obstacle-analysis

obstacle-analysis is a skill for Claude Code, Codex from yogsoth-ai/north-star-crystallization. It costs 44 tokens per session (371 once invoked), scanned A, original, Apache-2.0.

A process for finding what may prevent someone from pursuing a chosen research direction, judging how serious each problem is, and suggesting evidence-supported ways to address it.

In plain words
What is it for?
It helps identify obstacles, assess their severity, propose mitigations, and ask the user whether to accept them.
Why use it?
It makes practical barriers visible before the user commits to a direction.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit It helps identify obstacles, assess their severity, propose mitigations, and ask the user whether to accept them.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/yogsoth-ai/north-star-crystallization/obstacle-analysis.svg)](https://agentmods.dev/skills/yogsoth-ai/north-star-crystallization/obstacle-analysis)
Your own site
<a href="https://agentmods.dev/skills/yogsoth-ai/north-star-crystallization/obstacle-analysis"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/north-star-crystallization/obstacle-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 371 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 $0.00044 $0.00371
Opus 5 $0.00022 $0.00186
Sonnet 5 $0.00009 $0.00074
Haiku 4.5 $0.00004 $0.00037

Measured 8d ago against content hash 77196fd5f85a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

obstacle-analysis 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 8d 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/obstacle-analysis/SKILL.md · 40 lines

What it actually says

Obstacle Analysis

Identify barriers, assess severity, propose mitigations, get acceptance.

Available SOPs

SOP Purpose Execution
identify-obstacles Identify obstacles from ActorProfile + chosen direction subagent (search optional)
assess-obstacle-severity Rate severity of each obstacle subagent (search optional)
propose-mitigations Propose evidence-backed mitigations subagent (search required)
ask-obstacle-acceptance Present obstacles + mitigations, get user decision dialogue (search optional)

Search Tools Available (for all SOPs)

  • web-search (web-browsing): Quick web scanning, snippets
  • web-research (web-browsing): Full page reading + analysis
  • literature-overview (literature-engine): Paper landscape scan
  • literature-search (literature-engine): Medium-depth paper search (AI summaries)
  • literature-research (literature-engine): Deep paper reading (raw full text + PDF queries)

Methodology Guidance

  • SOPs can iterate within this tactic (re-assess after new information)
  • You decide whether additional search is needed to evaluate obstacles

Hard Constraint

  • Maximum 2 rounds of the full identify → assess → propose → ask cycle
  • After 2 rounds of ask-obstacle-acceptance with unresolved obstacles: return to present-candidates (direction-narrowing tactic)

Output (Tactic-Level Aggregation)

ObstacleReport { obstacles[], mitigations[], accepted: bool }

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. 8d ago First seen · 40 lines · 44 tokens per session scan A 77196fd5f85a

Subscribe to this mod's changes

obstacle-analysis 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 44 tokens to every session and 371 once invoked, about $0.0002 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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