How do I install the Rank By User Fit skill?

Run npx skillmds add lyndonkl/rank-by-user-fit in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Rank By User Fit safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Rank By User Fit?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Rank By User Fit free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Rank By User Fit?

lyndonkl (@lyndonkl) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Rank By User Fit by lyndonkl — Agent Skill

Name: Rank By User Fit
Rating: 4.2 (5 reviews)
Author: lyndonkl

Rank By User Fit

Scores and ranks substacker Trend Scout annotated candidates against voice-profile and goals, producing a top-10 keep list and an explicit drop list with reasons. Weighted-sum scoring across intuition-density fit, goal alignment, dedup penalty, source reliability, freshness. Produces the digest's keeps and drops sections. Use after cross-ref-topic-ledger. Trigger keywords — rank, fit score, user fit, keep list, drop list, signal weight.

lyndonkl f9d4eab 2.6 KB Updated Sep 9, 2026

File contents

Rank by User Fit

Workflow

Per candidate pool:
- [ ] Step 1: Score each on 5 dimensions (intuition_density_fit, goal_alignment, dedup_penalty, source_reliability, freshness)
- [ ] Step 2: Weighted sum → rank
- [ ] Step 3: Top items with score > threshold (default 30), max 10 → keep list
- [ ] Step 4: Remaining → drop list; per-drop one-line reason using worst-scoring dimension
- [ ] Step 5: Enforce minimum: ≥2-3 drops visible even in slow weeks (ranker transparency)

Scoring rubric

Dimension	Weight	Range
intuition_density_fit	3.0	high=10, medium=5, low=0
goal_alignment	2.0	full-match=10, partial=5, none=2, anti-aligned=-3
dedup_penalty	2.0	NEW=+5, OVERLAPS seed=+2, OVERLAPS draft=0, OVERLAPS published=-2 (unless reinforcement_angle strong)
source_reliability	1.0	essential=10, optional=6, aggregator=4
freshness	1.0	in-window=10, republished=5, older=2

Threshold for keep: score > 30.

Worked example

Candidate: Karpathy microgpt (teaches GPT internals in 200 lines, in window).

intuition_density_fit: 10 × 3 = 30 (high)
goal_alignment: 10 × 2 = 20 (matches "intuition-first" explicitly)
dedup_penalty: +5 × 2 = 10 (NEW)
source_reliability: 10 × 1 = 10 (essential)
freshness: 10 × 1 = 10 (in window)
Total: 80 → keep, high rank

Candidate: "OpenAI announces GPT-5.5" release post.

intuition_density_fit: 0 × 3 = 0
goal_alignment: 2 × 2 = 4
dedup_penalty: +5 × 2 = 10
source_reliability: 4 × 1 = 4
freshness: 10 × 1 = 10
Total: 28 → drop. Reason: capability announcement; no mechanism taught.

Guardrails

Never let freshness outweigh intuition-density. Freshness is a tiebreaker.
Never keep more than 10.
Never drop all items from a single essential source two weeks in a row without surfacing "this source may be stale" for update-watchlist.
Never drop an item solely for source weight 4 — good content from unknown sources matters.
Always produce drops. Zero drops in a week with >5 candidates = ranker failure.

---name: rank-by-user-fitdescription: Scores and ranks substacker Trend Scout annotated candidates against voice-profile and goals, producing a top-10 keep list and an explicit drop list with reasons. Weighted-sum scoring across intuition-density fit, goal alignment, dedup penalty, source reliability, freshness. Produces the digest's keeps and drops sections. Use after cross-ref-topic-ledger. Trigger keywords — rank, fit score, user fit, keep list, drop list, signal weight.--- # Rank by User Fit ## Workflow ``` Per candidate pool: - [ ] Step 1: Score each on 5 dimensions (intuition_density_fit, goal_alignment, dedup_penalty, source_reliability, freshness) - [ ] Step 2: Weighted sum → rank - [ ] Step 3: Top items with score > threshold (default 30), max 10 → keep list - [ ] Step 4: Remaining → drop list; per-drop one-line reason using worst-scoring dimension - [ ] Step 5: Enforce minimum: ≥2-3 drops visible even in slow weeks (ranker transparency) ``` ## Scoring rubric | Dimension | Weight | Range | |---|---|---| | intuition_density_fit | 3.0 | high=10, medium=5, low=0 | | goal_alignment | 2.0 | full-match=10, partial=5, none=2, anti-aligned=-3 | | dedup_penalty | 2.0 | NEW=+5, OVERLAPS seed=+2, OVERLAPS draft=0, OVERLAPS published=-2 (unless reinforcement_angle strong) | | source_reliability | 1.0 | essential=10, optional=6, aggregator=4 | | freshness | 1.0 | in-window=10, republished=5, older=2 | Threshold for keep: score > 30. ## Worked example Candidate: Karpathy microgpt (teaches GPT internals in 200 lines, in window). - intuition_density_fit: 10 × 3 = 30 (high) - goal_alignment: 10 × 2 = 20 (matches "intuition-first" explicitly) - dedup_penalty: +5 × 2 = 10 (NEW) - source_reliability: 10 × 1 = 10 (essential) - freshness: 10 × 1 = 10 (in window) - **Total: 80 → keep, high rank** Candidate: "OpenAI announces GPT-5.5" release post. - intuition_density_fit: 0 × 3 = 0 - goal_alignment: 2 × 2 = 4 - dedup_penalty: +5 × 2 = 10 - source_reliability: 4 × 1 = 4 - freshness: 10 × 1 = 10 - **Total: 28 → drop. Reason: capability announcement; no mechanism taught.** ## Guardrails 1. Never let freshness outweigh intuition-density. Freshness is a tiebreaker. 2. Never keep more than 10. 3. Never drop all items from a single essential source two weeks in a row without surfacing "this source may be stale" for `update-watchlist`. 4. Never drop an item solely for source weight 4 — good content from unknown sources matters. 5. Always produce drops. Zero drops in a week with >5 candidates = ranker failure.

Frequently asked questions