self-improve
Analyze AUTONO's own performance and implement one high-impact improvement today
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Analyze AUTONO's own performance and implement one high-impact improvement today
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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>.