blast-radius
Analyzes code change impact with risk scoring and affected-node mapping. Use before merging to understand what a change touches and what lacks test coverage.
What this skill does
# Blast Radius Analysis
Analyze the impact of current code changes using the
code knowledge graph.
## Prerequisites
This skill requires the **gauntlet** plugin for graph
data. Check if it's available:
```bash
GRAPH_QUERY=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1)
```
**If gauntlet is not installed** (GRAPH_QUERY is empty):
Fall back to a manual impact analysis using `git diff`
and `grep` to trace imports and call sites. Skip graph
steps and go directly to step 3 (manual mode).
**If gauntlet is installed but no graph.db exists**:
Tell the user: "Run `/gauntlet-graph build` first."
## Steps
1. **Show current changes**: Run `git diff --stat` to
show the user what files changed.
2. **Run impact analysis** (requires gauntlet):
```bash
python3 "$GRAPH_QUERY" \
--action impact --base-ref HEAD --depth 2
```
**Fallback tier 1 (sem available, no gauntlet)**:
Use sem for cross-file dependency tracing:
```bash
if command -v sem &>/dev/null; then
sem impact --json <changed-file>
fi
```
This traces real function-level dependencies instead
of filename matching. See `leyline:sem-integration`
for detection patterns.
**Fallback tier 2 (no sem, no gauntlet)**: Trace
callers of changed functions with rg (or grep):
```bash
# Prefer rg for speed; fall back to grep
if command -v rg &>/dev/null; then
git diff --name-only HEAD | while read f; do
stem="${f%.*}"; stem="${stem##*/}"
[ -z "$stem" ] && continue # skip dotfiles (.gitignore etc.)
rg -l "$stem" . 2>/dev/null
done | sort -u
else
git diff --name-only HEAD | while read f; do
stem="${f%.*}"; stem="${stem##*/}"
[ -z "$stem" ] && continue # skip dotfiles (.gitignore etc.)
grep -rl "$stem" . 2>/dev/null
done | sort -u
fi
```
Note: this searches all file types. For Python-only
projects, add `--type py` to `rg` or `--include="*.py"`
to `grep` to reduce false positives.
3. **Display results in priority order**:
Format the output as a table:
```
Risk | Node | File | Anchor | Reason
0.85 | auth.py::verify_token | auth.py:45 | `def verify_token(token):` | untested, security
0.62 | db.py::execute_query | db.py:112 | `cursor.execute(query, params)` | high fan-in
0.41 | api.py::handle_request | api.py:78 | `def handle_request(req):` | flow participant
```
The `Anchor` column is the verbatim source text at the cited line.
It lets a reviewer confirm the finding without re-running the tool.
4. **Highlight untested functions**: List any affected
functions that lack test coverage (no TESTED_BY edge).
5. **Show overall risk**: Display the overall risk level
(low/medium/high) based on the maximum risk score.
6. **Suggest actions**:
- For high-risk nodes: "Consider adding tests before
merging"
- For security-sensitive nodes: "Review authentication
and authorization logic carefully"
- For high-fan-in nodes: "Changes here affect many
callers; verify backward compatibility"
### Verify Findings Are Grounded (`blast-radius:findings-verified`)
Every finding must cite a real location and a verbatim anchor. Write
findings to `.review/findings.json` and confirm each citation resolves:
```bash
python plugins/imbue/scripts/citation_verifier.py \
--findings .review/findings.json --repo-root .
```
Drop or label `UNVERIFIED` any finding the verifier fails (exit `1`); only
verified findings enter the report. See `Skill(imbue:review-core)` Step 5
and `Skill(imbue:structured-output)` for the schema.
## Exit Criteria
- [ ] Results table lists every affected node with a `File` (file:line)
and verbatim `Anchor` column.
- [ ] Overall risk level (low/medium/high) is displayed based on the
maximum risk score.
- [ ] Every reported finding carries a `Location` + verbatim `Anchor`
confirmed by `citation_verifier.py` (exit `0`), or unverified
findings were dropped or labeled `UNVERIFIED`.
## Risk Scoring Model
Five weighted factors (sum capped at 1.0):
| Factor | Weight | Meaning |
|--------|--------|---------|
| Test gap | 0.30 | No test coverage |
| Security | 0.20 | Auth/crypto/SQL keywords |
| Flow participation | 0.25 | Part of execution flows |
| Cross-community | 0.15 | Called from other modules |
| Caller count | 0.10 | High fan-in function |
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.