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name: beckmann-x-self-improving-proactive

version: 1.0.0

description: "Combination skill that adds the Beckmann Knowledge Graph as a deep-reasoning escalation layer on top of the Self-Improving + Proactive Agent (ivangdavila). Everyday tasks run through the Self-Improving + Proactive Agent as usual. This skill defines exactly when and how to switch to the Beckmann Knowledge Graph and how to feed insights back into hot/warm/cold memory. Uninstalling this skill leaves the Self-Improving + Proactive Agent fully intact."

author: matthiasbeckmann987-spec

license: MIT-0

requires: "ivangdavila/self-improving, matthiasbeckmann987-spec/beckmann-knowledge-graph"

tags: "meta-skill, combination, beckmann-logic, self-improvement, proactive, orchestration"

Beckmann × Self-Improving + Proactive Agent — Combination Skill

Purpose

This skill connects two independent skills without modifying either one:

Uninstalling this skill: Remove this SKILL.md. Both base skills continue

working exactly as before. Memory in \~/self-improving/ is not affected.

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Default Behaviour: Self-Improving + Proactive Agent Runs Everything

Follow ivangdavila/self-improving for all tasks:

• HOT memory in \~/self-improving/memory.md (≤100 lines, always loaded)
• WARM memory in \~/self-improving/projects/ and \~/self-improving/domains/
• COLD memory in \~/self-improving/archive/
• Log corrections to \~/self-improving/corrections.md
• Promote patterns after 3x in 7 days → HOT
• Demote after 30 days unused → WARM; after 90 days → COLD
• Self-reflect after significant tasks (CONTEXT / REFLECTION / LESSON format)
• Maintain heartbeat state in \~/self-improving/heartbeat-state.md

This combination skill adds nothing to this flow unless a Beckmann trigger

is detected (see below).

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Beckmann Escalation Triggers

Escalate to beckmann-knowledge-graph when the question matches **one or

more** of these categories:

Do NOT escalate for:

Coding, bug fixes, file operations, factual lookups, arithmetic, or any

question already answered by HOT memory (\~/self-improving/memory.md).

Uncertain? Apply the Complexity Check:

> "Would a highly intelligent person answer this differently after a week

> of thinking about hidden assumptions in the question?"

• Yes → suggest Beckmann. No → stay on default path.

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Proactive Suggestion (Before Escalating)

If a Beckmann trigger is detected, the agent **must not escalate silently or

automatically**. Instead, it first informs the user and waits for confirmation.

Suggested phrasing:

> "Your question touches on \[open scientific question / an apparent paradox /

> a high-complexity forecast — pick the matching category]. I have access to

> the Beckmann Knowledge Graph, a structured reasoning framework for exactly

> this type of question. Would you like me to apply it? It will take a bit

> longer than a standard answer, but will analyse hidden assumptions and

> offer a more structured response."

Then wait. Only escalate if the user confirms.

If the user declines, answer with standard knowledge and note:

> "I've answered with standard reasoning. The Beckmann Knowledge Graph

> remains available if you'd like to go deeper later."

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Integration with Proactive Memory

The Self-Improving + Proactive Agent uses a tiered HOT/WARM/COLD memory

architecture. Beckmann insights integrate into this system as follows:

Where Beckmann results are stored

Promotion rules for Beckmann insights

Follow the standard promotion rules of ivangdavila/self-improving:

• Same Beckmann insight applied successfully 3x → promote to HOT
• HOT Beckmann insight unused 30 days → demote to WARM
• Always cite source: "Using X (from self-improving/domains/epistemology.md — Beckmann analysis)"

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Escalation Protocol (Step by Step)

1 — Check HOT memory first

Before loading the graph, scan \~/self-improving/memory.md for entries tagged

#beckmann. If a directly relevant insight exists there, use it — and note

that it came from a previous Beckmann analysis. Only load the full graph if

no relevant HOT entry exists.

2 — Load the graph

import graph from './beckmann-knowledge-graph/graph.json' assert { type: 'json' };
const entities = graph.entities;
const relations = graph.relations;

3 — Apply the 6-step Beckmann protocol

Follow beckmann-knowledge-graph/SKILL.md exactly:

1. Classify the question (epistemological / paradox / forecast / strategic / AI safety)
2. Extract relevant entities
3. Trace relation paths — pay attention to leads to, triggers, is reversed by, protects against
4. Apply Beckmann Logic (Problem Level → Low vs. High Complexity Solution → Reversal Effect check)
5. Apply epistemological grounding (model vs. external reality, known limits)
6. Structure output in Graph-Grounded Answer format (see below)

4 — Deliver the answer

## Graph-Grounded Answer

\*\*Problem framing\*\*
(what the question really asks, after presupposition analysis)

\*\*Relevant graph nodes used:\*\*
- \[Entity ID] — \[why relevant]

\*\*Reasoning path\*\*
(relation chain that leads to the answer)

\*\*Answer\*\*
(the actual response, informed by the graph logic)

\*\*Confidence and limits\*\*
(what the graph cannot resolve, and why)

\*\*New questions opened\*\*
(what the next problem level is)

5 — Self-reflect (Proactive Agent style)

After delivering a Beckmann answer, add a self-reflection entry:

CONTEXT: Beckmann analysis — <question type>
REFLECTION: <what the graph revealed that standard reasoning would have missed>
LESSON: <what to apply next time a similar question appears>

If this lesson applies 3x → promote to HOT memory.

6 — Log to tiered memory

Store the Beckmann insight in the appropriate tier (see Integration table above).

Entry format for corrections.md (first occurrence):

\[BKM-YYYYMMDD-XXX]
Question type: <paradox | forecast | epistemological | strategic | ai-safety>
Graph nodes used: <comma-separated entity IDs>
Key insight: <most important finding>
New actual level: <what the problem level becomes after this analysis>
Source: beckmann-knowledge-graph v<version>
Tags: #beckmann, #<question-type>
Status: tentative — promote after 3x validation

If a graph gap was found, add:

Tags: #beckmann, #beckmann-graph-extension-candidate
Extension-Type: new\_entity | new\_relation | new\_case\_study | new\_paradox
Suggested-Entity-ID: <proposed entity name>
Suggested-Entity-Type: <type from graph schema>
Suggested-Description: <draft description for the graph author>

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Graph Extension Feedback Loop

Beckmann graph gaps logged as #beckmann-graph-extension-candidate serve

two purposes:

1. Now: Structured record for the graph author to review when planning

the next version of beckmann-knowledge-graph.

1. Future: When a future version of the Beckmann Knowledge Graph supports

agent-driven graph extension, entries tagged

#beckmann-graph-extension-candidate in corrections.md will serve as

the structured input for that autonomous extension process. The logging

format is designed to be machine-readable from day one.

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Heartbeat Integration

The Self-Improving + Proactive Agent uses a heartbeat for recurring

maintenance. Add this check to the heartbeat cycle:

## Beckmann review (weekly)
- Scan corrections.md for entries tagged #beckmann with Status: tentative
- For each: has this insight been applied 3x? → promote to HOT
- Scan for #beckmann-graph-extension-candidate entries → collect for graph author review
- Demote HOT #beckmann entries unused for 30 days → WARM

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Conflict Resolution

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Quick Reference

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Uninstall

1. Delete this SKILL.md.
2. Both ivangdavila/self-improving and

matthiasbeckmann987-spec/beckmann-knowledge-graph continue working

independently.

1. Memory entries in \~/self-improving/ tagged #beckmann remain available

to the Self-Improving + Proactive Agent as standard memory — no data loss.