detect-tensions

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Detect productive contradictions between notes - high semantic similarity with opposing conclusions that represent synthesis opportunities

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Detect Productive Tensions

ℹ️ First, set expectations: before anything else, print one short line with this skill's version and its most recent change - the top entry of metadata.changelog above - e.g. detect-tensions vX.Y - recent: <summary>. Then proceed.

Scans the knowledge base for productive contradictions: note pairs with high semantic similarity but opposing conclusions. These tension zones are where the most valuable articles and frameworks emerge.

State Dependencies

Source	Location	Read	Write	Description
Enrichments	`resources/brain-graph/data/graph_enrichments.json`	✓	✓	Tension records saved
FAISS Index	`resources/local-brain-search/data/brain.faiss`	✓		Similarity search
Metadata	`resources/local-brain-search/data/brain_metadata.pkl`	✓		Note content

Process

Step 1: Run tension detection

Default thresholds (similarity > 0.75, divergence > 0.3):

cd $PROJECT_ROOT/resources/brain-graph
../local-brain-search/venv/bin/python cli.py tensions

Broader search (more results, lower quality):

../local-brain-search/venv/bin/python cli.py tensions --similarity 0.70 --divergence 0.2

Step 2: Filter false positives, then present synthesis opportunities

The raw count is dominated by false positives - discard them before presenting:

Boilerplate / near-duplicate pairs. The signature is high similarity with maximal divergence (e.g. sim ≈ 1.00, divergence ≈ 1.00), and pairs where both notes are changelogs, registries, or near-identical restatements of one principle. These are detector artifacts, not contradictions.

Keep only pairs that assert genuinely opposing conclusions about the same question. For each surviving tension, explain:

What the two notes assert
Why they contradict
What synthesis opportunity exists (article topic, framework potential)

Step 3: Track existing tensions

../local-brain-search/venv/bin/python cli.py status --json

Check tension_count for total tracked tensions.

Step 4: Probe for cross-vocabulary tensions the detector cannot see

The detector pairs notes by cosine similarity (floor ~0.70) and scores opposition with a keyword heuristic (negation vs. assertion words). It is therefore structurally blind to the most valuable tensions: genuine contradictions are usually cross-vocabulary - two frameworks reaching opposite conclusions in different language - which fall BELOW the similarity floor and read too assertively for the keyword check. A thin or empty result does NOT mean no tensions exist; this tool surfaces candidates, it does not certify absence.

Compensate by manually checking known opposing-framework pairs even when they score below threshold, e.g.:

loss aversion (prospect theory) ↔ ergodicity / Kelly (bias vs. correct policy)
Bayesian/Brier "assign a probability" ↔ "There Is No Bayesian Dial" / radical uncertainty
heuristics-and-biases ↔ ecological / evolutionary rationality
expert failure as psychological ↔ expert failure as structural

Treat these as candidate tension edges regardless of the detector's similarity score.

Root-cause fix (code, out of scope for this playbook): durable precision needs resources/brain-graph/tension.py to replace the keyword stance heuristic with an LLM stance-classifier on a shared proposition, lower the similarity floor with theme/MOC-anchored cross-cluster pairing, and exclude index/changelog-layer nodes from the scan.

Key Principle

Tensions are features, not bugs. The system NEVER auto-resolves tensions. It surfaces them as the most productive intellectual territory in the vault.