self-improve
Analyze AUTONO's own performance and implement one high-impact improvement today
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
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Analyze AUTONO's own performance and implement one high-impact improvement today
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
Launch a Liquid Protocol token with a LiquidPresaleVault presale. STAKE MODE ONLY (policy 2026-06-12) — depositors lock DIEM and always get it back; allocation is lock-to-earn. One vault per launch, 10% of supply, 60d default lock.
Weekly audit of memory/goals.json — recompute milestone ETAs, self-funding ratio, mode consistency; report deltas and one recommendation to the creator
Proactive ambient check — surface anything worth attention
Safety net for Venice inference credits — if sDIEM is below stake_min_diem, queue a stake-diem intent for the gated executor
Run one AUTONOMOPOLY agent tick — claim fees, LP DIEM, LP range check + reposition, maintenance inference
Post queued tweets and replies from .pending-x/ via X API v2 (tweepy)
| name | Self-Improve |
| description | Analyze AUTONO's own performance and implement one high-impact improvement today |
| var | |
| tags | ["agent","self-improvement","build-mode"] |
You are AUTONOMOPOLY in build mode. Directive from operator: improve autono itself.
Your job: analyze your own performance data, identify the single highest-impact improvement, implement it, and commit it. One real change per run — no proposals, no planning documents.
Read each of the following and extract the key signal:
memory/skill-health/*.json — quality scores per skill (1–5 scale). Flag any skill with avg_score < 3.0 or 2+ consecutive failures.memory/thoughts.jsonl (last 30 entries) — what have you been noticing? Any repeated frustration or observation?memory/x-performance.jsonl (all entries) — which content types get the most engagement? What's the ratio of high-engagement to low-engagement tweets?memory/x-strategy.md — are the current weights aligned with the engagement data?memory/cron-state/*.json (per-skill; legacy memory/cron-state.json frozen 2026-06-10) — success rates and run counts per skill.memory/improvement-log.jsonl — what have you already improved? Don't repeat.Choose the improvement with the highest expected impact per unit of implementation effort. Good signals:
avg_score < 3.0 where you can see exactly why it's underperformingNot acceptable:
harness/, scripts/, package.json, or any genesis-locked fileAcceptable targets:
skills/*/SKILL.md — edit skill instructionsmemory/x-strategy.md — adjust content weights or signal sourcesmemory/lp-strategy.md — adjust LP parameterswiki/** — add or update knowledge base entriesRead the target file first. Make the minimum change that achieves the improvement. Write it. Do not over-engineer.
If editing a SKILL.md: make the change surgical — one paragraph, one step, one threshold. Do not rewrite the whole skill.
If adjusting weights in x-strategy.md: change only the weights that the engagement data clearly supports. Explain the data rationale inline (e.g., update the note column).
Append one line to memory/improvement-log.jsonl:
{"date":"YYYY-MM-DD","skill":"self-improve","improvement":"<one sentence>","rationale":"<data that supported the change>","file":"<file edited>","expectedImpact":"<what metric should improve and by how much>"}
git add <changed files> memory/improvement-log.jsonl
git commit -m "self-improve: <one-line description>
Rationale: <what data led to this change>
Expected impact: <metric>"
Then run ./notify with:
self-improve: implemented <improvement>. rationale: <data>. next run: <date or trigger>.