An agent skill that stops AI models from drowning complex tasks in words — it thinks in compact meaning, not prose, and only talks normally when it needs to.
Who it's for: anyone building or running agents that handle multi-step planning, comparing options, or juggling lots of constraints.
Why it exists: natural language is verbose. When an agent restates requirements as prose over and over, it burns tokens, repeats itself, and drifts. This skill gives the model a compressed "mental workspace" — the same information, less overhead.
npx skills add CamjamPNG/skills
When active, the model:
- Extracts the real goal, entities, hard requirements, preferences, conditions, and uncertainty from the task
- Represents them compactly — e.g.
PRICE<=500
,RAM>=16GB
,BEST=B
- Reasons over that compressed state instead of restating prose
- Preserves specificity — never swaps a specific fact for a generic label
- Expands back into natural language when it talks to you
The point is not fewer words. The point is more meaning per word — a representation that stays compact, precise, and reversible, and that never loses information to sound shorter.
A user asks to compare three laptops. The model internally works with:
REQ: PRICE<=700USD, OS=WINDOWS, RAM>=16GB, STORAGE>=512GB
PREF: PERFORMANCE > BATTERY
VALID: A, B REJECT: C (RAM<16)
BEST: B
...then answers in plain English: "I'd pick B — it meets every requirement and has 32GB RAM and 1TB storage."
efficient-semantic-thinking/
└── SKILL.md