Oh My Pi is a coding agent with an integrated development environment, command-line interface, tools, language-server operations, and debugger operations. It is used by developers who want an AI coding assistant that can read and edit projects, work with models from multiple providers, and interact with development tools. The catalogue add-ons extend its agent workflows with skills, commands, agents, plugins, MCP, and instructions.
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 can1357/oh-my-pi --skill semantic-compressiongit clone --depth 1 https://github.com/can1357/oh-my-piWrote 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/can1357/oh-my-pi/semantic-compression)<a href="https://agentmods.dev/skills/can1357/oh-my-pi/semantic-compression"><img src="https://agentmods.dev/badge/skills/can1357/oh-my-pi/semantic-compression/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/can1357/oh-my-pi/semantic-compression"><img src="https://agentmods.dev/badge/skills/can1357/oh-my-pi/semantic-compression.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 | $0.00077 | $0.03402 |
| Opus 5.5 | $0.00031 | $0.01361 |
| Sonnet 5.5 | $0.00015 | $0.00680 |
| Haiku 4.5 | $0.00008 | $0.00340 |
Grade A, and why
semantic-compression 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic Compression
Compression is re-encoding, not word deletion. Filtering function words out of an English sentence leaves a damaged English sentence (System design: efficient process incoming data, multiple sources). Instead re-frame each claim in a register whose grammar is punctuation and layout — then the function words have no work left and drop out on their own.
Target texts are load-bearing: tool descriptions, system prompts, skills. A model executes them cold, with no author present to disambiguate. Compression that forces a guess is a bug, not a saving.
Procedure
- Density gate — check before touching anything. Two signals, in order: (a) are articles and copulas already near-absent? (b) compress one representative section and measure the token delta. Already in this register (house-style prompt, tool doc, spec) or delta under ~10%? STOP. Report that it is already dense and keep the original. Bullet length alone is a weak signal — API literals and enumerations inflate it. Measured on a real house-style tool prompt: 853 → 778 tokens (8.8%), while that pass silently dropped a
NEVER assume …rule, a throw condition, and afull-resdetail. On already-dense text the remaining words are the payload, and the expected saving is smaller than the expected loss. - Split the source into atomic claims: one definition, obligation, default, or fact each.
- Inventory the payload first, before deleting anything. List every load-bearing token: identifiers, error/exception names, throw conditions, defaults with their units, bounds, and every MUST/NEVER/PREFER line. Anything you then drop is a loss you declare deliberately rather than discover later.
- Cut what the model already knows. "JSON is a text format", "tests catch regressions" → delete. Keep only what is specific to this tool, repo, or domain.
- Cut restatements. Merge every duplicate of one rule into a single canonical line, placed where it is needed. Two statements of one rule with different scope are not duplicates.
- Frame each claim — definition · obligation · default · condition→consequence · enumeration · verdict. The frame picks the construction.
- Hoist repeated qualifiers into one scope line: three mentions of "relative to the repo root" →
All paths repo-relative.once, up top. - Re-encode, then run Verification.
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 · 143 lines · 77 tokens per session scan A 11e5ee26fccf
semantic-compression is a skill published in the GitHub repository can1357/oh-my-pi (35,689 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 3,402 once invoked, about $0.0003 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.
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