Skill: matthiasbeckmann987-spec/beckmann-x-self-improving-agent (version 1.0.0)

\--- 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| |[REDACTED_SECRET]|**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. \--- ## 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). \--- ## 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. \--- ## 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." \--- ## Escalation Protocol (Step by Step) ### 1 — Load the graph ```javascript 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`: ```markdown ## \[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> ``` \--- ## 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:** ```markdown - 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. \--- ## 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| \--- ## 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| \--- ## Uninstall 1. Delete this `SKILL.md`. 2. Both `pskoett/self-improving-agent` and [REDACTED_SECRET] continue working independently. No data in `.learnings/` is affected.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)