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Decision Health — detection patterns

Proposed signals for human review. Not verdicts.

Module: detection.pydata/detection.json → Intelligence → Know vs assume.
Complement: deep_scan.pydownload + parse /proposals/*.md, open issues with bodies, governance workflow YAMLs → stored corpus → Patterns / Predictions (aggregates). Raw grant-execution lists are Admin-reference only.

Pattern What we measure Artifact
Fast-track past objections Closed CIP PRs where approval/merge is close to unresolved objection language CIP PRs
Feasibility limbo CIP Approved/Active >60 days without Final CIP dirs / status
Grant milestone quality Adoption vs artifact framing /proposals/*.md (+ linked issues) — not PR checklist
SV weight concentration Weight rows parsed from CIP text (not on-chain votes) CIP text
Featured App tech review FA-related CIP threads lacking review keywords CIP / FA threads
DA self-funding Share of funded proposal filenames with DA-* /proposals/*.md listing
Grant execution activity Open issues with milestone/delivery language Dev Fund issues (excl. PRs)

Patterns (structural signals)

Module: org_intel.py_patterns() / _predictions()data/org_snapshot.json → Intelligence → Patterns.

Patterns are grounded structural signals, not Decision-Health card tags. Each one is measured from live GitHub counts or classified-card data and rendered as an evidence-backed card. Contract for every pattern object:

Field Rule
stat The headline number/date/ratio — the observation. Tabular, verifiable.
evidence[] 1+ rows, each {label, value, url} linking to the source (repo issues/PRs, board, CIP). No evidence → no pattern.
implication The strategic read, impersonal. Why this drives roadmap / review / outreach.
watch The concrete artifact that would confirm or break the signal.
basis One line naming the measured source (counts, timestamps, attribution).
strength 1–3, derived from measured magnitude — never a guess.
kind structural | people | surface | governance (drives colour).

Every pattern must name a measurable artifact (count, date, ratio, or linked PR/issue). Strength and cited evidence carry confidence — emit nothing that cannot point at a source.

Predictions (_predictions) follow the same discipline: from (surface), trigger (measured), watch_for (next artifact), confirm (what closes the loop), url (source).

Posture

Every Decision-Health row is a proposed signal. Soft language (“possible”, “worth a look”). Cards remain the HITL correction path; detection does not auto-inject into grounding.

Governance CI (sig-label / champion / milestone / aipricheck) is executable — cite workflows, don’t paraphrase as optional policy.

Caps

~12 CIP PRs for fast-track · ~35 CIP dirs for limbo · search API capped · call count in detection.json.
Deep scan: proposals directory listing + ≤3 pages of open issues.

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