decision-ladder
Ponytail-inspired YAGNI enforcement — climb the decision ladder before writing any code. stdlib → regex → existing module → new code.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Ponytail-inspired YAGNI enforcement — climb the decision ladder before writing any code. stdlib → regex → existing module → new code.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Memory as a learnable skill — store, recall, compress, and consolidate agent memories. Inspired by Stanford AutoMem.
Auto-decide whether a task runs on a LOCAL model or a CLOUD model (DeepSeek, GLM, Nemotron, Grok, Gemma, …) from an automatic effort estimate, and run multi-model fusion (cascade, draft→refine, vote). Use when the user wants automatic local-vs-cloud routing, to add cloud LLM providers, to make local and cloud models collaborate, or mentions effort-based routing, model fusion/ensemble, OpenRouter, DeepSeek, GLM, Nemotron, or Grok.
Fenêtres de contexte pour boucles rétroactives — charge seulement les deltas.
Optimisations DAG/RAG — waves, pruning, memoization, routing.
Generate one self-contained, timestamped HTML dashboard of the system's cost picture — routing savings (control loop), metric trends, current metrics, and the cost of outstanding fixes. Also renders as a live ANSI terminal view (--tui, --watch) and serves a live HTTP API (api.py). Use when the user wants a single visual view of cost/savings/health over time, or a live terminal view they don't have to open a browser for.
Append-only JSONL decision log (.botte/events.jsonl) that every filter in the belt writes to — routing, cache hits, escalations, micro-NN outputs. The single source of truth demo mode, the live dashboard, and session replay all read from. Use when you want to see or emit a live feed of routing/cache/escalation decisions, or when building a tool that needs to watch the belt work in real time.
| name | decision-ladder |
| description | Ponytail-inspired YAGNI enforcement — climb the decision ladder before writing any code. stdlib → regex → existing module → new code. |
| version | 1.0.0 |
| trigger_keywords | ["decision ladder","ponytail","yagni","lazy dev","over-engineering"] |
Ponytail-inspired YAGNI enforcement for botte-secrete. Before writing ANY code, climb this ladder. Each rung that passes saves the cost of every rung above it.
from skills.decision_ladder.ladder import climb, audit_task_list
# Single task
result = climb("extract function names from a Python file")
# → LadderResult(rung="stdlib", solution="ast module (ast.parse/walk)", saved_lines=15)
# Audit a task list
report = audit_task_list([
"parse JSON config",
"design auth middleware",
"count word frequency in text",
"strip HTML tags from string",
"implement custom OR-Tools solver",
])
# → avoidable_pct: 80%, lines_saved: 85
Add to workflow-check as a pre-code hook. See hook.py.
Tracks: