present-candidates

present-candidates is a skill for Claude Code, Codex from yogsoth-ai/north-star-crystallization. It costs 64 tokens per session (277 once invoked), scanned A, original, Apache-2.0.

A research workflow that identifies sub-directions within a chosen field, compares them, and presents ranked options. It can start broad or focus on specific research problems.

In plain words
What is it for?
Use it to evaluate research areas, compare activity and open gaps, and choose a direction for further investigation.
Why use it?
It helps turn a general interest into a smaller set of research directions that match the user’s skills and possible gaps in the field.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to evaluate research areas, compare activity and open gaps, and choose a direction for further investigation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yogsoth-ai/north-star-crystallization/present-candidates
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add yogsoth-ai/north-star-crystallization --skill present-candidates
Clone the repo
git clone --depth 1 https://github.com/yogsoth-ai/north-star-crystallization

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for present-candidates

README.md
[![agentmods](https://agentmods.dev/badge/skills/yogsoth-ai/north-star-crystallization/present-candidates/github.svg)](https://agentmods.dev/skills/yogsoth-ai/north-star-crystallization/present-candidates)
Your own site
<a href="https://agentmods.dev/skills/yogsoth-ai/north-star-crystallization/present-candidates"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/north-star-crystallization/present-candidates/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for present-candidates

Your own site · 80×15
<a href="https://agentmods.dev/skills/yogsoth-ai/north-star-crystallization/present-candidates"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/north-star-crystallization/present-candidates.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 277 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.00277
Opus 5 $0.00032 $0.00138
Sonnet 5 $0.00013 $0.00055
Haiku 4.5 $0.00006 $0.00028

Measured 9d ago against content hash ebd525b1b0a2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

present-candidates scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/present-candidates/SKILL.md · 35 lines

What it actually says

Present Candidates

Analyze + present sub-directions as ranked candidates.

Execution

Dialogue — inline, CC does the analysis and presents directly.

What to Do

  1. From broad-paper-search and deep-web-search results, identify distinct sub-directions within the chosen field
  2. For each sub-direction, assess: current activity level, gap size, skill fit with ActorProfile
  3. Rank and present to user

Depth Scales by Start Mode

  • cold-start: "Here are the broad sub-directions you could pursue in [field]"
  • warm-start: "Within [direction], here are the specific sub-problems and research tracks"
  • hot-start: "For [specific topic], here are the granular knowledge points and technical details to consider"

What to Ask (one at a time)

  • Which of these excites you most?
  • Where do you think you could make a unique contribution?

Output

RankedCandidates[] + user's selection.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 35 lines · 0 tokens per session scan A ebd525b1b0a2

Subscribe to this mod's changes

present-candidates is a skill published in the GitHub repository yogsoth-ai/north-star-crystallization (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 277 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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