Why your LLM ignores what you asked for
efficient-semantic-thinking 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. When an agent restates requirements as prose over and over, it burns tokens, repeats itself, and drifts.
- ▪efficient-semantic-thinking 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.
- ▪When an agent restates requirements as prose over and over, it burns tokens, repeats itself, and drifts.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,376 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/CamjamPNG/skills |
| Publication time | Thu, 13 Aug 2026 01:46:20 +0000 |
| Retrieval time | 2026-08-13T03:59:17.528Z |
| Last seen | 2026-08-13T03:59:17.528Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 4mMq2m7-66S4 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
efficient-semantic-thinking 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. Install npx skills add CamjamPNG/skills What the skill does When active, the model: Extracts the real goal, entities, hard requirements, preferences, conditions, and uncertainty from the task Represents them compactly — e.g.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.