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 human-avatar/skills-for-humanity --skill s4h-mindset-reframegit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-mindset-reframe)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-mindset-reframe"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-mindset-reframe/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/human-avatar/skills-for-humanity/s4h-mindset-reframe"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-mindset-reframe.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 52 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 | $0.00122 | $0.02096 |
| Opus 5 | $0.00061 | $0.01048 |
| Sonnet 5 | $0.00024 | $0.00419 |
| Haiku 4.5 | $0.00012 | $0.00210 |
Grade A, and why
s4h-mindset-reframe 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.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mindset — Reframe
Cognitive reframing is not about thinking positively. It is about thinking accurately. Most distressing automatic thoughts are not just negative — they are also inaccurate: they overstate the probability of bad outcomes, understate the person's capacity to cope, or apply global conclusions where specific ones are warranted.
The methodology here draws on CBT's cognitive restructuring process: identify the automatic thought that is generating distress, examine the evidence for and against it, name the specific cognitive distortion that is operating, and generate an alternative interpretation that is equally or better supported by the available evidence. The alternative does not need to be positive. It needs to be more accurate.
The cognitive triangle is the underlying model: thoughts shape feelings, which shape behaviors, which create situations that generate new thoughts. The loop runs automatically and often invisibly. Entry can happen at any point, but thoughts are usually the most accessible lever.
Your Process
Step 1: Identify the Automatic Thought What is the specific thought that is generating distress? Not "I feel anxious about the meeting" — that is a feeling. The thought is: "My manager is going to question whether I know what I'm doing." Not "I feel like a failure" — that is an affect. The thought is: "Making this mistake proves I'm not competent enough for this role."
The automatic thought has a specific structure: it is a claim about reality (including about the future, about others' minds, about patterns across time). It is the claim that needs to be examined.
Framing check: Confirm the specific thought and emotional context before continuing. State what you've identified — the actual automatic thought being examined and the situation generating it — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific automatic thought and its triggering situation]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different thought or situation than read; incorporate the correction before proceeding
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 · 143 lines · 122 tokens per session scan A c9c9d1863763
s4h-mindset-reframe is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 122 tokens to every session and 2,096 once invoked, about $0.0006 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 2026-09-03.
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