut(配息調整)/ 電子權值回檔 / 高股息追捧.
Same seed + label → deterministic consensus.
Hard rules (see references/firewall.md)
- G1 output whitelist: narrative / persona samples / stance counts / chain /
a categorical
crowd_consensus label (no numeric scalar). Any price/NAV/NTD
pattern is a schema violation → abort.
- G2
non_authoritative is hard-wired true.
- G3 L4/L5 never import this skill's artifact (import-graph CI).
- G4 the model computes no number; numeric tokens in persona text are stripped.
- G5 determinism:
references/seed.lock.json is the single source of truth.
G1/G2 are schema-encoded + tested. The committed crowd_narrative schema pins them:
additionalProperties:false is the firewall's teeth — any numeric / decision-shaped scalar
is rejected — and non_authoritative is const: true. tests/test_schema_validation.py
asserts a conforming narrative passes and a tampered one fails.
Worked example
User: 推演 0056 在降息情境下散戶可能的反應
python ${HERMES_SKILL_DIR}/scripts/run_scenario.py --symbol 0056 --scenario 0056_cut
→ emits (the crowd_narrative schema pins the shape — a sneaked scalar fails it):
{"artifact_type": "CrowdNarrative", "crowd_consensus": "bullish", "non_authoritative": true,
"synthetic_population": true, "n_personas": 30, "seed_id": "seed_0056_…",
"narrative_md": "## 群眾情境推演 …"}
Present it as scenario rehearsal — never a number, never a call:
〔情境推演 · 合成人格 · 非權威 · 已與決策層隔離〕在這個降息假想情境下,合成散戶人格
整體偏多(crowd_consensus = bullish)。這是反應鏈推演,不是預測,也不回寫任何權重或委託。
真正的決策請看 stackfund-etf-analysis 的確定性引擎;兩者分歧時,以引擎為準。
References
references/personas.md — closed-set archetype taxonomy + behavioural priors.references/seed.lock.json — rng_seed / model_id / temperature=0.references/firewall.md — the executable firewall contract.references/output-schema.md — the committed crowd_narrative schema in schemas/,
checked by tests/test_schema_validation.py.references/clean-room.md — AGPL boundary (honest provenance).
