Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/oaustegard/claude-skills/assessing-impact)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/assessing-impact"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/assessing-impact/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/oaustegard/claude-skills/assessing-impact"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/assessing-impact.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 | $0.00148 | $0.01368 |
| Opus 5 | $0.00074 | $0.00684 |
| Sonnet 5 | $0.00030 | $0.00274 |
| Haiku 4.5 | $0.00015 | $0.00137 |
Grade A, and why
assessing-impact 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 11d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
| Flag | Default | Purpose |
|---|---|---|
--features PATH |
_FEATURES.md |
Root _FEATURES.md — when present, refs get clustered by feature in addition to by package. |
--skip DIRS |
(defaults from tree-sitting) | Extra comma-separated dirs to skip. |
--limit-per-name N |
500 | Cap refs per symbol name. Bump if you suspect truncation. |
--json |
off | Emit JSON instead of markdown — for downstream tooling. |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 148 lines · 148 tokens per session scan A 048bf626f8ac
assessing-impact is a skill published in the GitHub repository oaustegard/claude-skills (148 stars, last pushed yesterday), licensed MIT. It adds 148 tokens to every session and 1,368 once invoked, about $0.0007 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 2026-08-30.
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