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 lexfrei/ccc --skill pore-analysisgit clone --depth 1 https://github.com/lexfrei/cccWrote 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/lexfrei/ccc/pore-analysis)<a href="https://agentmods.dev/skills/lexfrei/ccc/pore-analysis"><img src="https://agentmods.dev/badge/skills/lexfrei/ccc/pore-analysis/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/lexfrei/ccc/pore-analysis"><img src="https://agentmods.dev/badge/skills/lexfrei/ccc/pore-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00124 | $0.01709 |
| Opus 5.5 | $0.00050 | $0.00684 |
| Sonnet 5.5 | $0.00025 | $0.00342 |
| Haiku 4.5 | $0.00012 | $0.00171 |
Grade A, and why
pore-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 1mo 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pore-analysis
Quantify a porous film from one top-view SEM micrograph: porosity, pore diameter and shape, interpore distance, wall thickness, lattice ordering, and — for anodic aluminium oxide — the inferred anodizing voltage.
Design: vision decides, Python computes
The work splits in two, deliberately. You do everything that varies between instruments and cannot be hardcoded: look at the image, find the scale bar and measure its pixel length, read its nm value, decide what to crop out (info panel, label, debris), judge pore contrast polarity, and sanity-check the result with your eyes. The Python core (scripts/pore_analysis.py) does only the deterministic maths you cannot eyeball: segment with the parameters you give it, then compute porosity, per-pore size/shape over hundreds of pores, lattice period, and ordering (psi6, Voronoi, radial g(r) and orientational correlation length), and raise a physics-based trust check.
This split is the point. Do not look for an autodetect flag — there isn't one, on purpose. The next image may come from a different microscope with the scale bar elsewhere, a panel on a different edge, bright-on-dark pores, or a different label format. Your eyes adapt to that; a hardcoded detector would not.
When to invoke
Invoke when the user provides a top-view SEM image of a porous/membrane structure and wants any of: porosity, pore size/diameter, pore shape, interpore distance, wall thickness, lattice ordering/regularity, or AAO process inference. Trigger phrases include "анализ пор", "пористость", "размер пор", "pore analysis", "AAO", "анодный оксид", "porous membrane SEM".
Do not invoke for cross-section/tilted SEM where pores are seen from the side (this core assumes top-view), non-porous imagery, or generic image editing.
Dependencies
pip install numpy scipy scikit-image matplotlib. Reuse the user's venv if there is one; otherwise create one. matplotlib is optional — without it, figures are skipped but the report and CSV are still produced.
What ships with it
3 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.
- 1mo ago First seen · 83 lines · 124 tokens per session scan A 3201f354312c
pore-analysis is a skill published in the GitHub repository lexfrei/ccc (11 stars, last pushed 14d ago), licensed BSD-3-Clause. It adds 124 tokens to every session and 1,709 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 2026-09-04.
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