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 agentmods add skills/777genius/universal-agent-plugins/code-impact-analysisnpx skills add 777genius/universal-agent-plugins --skill code-impact-analysisgit clone --depth 1 https://github.com/777genius/universal-agent-pluginsWhat 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 | $0.00041 | $0.00314 |
| Opus 5 | $0.00020 | $0.00157 |
| Sonnet 5 | $0.00008 | $0.00063 |
| Haiku 4.5 | $0.00004 | $0.00031 |
Grade A, and why
code-impact-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Code Impact Analysis
Build a verified impact set before editing shared behavior or public contracts.
- Locate the definition with an LSP tool or
rg. - Find exact call sites and serialized field names with
rg. - Find type-aware references with Serena or the language server.
- Ask CGC for callers, callees, dependencies, and complexity when indexed.
- Use Semble to find conceptually similar patterns that exact search may miss.
- Read every high-risk caller and decide the minimum edit set.
- Identify focused tests, type checks, migrations, and user-visible verification.
Examples:
rg -n "symbolName\\(" .
uvx --from semble==0.5.4 semble search "similar usage of symbolName" .
cgc analyze callers symbolName --context "$(basename "$PWD")"
cgc analyze calls symbolName --context "$(basename "$PWD")"
When tools disagree, inspect the conflicting results. Generated code, dynamic dispatch, framework conventions, or an incomplete index can explain the gap. Never reduce the impact set solely because a graph or semantic search returned few results.
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.
- yesterday First seen · 33 lines · 41 tokens per session scan A 57324fc31989
code-impact-analysis is a skill published in the GitHub repository 777genius/universal-agent-plugins (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 41 tokens to every session and 314 once invoked, about $0.0002 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 2026-08-31.
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