Beckmann Knowledge Graph × Self-Improving Agent

Integrates Beckmann Knowledge Graph for deep reasoning on complex, philosophical, or strategic questions within the Self-Improving Agent framework, escalatin...

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name: beckmann-x-self-improving-agent 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 Agent (pskoett). Everyday tasks run through the Self-Improving Agent as usual. This skill defines exactly when and how to switch to the Beckmann Knowledge Graph and how to feed insights back. Uninstalling this skill leaves the Self-Improving Agent fully intact." author: matthiasbeckmann987-spec license: MIT-0 requires: "pskoett/self-improving-agent, matthiasbeckmann987-spec/beckmann-knowledge-graph" tags: "meta-skill, combination, beckmann-logic, self-improvement, orchestration"

Beckmann × Self-Improving Agent — Combination Skill

Purpose

This skill connects two independent skills without modifying either one:

Skill	Role here
pskoett/self-improving-agent	Default engine for all tasks
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. No data is lost.

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Default Behaviour: Self-Improving Agent Runs Everything

Follow pskoett/self-improving-agent for all tasks:

• Log errors to .learnings/ERRORS.md
• Log learnings to .learnings/LEARNINGS.md
• Promote patterns after 3 repetitions
• Maintain the LRN-YYYYMMDD-XXX log format

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 existing learnings in .learnings/.

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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Escalation Protocol (Step by Step)

1 — Load the graph

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

2 — 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)

3 — 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)

4 — Log back to Self-Improving Agent

After every Beckmann analysis, add an entry to .learnings/LEARNINGS.md:

## \[LRN-YYYYMMDD-XXX] insight

\*\*Logged\*\*: <ISO-8601 timestamp>
\*\*Priority\*\*: medium
\*\*Status\*\*: pending
\*\*Area\*\*: beckmann

### Summary
Beckmann analysis: <one-line description of the question>

### Details
- 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>

### Suggested Action
<Promote to CLAUDE.md if broadly applicable.
Flag as #beckmann-graph-extension-candidate if a graph gap was found.>

### Metadata
- Source: beckmann-knowledge-graph v<version>
- Tags: #beckmann, #<question-type>

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Graph Extension Feedback Loop

The Beckmann Knowledge Graph is designed to grow. When this combination skill surfaces a gap in the graph, flag it so the graph author can act on it.

Flag as #beckmann-graph-extension-candidate when:

• No entity matched the core concept of the question
• A new historical case study would have strengthened the analysis
• A new relation type was needed but absent
• A new paradox or open question was encountered that the graph doesn't cover

Add to the LEARNINGS entry Metadata:

- 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>

**Future capability:** When a future version of the Beckmann Knowledge Graph supports agent-driven graph extension, entries tagged #beckmann-graph-extension-candidate will serve as the structured input for that process. No changes to the logging format will be needed.

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Conflict Resolution

Situation	Rule
Self-Improving Agent says "move on"; Beckmann analysis still open	Finish the Beckmann analysis first, then log
Beckmann produces a low-complexity solution	Red flag — apply reversal effect check before delivering
A LEARNINGS entry contradicts a Beckmann graph node	Prefer the externally validated answer; log the contradiction as #beckmann-candidate
graph.json missing or unreadable	Fall back to Self-Improving Agent only; log missing graph as error

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Quick Reference

Signal	Action
Coding error, failed command, user correction	→ Self-Improving Agent: log to ERRORS.md or LEARNINGS.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
Beckmann analysis complete	→ Log to LEARNINGS.md with #beckmann tag

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Uninstall

1. Delete this SKILL.md.
2. Both pskoett/self-improving-agent and matthiasbeckmann987-spec/beckmann-knowledge-graph continue working independently. No data in .learnings/ is affected.