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 genai-io/san --skill simplifygit clone --depth 1 https://github.com/genai-io/sanWrote 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/genai-io/san/simplify)<a href="https://agentmods.dev/skills/genai-io/san/simplify"><img src="https://agentmods.dev/badge/skills/genai-io/san/simplify/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/genai-io/san/simplify"><img src="https://agentmods.dev/badge/skills/genai-io/san/simplify.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 | $0.00094 | $0.00622 |
| Opus 5.5 | $0.00038 | $0.00249 |
| Sonnet 5.5 | $0.00019 | $0.00124 |
| Haiku 4.5 | $0.00009 | $0.00062 |
Grade A, and why
simplify 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Simplify: quality cleanup on the changed code
Review the changed code for cleanups it would benefit from, then apply them. This
is the quality pass — reuse, hacky patterns, wasted work. It is not the bug
pass (code-review) and not a requested restructuring (refactor); stay in its
lane and don't report correctness bugs or propose structural rewrites here.
Phase 1: Identify changes
Run git diff (or git diff HEAD when changes are staged). With no git changes,
review the most recently modified files the user named or that you edited earlier
in this conversation.
Phase 2: Review across three lenses (in parallel)
Use the Agent tool to launch three reviewers concurrently in one message (foreground). Give each the full diff. Each returns a short list of concrete, located findings.
- Reuse. New code that duplicates an existing helper or utility; hand-rolled logic (string munging, path handling, env checks, type guards) that the codebase already has a function for. Point to the existing one to use instead.
- Quality. Redundant state that could be derived; parameter sprawl instead of restructuring; near-duplicate blocks that should share an abstraction; leaky abstractions; stringly-typed code where a constant/enum exists; needless wrapper layers; conditionals nested 3+ deep (flatten with early returns or a lookup); comments that narrate WHAT instead of non-obvious WHY.
- Efficiency. Redundant computation or repeated reads; independent work run sequentially that could be parallel; new blocking work on a startup or per-request hot path; unconditional no-op updates in loops/handlers (guard with change detection); pre-checking existence before operating (TOCTOU); unbounded growth or leaked resources; reading/loading far more than needed.
Phase 3: Apply the cleanups
Aggregate the findings and fix each directly. Keep each fix minimal and matched to the file's existing style. If a finding is a false positive or not worth it, skip it — don't argue with it. When done, briefly summarize what you cleaned up, or confirm the code was already clean.
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 · 59 lines · 94 tokens per session scan A 419f5c41160c
simplify is a skill published in the GitHub repository genai-io/san (78 stars, last pushed today), licensed Apache-2.0. It adds 94 tokens to every session and 622 once invoked, about $0.0004 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.
Other skills, from other repositories
code-review
Perform thorough code reviews with security, performance, and maintainability analysis. Use when user asks to review code, check for bugs, or audit a codebase.
review-this-branch
Run the nohuman review gate (fresh-session adversarial reviewer + tamper guard) over the current branch or a GitHub pull request, with no server, no database, and no onboarding, and relay the pass/fail checklist.
review-harness
Use when reviewing implementation work that ran through a /tmp Pi task harness. Checks contract drift, unsafe shortcuts, validation evidence, and status honesty.
scrutiny-validator
Adversarial scrutiny procedure for engineering validation assignments. Runs hard-gate commands, reviews the current implementation and evidence integrity against assigned contracts, can use feature-reviewer lanes, and returns per-target verdicts.
ahk-review
Preview a code review against ticket/objective alignment, with deep semantic (name-vs-behavior) analysis. No tasks created, no harness tracking.
pi-parity-review
Adversarially verify that a ported change is faithful to the original pi implementation (TS source + published npm build). Use after porting upstream pi changes, or standalone on any area of this repo ("is X faithful to pi?").