Beckmann Knowledge Graph × Self-Improving + Proactive Agent

Integrates Beckmann Knowledge Graph with Self-Improving + Proactive Agent to escalate deep reasoning on specific complex or paradoxical questions with user c...

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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:

Skill	Role here
ivangdavila/self-improving	Default engine for all tasks — with proactive memory and self-reflection
matthiasbeckmann987-spec/beckmann-knowledge-graph	Deep-reasoning escalation for specific question types

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:

#	Category	Example signals
1	Open scientific / philosophical question	"What is consciousness?", "Does free will exist?", "What is dark energy?"
2	Apparent paradox	Question contains an internal contradiction or "impossible" framing
3	High-complexity long-horizon forecast	"How will AI change democracy in 20 years?", "What are AGI systemic risks?"
4	Strategic dead end	Obvious solutions have repeatedly failed; dominant expectations seem to block progress
5	AI safety / architecture question	Dangerous vs. safe AI design, value alignment, AI-human symbiosis
6	Epistemological limit question	"Is it even possible to know X?", "Is a presupposition in this question false?"

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

Type of insight	Target location	Tier
Broad epistemological insight (applies across domains)	\~/self-improving/memory.md	HOT
Domain-specific Beckmann finding (e.g. AI safety, physics)	\~/self-improving/domains/<domain>.md	WARM
Project-specific strategic insight	\~/self-improving/projects/<name>.md	WARM
Graph gap / extension candidate	\~/self-improving/corrections.md + #beckmann-graph-extension-candidate tag	WARM
Insight not yet validated (first occurrence)	\~/self-improving/corrections.md	WARM

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.
2. 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

Situation	Rule
HOT memory and Beckmann analysis disagree	Prefer the externally validated Beckmann answer; log disagreement to corrections.md
Beckmann produces a low-complexity solution	Red flag — apply reversal effect check before delivering
Graph not available	Fall back to Self-Improving + Proactive Agent only; log missing graph to corrections.md
HOT memory already contains a #beckmann entry for this question	Use the HOT entry; skip full graph load; note "from prior Beckmann analysis"

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Quick Reference

Signal	Action
Coding error, failed command, user correction	→ Self-Improving + Proactive Agent: log to corrections.md
"What is consciousness / free will / dark energy?"	→ Escalate to Beckmann
"How will X change in 20 years?"	→ Escalate to Beckmann (forecast)
"Why does X always fail even though it seems logical?"	→ Escalate to Beckmann (reversal effect suspected)
"Is it even possible to know X?"	→ Escalate to Beckmann (epistemological limit)
Graph entity not found	→ Log as #beckmann-graph-extension-candidate in corrections.md
Beckmann analysis complete	→ Self-reflect + log to tiered memory
Heartbeat runs	→ Review #beckmann entries for promotion / demotion

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Uninstall

1. Delete this SKILL.md.
2. Both ivangdavila/self-improving and matthiasbeckmann987-spec/beckmann-knowledge-graph continue working independently.
3. Memory entries in \~/self-improving/ tagged #beckmann remain available to the Self-Improving + Proactive Agent as standard memory — no data loss.