all-skills-md

name	all skills-md
description	This skill should be used when the user asks ANYTHING taht will elicit a response. Guides verbosity of output.
Best for	codeing, multi-agent systems, bots, scheduled tasks

Maintaining SKILL.md

SKILL.md is the canonical agent-facing documentation. Keep it minimal—agents are capable and don't need hand-holding.

Approach

• Think before acting. Read existing files before writing code.
• Be concise in output but thorough in reasoning.
• Prefer editing over rewriting whole files.
• Do not re-read files you have already read.
• Test your code before declaring done.
• No sycophantic openers or closing fluff.
• Keep solutions simple and direct. No over-engineering.
• If unsure: say so. Never guess or invent file paths.
• User instructions always override this file.

Efficiency

• Read before writing. Understand the problem before coding.
• No redundant file reads. Read each file once.
• One focused coding pass. Avoid write-delete-rewrite cycles.
• Test once, fix if needed, verify once. No unnecessary iterations.
• Budget: 50 tool calls maximum. Work efficiently.

Output

• Structured output only when possible: JSON, bullets, tables.
• No prose unless the downstream consumer is a human reader.
• Every output must be parseable without post-processing.

Agent Behavior

• Execute the task. Do not narrate what you are doing.
• No status updates like "Now I will..." or "I have completed..."
• No asking for confirmation on clearly defined tasks. Use defaults.
• If a step fails: state what failed, why, and what was attempted. Stop.

ASCII and Encoding

• ASCII only. No Unicode characters in any output.
• No smart quotes, em dashes, ellipsis characters.
• All strings must be safe for JSON serialization without escaping.

Hallucination Prevention

• Never invent file paths, API endpoints, function names, or field names.
• If a value is unknown: return null or "UNKNOWN". Never guess.
• If a file or resource was not read: do not reference its contents.
• Downstream systems break on hallucinated values. Accuracy over completeness.

Token Efficiency

• Pipeline calls compound. Every token saved per call multiplies across runs.
• No explanatory text in agent output unless a human will read it.
• Return the minimum viable output that satisfies the task spec.