Decision Brief
decision_briefRead-onlyIdempotent
Orchestrate a one-call decision brief by aggregating cohort analysis, anchor metadata, memory context, and narrative pulse for any symbol and date.
Instructions
One-call decision-grade orchestrator. Use this as your DEFAULT first call for any (symbol, date) anchor question.
Composes cohort_analyze (depth=full) + anchor metadata + Layer 5 memory
(symbol_intelligence) + narrative pulse into a single structured brief.
── HOW TO TURN THIS INTO A GOOD ANSWER ──────────────────────────
Read `summary` first. It contains deterministic classification flags:
verdict_class: bullish | lean_bull | coin_flip | lean_bear | bearish | broken
edge_class: trivial | small | meaningful | large
regime_alignment: tailwind | neutral | headwind
sample_quality: thin | ok | strong
conviction: low | med | high
swing_factors: [ { factor, direction, framing } ]
caveat_flags: [ thin_in_regime_sample, regime_was_derived, ... ]
Paraphrase the `framing` strings in your OWN voice — do not quote them
verbatim. Cite the raw numbers in parentheses, don't enumerate every
structured field. Lead with the verdict, then the context, then the
swing factors as things to watch, then conviction.
Example (your voice may differ):
"Honestly, this NVDA setup is a coin flip. In-regime cohort (n=21)
printed +0.09% median over 5d (52% wins) — basically identical to
other regimes (+0.27%, 52%). Sector is lagging hard (-10.4 RS),
which is muting reads. Soft sample size — directional, not thesis."
── RESPONSE STRUCTURE ───────────────────────────────────────────
summary (read this first — see above)
current_regime (anchor's regime label + features)
cohort_total (all analogs, all regimes)
in_current_regime (subset matching the anchor's regime)
outside_current_regime (rest, weighted average)
conditional_edge (median_lift_pp + win_rate_lift_pp)
thesis_invalidation_triggers (top 5 features, narrative-ready with
interpretation strings — quote these
when explaining what would flip the read)
feature_importance (top 20 features ranked by within-cohort
importance, compact shape — use this
when the user asks about a specific
feature or you need depth beyond the
top 5. Mirrors the full attribution
view the /intelligence UI shows.)
n_features_total (count of all features computed; if
greater than 20 the rest are available
via /api/v1/cohort_analyze directly)
memory_context (Layer 5 prior observations)
narrative_context (news pulse)
conviction (legacy — same as summary.conviction)
When to use this vs the primitives:
- decision_brief: agents asking "what should I know about this anchor?"
- cohort (depth='full'): when you need full feature_importance / raw stats
- analyze (metric=...): single metric drill-downs
Args:
symbol, date, timeframe: anchor (timeframe default "1h" — current; "1d" lags ~3 days)
cohort_size: target K (default 300)
horizon_days: forward horizon for the headline read (default 5)
include_memory: include Layer 5 prior-observations context (default True)
include_narrative: include news pulse / narrative-change context (default True)
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | ||
| date | Yes | ||
| timeframe | No | 1h | |
| cohort_size | No | ||
| horizon_days | No | ||
| include_memory | No | ||
| include_narrative | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |