Borrowing it
Nothing to install: this file belongs to anortham/julie. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/anortham/julie/main/.claude/skills/impact-analysis/SKILL.mdgit clone --depth 1 https://github.com/anortham/julieWrote 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/anortham/julie/impact-analysis)<a href="https://agentmods.dev/skills/anortham/julie/impact-analysis"><img src="https://agentmods.dev/badge/skills/anortham/julie/impact-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/anortham/julie/impact-analysis"><img src="https://agentmods.dev/badge/skills/anortham/julie/impact-analysis.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 | $0.00044 | $0.01431 |
| Opus 5.5 | $0.00018 | $0.00572 |
| Sonnet 5.5 | $0.00009 | $0.00286 |
| Haiku 4.5 | $0.00004 | $0.00143 |
Grade A, and why
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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- impact-analysis — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Impact Analysis
Analyze the impact of changing a symbol by finding all references and assessing risk. Use this BEFORE modifying widely-used symbols.
Process
Step 1: Resolve the change target
If the user gives you a symbol name, resolve the definition first so you know which file or symbol they mean:
fast_search(query="<symbol_name>", workspace="<workspace_id>")
deep_dive(symbol="<symbol_name>", context_file="<partial_file_path>", depth="overview", workspace="<workspace_id>")
Use context_file when the name is ambiguous. If the target is described conceptually rather than named exactly, try fast_search(query="<concept>", backend="semantic", workspace="<workspace_id>") or backend="hybrid" to find candidate symbols. Semantic/hybrid fast_search results are symbol-only; use explicit lexical for file paths and pure lexical comparison. Default fast_search may show labeled semantic fallback candidates only after an identifier-like unscoped lexical zero-hit. blast_radius(symbol_ids=[...], workspace="<workspace_id>") is the tightest seed mode, but only use it when another Julie result already gave you concrete symbol IDs. If all you have is a definition file, use file_paths=[...].
Step 2: One-shot impact via blast_radius
blast_radius(file_paths=["<definition_file>"], max_depth=2, include_tests=true, workspace="<workspace_id>")
blast_radius is the primary entry point for impact analysis. One call returns ranked impacted symbols with why-reasons and likely tests. It walks the reference graph deterministically, so you don't have to chain get_context → fast_refs → deep_dive to build the same picture.
You can seed it three ways:
file_paths=["src/foo.rs"]— default when you know the changed file but not a symbol IDsymbol_ids=["<id>"]— tighter impact when another Julie result already gave you concrete symbol IDs
If the impact list is large, the output ends with Output truncated at <N> results; narrow the query or pass a smaller limit. See Step 4.
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 Changed · -17 lines 4b1f043d6295
- 2mo ago First seen · 147 lines · 44 tokens per session scan A d5aa5cd159dc
impact-analysis is a skill published in the GitHub repository anortham/julie (6 stars, last pushed 27d ago), licensed MIT. It adds 44 tokens to every session and 1,431 once invoked, about $0.0002 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 2026-08-31.
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