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 digital-stoic-org/agent-skills --skill frame-problemgit clone --depth 1 https://github.com/digital-stoic-org/agent-skillsWrote 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/digital-stoic-org/agent-skills/frame-problem)<a href="https://agentmods.dev/skills/digital-stoic-org/agent-skills/frame-problem"><img src="https://agentmods.dev/badge/skills/digital-stoic-org/agent-skills/frame-problem/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/skills/digital-stoic-org/agent-skills/frame-problem"><img src="https://agentmods.dev/badge/skills/digital-stoic-org/agent-skills/frame-problem.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 5 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
- medium Excessive Agency · line 51 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00066 | $0.01851 |
| Opus 5 | $0.00033 | $0.00925 |
| Sonnet 5 | $0.00013 | $0.00370 |
| Haiku 4.5 | $0.00007 | $0.00185 |
Grade A, and why
frame-problem 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.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frame
Sense-make → triangulate → decompose if needed → route. Domain determines agent pattern, not just skill.
Framing: $ARGUMENTS
⚠️ AskUserQuestion Guard
CRITICAL: After EVERY AskUserQuestion call, check if answers are empty/blank. Known Claude Code bug: outside Plan Mode, AskUserQuestion silently returns empty answers without showing UI.
If answers are empty: DO NOT proceed with assumptions. Instead:
- Output: "⚠️ Questions didn't display (known Claude Code bug outside Plan Mode)."
- Present the options as a numbered text list and ask user to reply with their choice number.
- WAIT for user reply before continuing.
0. Auto-classify (skip if no $ARGUMENTS)
Read $ARGUMENTS. Attempt domain classification using constraint language.
If confidence ≥80%: Propose — but ALWAYS run the Adjacent Domain Challenge before confirming:
🎯 Auto-classified: [Domain] (constraint: [type])
→ Verb: [probe|analyze|execute|act|decompose]
→ Suggested route: [skill chain]
⚖️ Adjacent challenge: What if this is actually [nearest domain]?
[1-2 sentence argument for why it could be the adjacent domain]
[Why the original classification still holds — or doesn't]
Confirm? [Yes / Re-classify manually]
LLM bias warning: You are systematically biased toward Complicated (you have "expert knowledge" for everything, so you see governing constraints everywhere). When auto-classifying as Complicated, actively look for signs it might be Complex: Would two experts disagree? Is there genuine novelty? Has this specific combination been tried before?
If confidence <80% or no $ARGUMENTS: Skip to Step 1 (triangulation).
1. Triangulate (3 tests)
Do NOT ask user to self-classify by constraint type — people systematically misclassify. Instead, ask 3 concrete questions they CAN answer accurately.
Question Refinement: If $ARGUMENTS is vague or broad, generate 2-3 clarifying sub-questions to sharpen the problem statement. Present them inline before proceeding.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 152 lines · 66 tokens per session scan A 7d398a56ecfe
frame-problem is a skill published in the GitHub repository digital-stoic-org/agent-skills (20 stars, last pushed 2d ago), licensed MIT. It adds 66 tokens to every session and 1,851 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 2026-08-30.
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