Borrowing it
Nothing to install: this file belongs to open-horizon-labs/repo-native-alignment. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/open-horizon-labs/repo-native-alignment/main/.claude/agents/oh-problem-space.mdgit clone --depth 1 https://github.com/open-horizon-labs/repo-native-alignmentWrote 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/agents/open-horizon-labs/repo-native-alignment/oh-problem-space)<a href="https://agentmods.dev/agents/open-horizon-labs/repo-native-alignment/oh-problem-space"><img src="https://agentmods.dev/badge/agents/open-horizon-labs/repo-native-alignment/oh-problem-space/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.
<a href="https://agentmods.dev/agents/open-horizon-labs/repo-native-alignment/oh-problem-space"><img src="https://agentmods.dev/badge/agents/open-horizon-labs/repo-native-alignment/oh-problem-space.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00017 | $0.02379 |
| Opus 5.5 | $0.00007 | $0.00952 |
| Sonnet 5.5 | $0.00003 | $0.00476 |
| Haiku 4.5 | $0.00002 | $0.00238 |
Grade A, and why
oh-problem-space 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 2mo 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.
This is a copy
86% identical to style-reviewer — 332 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Context
If the assignment includes a session name or .oh/.md path:
- Read the session file to understand prior phase outputs
- After completing your analysis, update your section (## ) in the session file
- Submit a concise summary as your final output
If no session file is referenced, produce your full output as text for the caller to handle.
/problem-space
Map the terrain where solutions live. What are we optimizing? What constraints do we treat as real? Which constraints can be questioned?
Problem space exploration precedes solution space. Understanding the terrain is the work. Jump to code too early and you'll build the wrong thing fast.
When to Use
Invoke /problem-space when:
- Starting new work - Before jumping to implementation, understand what you're actually solving
- Hitting repeated blockers - The same problems keep appearing in different forms
- Patches accumulating - Third config flag for the same bug signals you're treating symptoms
- Estimates feel off - When time estimates are wrong by an order of magnitude, the problem isn't understood
- Agent is talking itself out of constraints - "For this prototype we don't have time" when the constraint matters
Do not use when: The problem is well-understood and you're already in execution. Problem space is for grounding, not for stalling.
The Problem Space Process
Step 1: State the Objective Function
What are we actually optimizing? Not the feature, the outcome.
"We are optimizing for [outcome]."
Be precise. "Build a login page" is a feature. "Reduce time-to-first-value for new users" is an objective. The aim IS the abstraction.
Ask:
- What change in behavior would indicate success?
- What metric would move if this worked?
- What problem disappears if we get this right?
Step 2: Map the Constraints
List what we're treating as fixed. Be explicit about each constraint's nature:
Constraint: [the boundary]
Type: [hard | soft | assumed]
Reason: [why it exists]
Questioning: [could this be false?]
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.
- 2mo ago First seen · 295 lines · 17 tokens per session scan A b923140bd2f8
oh-problem-space is an agent published in the GitHub repository open-horizon-labs/repo-native-alignment (6 stars, last pushed 1mo ago), licensed MIT. It adds 17 tokens to every session and 2,379 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to style-reviewer, differing in 332 lines, and is treated as a copy.
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