emergent-pattern-detector

Files (2)

• SKILL.md
• openai.yaml · agents/

SKILL.md (first lines)

# Emergent Pattern Detector

## Goal

Notice when accumulated signals are turning into a real pattern.

This skill does not force pattern where none exists. It checks whether repeated observations now justify treating the pattern as decision-relevant.

## When To Use

- repeated signals are accumulating over time
- separate incidents may now form a meaningful pattern
- something feels like it is emerging but not yet settled

## Scope Boundaries

In scope:

- inspect repeated signals over time
- assess whether a pattern is emerging
- define what would confirm or break the pattern

Out of scope by default:

- final causal proof
- long statistical modeling

## Workflow

1. List the repeated signals or events.
2. Compare time, shape, and recurrence.
3. State the possible emergent pattern.
4. Judge whether it is decision-relevant yet.
5. Name confirming and disconfirming future signals.

## Output Contract

1. `signal_history`
2. `emergent_pattern_candidate`
3. `why_it_may_be_real`
4. `why_it_may_still_be_noise`
5. `decision_relevance`
6. `confirmation_signals`
7. `recommended_followup`
8. `next_step`

## Guardrails

- Do not promote coincidence into pattern without recurrence.
- Keep the threshold for "real enough" tied to decision impact.
- If the pattern is still weak, say so explicitly.

From Alubiama/awesome-cognitive-skills, MIT.

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