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.
npx skills add yogsoth-ai/north-star-crystallization --skill obstacle-analysisgit clone --depth 1 https://github.com/yogsoth-ai/north-star-crystallizationWrote 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.
[](https://agentmods.dev/skills/yogsoth-ai/north-star-crystallization/obstacle-analysis)<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>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.
| Model | Per session | Once 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 |
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.
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-acceptancewith unresolved obstacles: return topresent-candidates(direction-narrowing tactic)
Output (Tactic-Level Aggregation)
ObstacleReport { obstacles[], mitigations[], accepted: bool }
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.
- 8d ago First seen · 40 lines · 44 tokens per session scan A 77196fd5f85a
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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