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93pct

Autocomplete for agency next steps. Like Google autocomplete but for what to do next. Given any context, returns the top concrete viable actions ranked by ROI/effort ratio. Always outputs exactly one job ID to approve. Named after the 93% standard — every suggestion must clear that bar.

Informações da origem

Repositório
RabbitAI-Lab/rabbit-plugins-upstream
Última atividade na origem
26 de julho de 2026 às 20:50
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inglês
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SKILL.md
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name
93pct
description
Autocomplete for agency next steps. Like Google autocomplete but for what to do next. Given any context, returns the top concrete viable actions ranked by ROI/effort ratio. Always outputs exactly one job ID to approve. Named after the 93% standard — every suggestion must clear that bar.
version
1.0.0
author
Fiesta
tags
["planning","autocomplete","next-steps","agency","productivity"]
# 93% — Agency Autocomplete ## Doctrine > "Plan Shannon → Penn Station" Shannon is the currency of information (Claude Shannon, entropy theory). Penn Station is the commuter hub — where everything converges. The plan: route all decisions through Shannon (information value) to reach the station (execution). Low Shannon = entropy = noise = waste. High Shannon = signal = viable next step. ## Usage ``` python3 /root/.openclaw/workspace/skills/93pct/suggest.py # top 5 next steps now python3 /root/.openclaw/workspace/skills/93pct/suggest.py --stack # show approval stack python3 /root/.openclaw/workspace/skills/93pct/suggest.py --done <id> # mark completed ``` ## Output format Each suggestion includes: - Shannon score (0-10, information density) - Effort (minutes) - Blocker (what stops it) - Job ID to approve (if exec needed) - Exact command or action ## The 93% Bar A suggestion clears 93% if: - It is concrete (not "consider doing X") - It is viable right now (blocker is known and solvable) - It produces measurable output - It costs less than it returns
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