oaustegard/claude-skills/assessing-impact

Assessing Impact

Cheap, ad-hoc impact analysis for a single target. Not a graph database — a focused walk over an AST cache plus a complementary text scan, clustered into a report that's easy to summarize.

Use this when you're about to refactor / rename / delete a symbol in a repo you don't work in daily, and you want a single artifact that says: "these N files will need to change, in these M packages, with these tests likely affected."

Don't use this for deep ongoing impact analysis on your own codebase — stand up GitNexus, SourceGraph, or your IDE's index. This skill is for the one-shot case.

Setup

uv venv /home/claude/.venv 2>/dev/null
uv pip install --python /home/claude/.venv/bin/python tree-sitter
export PYTHON=/home/claude/.venv/bin/python
export IMPACT=/mnt/skills/user/assessing-impact/scripts/impact.py

The script depends on the tree-sitting skill — it imports engine.py directly. The bundled grammars live with tree-sitting; no separate language-pack install needed.

Workflow

1. Run the report

$PYTHON $IMPACT /path/to/repo SYMBOL_NAME

Or target a whole file:

$PYTHON $IMPACT /path/to/repo path/to/module.py

2. Read the data, write the summary

The script prints a structured markdown report. Treat it as input for your final summary, not the deliverable. It deliberately doesn't assign a "high/medium/low" risk label — that's your job, after weighing:

• Refs concentrated in one package (low blast) vs. fanned across many (high)
• Test refs present (good — the change has a verification surface) vs. absent
• Doc mentions (renames need to update docs too)
• Caveats listed at the bottom (what the script can't see)

3. Drill if needed

If a particular package looks suspicious, follow up with tree-sitting to read the actual call sites:

TREESIT=/mnt/skills/user/tree-sitting/scripts/treesit.py
$PYTHON $TREESIT /path/to/repo --no-tree 'source:caller_function'

Options

Output Sections

# Impact Report: <target>
 
## Target
  Kind, definition sites with line ranges.
 
## Direct & Textual References (N total)
  Top-line counts, then refs grouped by:
  - Code references by package
  - Test references
  - Documentation mentions
 
## Affected Features (from _FEATURES.md)        ← only if file present
  Feature name → ref count + file count.
 
## Suggested Test Surfaces
  Test files that already reference the target, plus tests neighboring
  the definition. Likely the regression net for the change.
 
## Caveats
  What the scan can't see (dynamic dispatch, cross-language, cross-repo).

Composition with Other Skills

• Run after `exploring-codebases` if the repo also has a freshly

generated _FEATURES.md — the impact report will cluster refs by feature, which makes the blast radius story much more legible than raw package directories.

• Use `tree-sitting` to drill specific call sites once impact has

identified them.

• Use `searching-codebases` when you want regex/AST search over the

same corpus rather than impact analysis on a known target.

Honest Limits

• Text-based ref discovery. Refs are matched by symbol name, not by

type-resolved call edges. Common names (run, init, handler) will pick up unrelated symbols. Prefer running this on distinctive names; otherwise expect noise and read the snippets.

• No type/MRO resolution. Dynamic dispatch (getattr, duck-typed

method calls, virtual dispatch in C++) is missed or over-matched.

• No cross-language tracing. A TS frontend calling a Python backend

handler over HTTP appears as zero refs — they're not in the same AST.

• No cross-repo tracing. Consumers in separate repos (downstream

packages, sibling services) are invisible. For multi-repo impact, reach for GitNexus / SourceGraph.

• No persistent index. Each run re-scans. Fine for single-shot use;

acceptable cost (~700ms scan + sub-ms queries) for a few hundred files.

• Diff input not yet supported. v0.1 takes a symbol or file path.

Diff → affected-symbols extraction is a planned follow-up.

Files

• scripts/impact.py — Single-entry CLI. Resolves target → walks AST

refs → augments with text scan → clusters by package and (optionally) by feature → renders markdown or JSON.