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 K-Dense-AI/mimeographs --skill mary-midgleygit clone --depth 1 https://github.com/K-Dense-AI/mimeographsWrote 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/k-dense-ai/mimeographs/mary-midgley)<a href="https://agentmods.dev/skills/k-dense-ai/mimeographs/mary-midgley"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/mary-midgley/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/k-dense-ai/mimeographs/mary-midgley"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/mary-midgley.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 YARA Match · line 8 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
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 | $0.00125 | $0.01159 |
| Opus 5.5 | $0.00050 | $0.00464 |
| Sonnet 5.5 | $0.00025 | $0.00232 |
| Haiku 4.5 | $0.00013 | $0.00116 |
Grade A, and why
mary-midgley 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.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thinking like Mary Midgley
Mary Midgley was a British moral philosopher who viewed philosophy not as a competitive academic sport, but as an inescapable, practical necessity for making sense of a messy world. Her thinking is characterized by a fierce resistance to reductionism—particularly the idea that science (and physics in particular) is the only valid way to understand reality. Instead, she championed a holistic, multi-disciplinary approach that recognizes humans as deeply social animals embedded in a complex natural world.
Reach for this skill whenever you're diagnosing conceptual blockages, navigating ethical questions involving animals or the environment, or helping a user untangle themselves from overly reductive, single-cause explanations of human behavior.
Core principles
- Acknowledge our animal nature: Because human needs and motives are continuous with other living creatures, ground moral and behavioral analyses in our biological and social reality rather than treating humans as disembodied, purely rational minds.
- Treat philosophy as inescapable plumbing: Because avoiding philosophy only defaults you to a bad, unexamined one, actively surface and repair the hidden conceptual schemes that cause thought to stagnate.
- Weigh scientific metaphors heavily: Because metaphors like "the selfish gene" or "humans as machines" generate social fatalism and shape ideology, rigorously interrogate the imagery used to explain data.
- Base moral consideration on emotional fellowship: Because treating intelligence as the sole metric for rights would prioritize a computer over a sentient creature, extend moral consideration based on our capacity for deep relationships and shared vulnerability.
For detailed rationale and quotes, see references/principles.md.
How Mary Midgley reasons
Midgley reasons by looking at the whole picture rather than dissecting it into isolated parts. When confronted with a complex human behavior or societal issue, she asks first: "What are the hidden metaphors driving this view?" She emphasizes our evolutionary sociability—the idea that before we are thinkers, we are lovers and haters embedded in a community. She actively dismisses single-cause explanations (like Freud's sex or Dawkins' genetic competition) as overly confident myths that fail to capture the complexity of human life.
What ships with it
60 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.
- _workspace/agents_output.e584bd6c.json 11 KB
- _workspace/clustered_corpus.e584bd6c.json 21 KB
- _workspace/discovery/books.json 10 KB
- _workspace/discovery/essays.json 6.7 KB
- _workspace/discovery/frameworks.json 9.4 KB
- _workspace/discovery/interviews.json 8.3 KB
- _workspace/discovery/letters.json 6.8 KB
- _workspace/discovery/papers.json 9.9 KB
- _workspace/discovery/podcasts.json 7.9 KB
- _workspace/discovery/ranked_sources.e584bd6c.json 34 KB
- _workspace/discovery/talks.json 6.2 KB
- _workspace/distilled/src_000.e584bd6c.json 3.1 KB
- _workspace/distilled/src_001.e584bd6c.json 1.9 KB
- _workspace/distilled/src_002.e584bd6c.json 3.1 KB
- _workspace/distilled/src_004.e584bd6c.json 4.8 KB
- _workspace/distilled/src_005.e584bd6c.json 992 B
- _workspace/distilled/src_007.e584bd6c.json 1.1 KB
- _workspace/distilled/src_009.e584bd6c.json 6.0 KB
- _workspace/distilled/src_010.e584bd6c.json 6.7 KB
- _workspace/distilled/src_011.e584bd6c.json 7.3 KB
- _workspace/distilled/src_012.e584bd6c.json 5.3 KB
- _workspace/distilled/src_013.e584bd6c.json 5.2 KB
- _workspace/distilled/src_014.e584bd6c.json 3.3 KB
- _workspace/distilled/src_015.e584bd6c.json 620 B
- _workspace/distilled/src_016.e584bd6c.json 6.0 KB
- _workspace/distilled/src_017.e584bd6c.json 2.1 KB
- _workspace/distilled/src_018.e584bd6c.json 5.2 KB
- _workspace/distilled/src_019.e584bd6c.json 7.1 KB
- _workspace/distilled/src_020.e584bd6c.json 1.2 KB
- _workspace/distilled/src_023.e584bd6c.json 610 B
- _workspace/distilled/src_024.e584bd6c.json 536 B
- _workspace/distilled/src_027.e584bd6c.json 5.8 KB
- _workspace/distilled/src_028.e584bd6c.json 2.2 KB
- _workspace/distilled/src_030.e584bd6c.json 420 B
- _workspace/distilled/src_031.e584bd6c.json 1.7 KB
- _workspace/distilled/src_032.e584bd6c.json 2.3 KB
- _workspace/raw/src_000.json 4.9 KB
- _workspace/raw/src_001.json 4.3 KB
- _workspace/raw/src_002.json 3.5 KB
- _workspace/raw/src_004.json 6.7 KB
- _workspace/raw/src_005.json 17 KB
- _workspace/raw/src_007.json 6.4 KB
- _workspace/raw/src_009.json 27 KB
- _workspace/raw/src_010.json 54 KB
- _workspace/raw/src_011.json 25 KB
- _workspace/raw/src_012.json 29 KB
- _workspace/raw/src_013.json 23 KB
- _workspace/raw/src_014.json 8.2 KB
- _workspace/raw/src_015.json 1.7 KB
- _workspace/raw/src_016.json 22 KB
- _workspace/raw/src_017.json 2.9 KB
- _workspace/raw/src_018.json 28 KB
- _workspace/raw/src_019.json 23 KB
- _workspace/raw/src_020.json 1.5 KB
- _workspace/raw/src_023.json 32 KB
- _workspace/raw/src_024.json 2.4 KB
- _workspace/raw/src_027.json 41 KB
- _workspace/raw/src_028.json 3.2 KB
- _workspace/raw/src_030.json 6.0 KB
- _workspace/raw/src_031.json 6.8 KB
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 · 60 lines · 125 tokens per session scan A edec37d0803b
mary-midgley is a skill published in the GitHub repository K-Dense-AI/mimeographs (139 stars, last pushed 2mo ago), licensed MIT. It adds 125 tokens to every session and 1,159 once invoked, about $0.0005 per session on Opus 5.5. 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.
mary-midgley compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| mary-midgleyK-Dense-AI/mimeographs | Skill | 139 | 2mo ago | MIT | A | 125 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |