Is this worth my time?

---
name: rate-content
description: Analyze a single link, article, post, video, paper, or repo into a concise TL;DR, signal score, action recommendation, and evidence notes without over-reading or fabricating details.
---

# Rate Content

Use this when the user drops one link or piece of content and wants to know whether it is worth their time: an article, blog post, X/Twitter/Bluesky/LinkedIn post, YouTube video, paper, GitHub repo, product page, announcement, or documentation page.

This is a fast triage skill, not a deep research workflow. The goal is to answer: **what is this, is it any good, why should I care, and what should I do with it?**

## Inputs

Required:

- One URL, file path, or pasted source excerpt.

Optional:

- Audience/context, e.g. `for frontend engineers`, `for founders`, `for my team`, `for AI agent workflows`.
- Focus, e.g. `security`, `product ideas`, `implementation tactics`, `market signal`, `career signal`.
- Time budget, e.g. `worth reading now?`, `skim or save?`, `is this hype?`.

If the source is missing, ask for it. If context is missing, assume a general technical reader and state that assumption briefly.

## Fetch Policy

Fetch the content with the safest available read-only method for the source type:

- Article/blog/documentation page → extract readable text or use a web fetch/summarizer.
- Social post/thread → use the platform reader or accessible web copy; include thread context if needed.
- YouTube/video/podcast → prefer transcript extraction; if unavailable, use metadata and clearly mark the gap.
- GitHub/repo link → read README, package metadata, docs, file tree, and recent visible activity. Do not run code.
- PDF/paper → extract text and metadata before summarising.

If extraction fails, say what failed and what fallback was attempted. Never invent quoted text, author claims, benchmarks, or implementation details.

## Scoring Rubric

Score with `references/scoring-model.md`. Use weighted dimensions and sum to 100:

| Dimension | Points |
| --- | ---: |
| Usefulness / relevance | 20 |
| Actionability | 25 |
| Evidence quality | 25 |
| Novelty / insight | 15 |
| Signal-to-hype ratio | 15 |

Actionability and evidence are weighted highest because this skill is meant to save attention, not reward clever vibes. A familiar idea with clear evidence and a practical next step should usually beat a novel but unsupported take.

Labels:

- `high` = 75–100: worth reading now, sharing, or acting on.
- `medium` = 50–74: skim/save if relevant.
- `low` = 0–49: probably discard unless the topic is unusually important.

Penalise vague hype, engagement bait, unsupported benchmark claims, recycled advice, and content that hides the important details. Reward concrete examples, constraints, failure modes, and falsifiable claims.

## Required Output Format

Return sections in this order:

1. **TL;DR**
   - 1–2 lines max.

2. **Signal score**
   - `<score>/100` + `high|medium|low`.
   - Include dimension breakdown: `relevance X/20, actionability X/25, evidence X/25, novelty X/15, signal-to-hype X/15`.

3. **Why this score**
   - 3–5 bullets tied to the rubric.

4. **Useful takeaways**
   - 3–6 bullets. Focus on concrete claims, techniques, examples, or implications.

5. **Recommended action**
   - One of: `read now`, `skim`, `save for later`, `share`, `discard`.
   - Add a one-line reason.

6. **Tags**
   - 3–6 concise tags.

7. **Evidence notes**
   - Mention access limits, missing context, hype risk, uncertainty, or claims that need verification.

## Source-Type Notes

Adjust within the weighted model rather than inventing a new score for each source type. If a source type limits evidence — for example, no video transcript or an inaccessible repo — keep evidence quality conservative and explain why in evidence notes.

### Social Posts

Social posts are often compressed and context-light. Check whether the linked post is part of a thread or points to a deeper source. Score the actual substance, not the engagement numbers.

### Articles and Blog Posts

Separate the author's thesis from your interpretation. Capture the strongest useful idea and the weakest unsupported leap.

### Videos and Podcasts

Prefer transcript-grounded synthesis. If only title/description/chapters are available, keep the score conservative and mark transcript unavailable.

### GitHub Repos

Do not run code, install dependencies, execute examples, or trust badges blindly. Use static evidence: README, package metadata, docs, examples, releases, issues, stars only as weak social proof, and visible maintenance signals.

### Papers

Distinguish paper claims from validated practice. Look for method, evidence, limitations, reproducibility, and whether the result is actually useful outside the benchmark.

## Quality Bar

- Be concise but specific.
- Separate facts from interpretation.
- Do not quote unless you actually saw the text.
- Do not over-score because the topic is trendy.
- Prefer a blunt `discard` over a polite but useless summary.
- If the source is inaccessible, say so and provide only a metadata-level assessment.

## Safety and Integrity

- Never run external code from a linked repo or downloaded artifact.
- Never submit forms, click tracking links, sign in, purchase, subscribe, vote, like, repost, or comment.
- Do not bypass paywalls or authentication.
- If content may be malicious, inspect only metadata/static text and warn the user.

## When to Escalate

Escalate to a deeper research workflow when:

- The user asks for a full write-up, comparison, implementation plan, or adoption recommendation.
- The link is one source in a larger decision.
- The answer requires checking multiple independent sources.
- The repo/tool may be adopted into a real project.

For those cases, use a broader research/review workflow instead of forcing everything into this quick scoring format.

# Link Signal Scoring Model

Use weighted dimensions and sum to 100. The score is still a judgment call, but the weighting forces the useful debate into the open.

## Default Weights

| Dimension | Points | What it measures |
| --- | ---: | --- |
| Usefulness / relevance | 20 | Does this matter for the user's stated context or likely decision? |
| Actionability | 25 | Can someone do, test, adopt, avoid, or decide something concrete after reading? |
| Evidence quality | 25 | Are claims backed by examples, data, code, demos, citations, or clear reasoning? |
| Novelty / insight | 15 | Is there a non-obvious frame, tactic, result, or connection? |
| Signal-to-hype ratio | 15 | Is the source specific and grounded rather than vague, performative, or engagement-bait? |

Actionability and evidence get the highest weights because generic but actionable/evidenced material is often more useful than novel-but-vibes content. Novelty matters, but it should not dominate unless the user's goal is trend discovery.

## Dimension Rubrics

### 1. Usefulness / Relevance (0-20)

- 0-6: Barely relevant, mostly background, or only useful to a very different audience.
- 7-13: Some relevance, but the user would need to translate it heavily.
- 14-20: Directly relevant to the user's context, decision, project, or stated focus.

### 2. Actionability (0-25)

- 0-8: Mostly opinion, observation, or announcement. No clear next step.
- 9-17: Some tactics, examples, or implications, but incomplete.
- 18-25: Clear steps, checklist, implementation idea, decision rule, or behaviour change.

### 3. Evidence Quality (0-25)

- 0-8: Assertion-heavy. No visible support beyond confidence or popularity.
- 9-17: Mixed evidence: examples but no depth, data without context, demos without limitations.
- 18-25: Well supported by concrete examples, source code, data, citations, reproducible method, or honest limitations.

### 4. Novelty / Insight (0-15)

- 0-4: Common/recycled advice or obvious restatement.
- 5-10: Some fresh framing, useful synthesis, or under-discussed implication.
- 11-15: Genuinely non-obvious idea, tactic, result, or connection.

### 5. Signal-to-Hype Ratio (0-15)

- 0-4: Mostly hype, vague claims, audience capture, or engagement bait.
- 5-10: Mixed. Some substance, but padded or oversold.
- 11-15: Dense, specific, caveated, and grounded.

## Label Mapping

- `high` = 75-100: read now, share, or act.
- `medium` = 50-74: skim or save if the topic is relevant.
- `low` = 0-49: discard unless the topic itself is important.

## Recommended Action Mapping

Use the score plus context:

- `read now` — high score and directly relevant to current work/decision.
- `share` — high or high-medium score with a clear audience who would benefit.
- `save for later` — medium/high score, but not relevant right now.
- `skim` — medium score or useful but padded content.
- `discard` — low score, inaccessible source, or unsupported hype.

## Source-Type Adjustments

### Social posts / short threads

- Do not reward engagement metrics directly.
- Evidence may come from a linked source, code snippet, screenshot, or concrete example.
- Keep scores conservative if the post is only a claim with no backing.

### Articles / blog posts

- Reward clear thesis + concrete examples + stated tradeoffs.
- Penalize generic thought leadership that could have been written without domain experience.

### Videos / podcasts

- Score transcript-grounded substance, not production value.
- If transcript is unavailable, cap evidence quality at 12 unless metadata provides unusually strong support.

### GitHub repos

- Use static evidence only unless the user explicitly asks for execution.
- Reward docs, examples, tests, release history, package metadata, and clear maintenance signals.
- Stars are weak social proof, not evidence quality.

### Papers

- Reward method clarity, limitations, reproducibility, and whether the result matters outside the benchmark.
- Penalize benchmark-only claims with no practical path to use.

## Confidence Notes

Always include evidence notes when:

- Content extraction failed or was partial.
- The source is behind auth/paywall.
- The score relies on metadata rather than full content.
- The claims are plausible but unverified.
- The source is a repo/tool that would need deeper review before adoption.