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 jerseycheese/agent-skills --skill abstraction-findergit clone --depth 1 https://github.com/jerseycheese/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/jerseycheese/agent-skills/abstraction-finder)<a href="https://agentmods.dev/skills/jerseycheese/agent-skills/abstraction-finder"><img src="https://agentmods.dev/badge/skills/jerseycheese/agent-skills/abstraction-finder/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/jerseycheese/agent-skills/abstraction-finder"><img src="https://agentmods.dev/badge/skills/jerseycheese/agent-skills/abstraction-finder.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 | $0.00149 | $0.01541 |
| Opus 5 | $0.00075 | $0.00771 |
| Sonnet 5 | $0.00030 | $0.00308 |
| Haiku 4.5 | $0.00015 | $0.00154 |
Grade A, and why
abstraction-finder 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 7d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Abstraction Finder
Find repetition that has earned an abstraction, then propose the smallest general-purpose form of it. Scan the code (whole repo or a named area), cluster genuine duplicates, and for each cluster propose a shared helper/service/base class/template/util with the concrete call sites it replaces.
The point isn't to list every near-match — it's to surface a short set of extractions that remove
real duplication without over-abstracting. This is the counterweight to kiss: kiss strips a
change to its minimum and resists new abstractions; this skill argues for an abstraction, but only
once the duplication is real and repeated. Hold both — the target is the abstraction the code has
actually earned, not the one you can imagine needing.
Scope first
State what you're scanning before you scan — the whole repo, a directory, a domain, one feature — and list it so the user can confirm you're pointed at the right code. A whole-repo scan is noisy; a scoped scan is actionable. Ask for a scope if the user didn't give one.
Process
- Find repetition. Search for near-identical logic, duplicated markup, parallel utilities, and copy-paste clusters within the scope.
- Read the real occurrences. Surface similarity is not sameness — two functions can look alike
and differ in a way that matters. Open each candidate before calling it a duplicate. (This is
evidence-checkterritory: "these are duplicates" and "no existing helper covers this" are claims to confirm by reading, not assert from memory.) - Cluster and count. Group true duplicates and record every call site (
file:line). Count is the primary signal: three or more genuine occurrences is a candidate, two is a watch-item, one is not a duplicate. - Check the rule of three and what already exists. Before proposing anything, confirm the language/framework or an existing shared helper doesn't already cover it. Don't reinvent what the stack provides. Two occurrences is a coincidence; three is a pattern. The exception: if two known sites are already drifting (the same fix had to be applied in both, or they've started to disagree), unify early — drift is itself a reason.
- Propose the smallest form. For each qualifying cluster: its shape (helper / service / base class / template / util), where it lands, a concrete signature, and the call sites it replaces.
- Weigh the trade-off. Name the cost (indirection, coupling, a new thing to maintain) against the benefit (call sites removed, single source of truth). Not every duplicate is worth collapsing.
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.
- 7d ago First seen · 123 lines · 149 tokens per session scan A 940e59de8b06
abstraction-finder is a skill published in the GitHub repository jerseycheese/agent-skills (1 stars, last pushed 9d ago), licensed MIT. It adds 149 tokens to every session and 1,541 once invoked, about $0.0007 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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