Semgrep

Trail of BitsOfficialPublished by Trail of Bits

Skill
Workflow
Security
CC-BY-SA-4.0
Low risk
Has scripts

From the author

Runs a Semgrep security scan over a codebase: detects languages, selects rulesets, presents the plan for explicit approval, then runs every approved ruleset through scripts/run-scans.sh, which batches the semgrep processes and writes scans.json, and merges the output to SARIF. Supports two scan modes, "run all" for full ruleset coverage and "important only" for security findings at medium-to-high confidence and impact. Uses Semgrep Pro for cross-file taint analysis when it is available. Use when asked to scan code for vulnerabilities, run a security audit with Semgrep, find bugs, or perform static analysis. For the same scan without the approval gate, use the /static-analysis:semgrep-scan workflow.

— the description by Trail of Bits

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill automates the execution of Semgrep security scans across a codebase. It manages the entire lifecycle of a scan: detecting languages, selecting appropriate rulesets, requiring explicit user approval for the scan plan, executing scans in parallel batches, and merging results into a unified SARIF file. It supports two modes: a full scan of all rulesets and an important-only mode that filters findings based on security category, confidence, and impact metadata.

What it does

Detects programming languages and checks for Semgrep Pro availability.
Selects relevant official and third-party rulesets based on detected languages.
Presents a detailed scan plan for explicit user approval before execution.
Executes scans using batched processes to generate raw JSON and SARIF outputs.
Applies optional post-filtering to focus on high-confidence security vulnerabilities.
Merges all scan results into a single, consolidated SARIF report.

When to use it

Performing a security audit of a codebase.
Identifying potential vulnerabilities prior to code review.
Scanning for known bug patterns.
Conducting first-pass static analysis.

Advantages

Enforces security best practices like disabling telemetry during scans.
Ensures consistent scan configuration through automated script execution.
Provides a clear approval gate to confirm scan scope and rulesets.
Supports cross-file taint analysis when Semgrep Pro is available.
Consolidates findings from multiple rulesets into a single report.

Usage examples

“Run a security scan on the current directory.”: The agent detects the languages, proposes a ruleset plan, and waits for your approval before running the scan.
“Perform an important-only security audit on /path/to/code.”: The agent configures the scan to filter for medium-to-high confidence security findings and generates a merged SARIF report.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/trailofbits/semgrep/download
Unzip it into the cross-client skills folder of your project:
```
mkdir -p .agents/skills && unzip semgrep.zip -d .agents/skills
```

Documentation by Trail of Bits

Semgrep Security Scan

Run a Semgrep scan with automatic language detection, parallel execution, and merged SARIF output.

Essential Principles

Always use --metrics=off — Semgrep sends telemetry by default; --config auto also phones home. Every semgrep command must include --metrics=off to prevent data leakage during security audits.
User must approve the scan plan (Step 3 is a hard gate) — The original "scan this codebase" request is NOT approval. Present exact rulesets, target, engine, and mode; wait for explicit "yes"/"proceed" before spawning scanners.
Third-party rulesets are required, not optional — Trail of Bits, 0xdea, and Decurity rules catch vulnerabilities absent from the official registry. Include them whenever the detected language matches.
scripts/run-scans.sh generates the commands; do not write them yourself — it builds every semgrep line from the approved list. That is what makes --metrics=off, the --include scoping rule, and the parallel dispatch properties of the code rather than instructions. Give it the approved rulesets and let it run.
Always check for Semgrep Pro before scanning — Pro enables cross-file taint tracking and catches ~250% more true positives. Skipping the check means silently missing critical inter-file vulnerabilities.
Report what did not run — scans.json carries failed and skipped alongside scans. A ruleset whose repo would not clone, or whose scan exited non-zero, must appear in the report. A partial scan presented as a complete one is worse than no scan.

When to Use

Security audit of a codebase
Finding vulnerabilities before code review
Scanning for known bug patterns
First-pass static analysis

When NOT to Use

Binary analysis → Use binary analysis tools
Already have Semgrep CI configured → Use existing pipeline
Need cross-file analysis but no Pro license → Consider CodeQL as alternative
Creating custom Semgrep rules → Use semgrep-rule-creator skill
Porting existing rules to other languages → Use semgrep-rule-variant-creator skill

Output Directory

All scan results, SARIF files, and temporary data are stored in a single output directory.

If the user specifies an output directory in their prompt, use it as OUTPUT_DIR.
If not specified, default to ./static_analysis_semgrep_1. If that already exists, increment to _2, _3, etc.

In both cases, always create the directory with mkdir -p before writing any files.

# Resolve output directory
if [ -n "$USER_SPECIFIED_DIR" ]; then
  OUTPUT_DIR="$USER_SPECIFIED_DIR"
else
  BASE="static_analysis_semgrep"
  N=1
  while [ -e "${BASE}_${N}" ]; do
    N=$((N + 1))
  done
  OUTPUT_DIR="${BASE}_${N}"
fi
mkdir -p "$OUTPUT_DIR/raw" "$OUTPUT_DIR/results"

The output directory is resolved once at the start of Step 1 and used throughout all subsequent steps.

$OUTPUT_DIR/
├── rulesets.json                # The approved plan (Step 3), read by run-scans.sh (Step 4)
├── scans.json                   # What ran, failed, skipped, and covered nothing (Step 4)
├── raw/                         # Per-scan raw output (unfiltered)
│   ├── python-python.json        # <language>-<ruleset> for language-scoped rules
│   ├── python-python.sarif
│   ├── python-django.json
│   ├── python-django.sarif
│   ├── all-security-audit.json   # all-<ruleset> for cross-language rules, run once
│   ├── all-security-audit.sarif
│   └── ...
└── results/                     # Final merged output
    └── results.sarif

Prerequisites

Required: Semgrep CLI (semgrep --version). If not installed, see Semgrep installation docs.

Optional: Semgrep Pro — enables cross-file taint tracking, inter-procedural analysis, and additional languages (Apex, C#, Elixir). Check with:

# --metrics=off because Principle 1 has no exceptions, and this is the first semgrep command
# of a run. stderr is kept because "OSS only" has several causes (logged out, no subscription,
# registry blocked) and the run downgrades silently for all of them.
if PRO_ERR=$(semgrep --pro --validate --metrics=off --config p/default 2>&1); then
  echo "Pro available"
else
  echo "OSS only"
  echo "  reason: $(printf '%s' "$PRO_ERR" | tail -n 3)"
fi

Limitations: OSS mode cannot track data flow across files. Pro mode uses -j 1 for cross-file analysis (slower per ruleset, but parallel rulesets compensate).

Scan Modes

Select mode in Step 2. Mode affects both the scan flags and post-processing.

Mode	Coverage	Findings Reported
Run all	All rulesets, all severity levels	Everything
Important only	All rulesets, pre- and post-filtered	Security vulns only, medium-high confidence/impact

Important only applies two filter layers:

Pre-filter: --severity WARNING --severity ERROR (CLI flag)
Post-filter: JSON metadata — keeps only category=security, confidence∈{MEDIUM,HIGH}, impact∈{MEDIUM,HIGH}

See scan-modes.md for metadata criteria and jq filter commands.

Orchestration Architecture

┌──────────────────────────────────────────────────────────────────┐
│ MAIN SESSION (this skill)                                        │
│ Step 1: Detect languages + check Pro availability                │
│ Step 2: Select scan mode + rulesets (ref: rulesets.md)           │
│ Step 3: Present plan + rulesets, get approval [⛔ HARD GATE]     │
│ Step 4: Run scripts/run-scans.sh with the approved rulesets      │
│ Step 5: Post-filter, merge, report, delete repos/                │
└──────────────────────────────────────────────────────────────────┘
         │ Step 4: Bash
         ▼
┌──────────────────────────────────────────────────────────────────┐
│ scripts/run-scans.sh                                             │
│   clone       each third-party repo once, into repos/            │
│   generate    one semgrep command per ruleset                    │
│                ├── python     p/python, p/django   --include=*.py│
│                ├── javascript p/javascript         --include=*.js│
│                ├── docker     p/dockerfile                       │
│                └── cross-language  p/security-audit, p/secrets,  │
│                                    the cloned repos  (no filter) │
│   run         in batches of --jobs, exit code read per process   │
│   write       scans.json — scans, failed, skipped                │
└──────────────────────────────────────────────────────────────────┘

The approval gate stays in the session; the script is execution only and asks nothing. The approved list reaches it as a JSON file, so the scan cannot reach a ruleset the user declined.

Cross-language rulesets go in one shared unit rather than being repeated per language. p/security-audit, p/secrets, and the third-party repos scan the whole target unscoped, so running them once per language ran the identical command N times and left the SARIF merge to dedup the copies.

Running it as a Workflow

This plugin ships /static-analysis:semgrep-scan, which runs the whole scan end to end: detect languages and Pro, select rulesets from rulesets.md, run scripts/run-scans.sh, merge and report. Pass it a JSON object, not prose:

/static-analysis:semgrep-scan {"target": "/abs/path", "mode": "run-all"}

It does not stop for ruleset approval. Invoking it with a target is the opt-in, the same way /variant-analysis:variants works. That is safe to do because the scan is read-only over the target — no --autofix, every write inside the output directory — so the approval gate below is a scope confirmation rather than a safety one. What ran is recorded in rulesets.json and scans.json either way.

Use the workflow when you want the scan run; work the five steps below when the ruleset selection itself matters and you want to see and edit the list first.

Workflow

Follow the detailed workflow in scan-workflow.md. Summary:

Step	Action	Gate	Key Reference
1	Resolve output dir, detect languages + Pro availability	—	Use Glob, not Bash
2	Select scan mode + rulesets	—	rulesets.md
3	Present plan, get explicit approval	⛔ HARD	AskUserQuestion
4	Run the scans	—	`scripts/run-scans.sh`
5	Post-filter, merge, report, clean up	—	Merge script (below)

Task enforcement: On invocation, create 5 tasks with blockedBy dependencies (each step blocks the previous). Step 3 is a HARD GATE — mark complete ONLY after user explicitly approves.

Merge command (Step 5):

# run-all
uv run --no-project {baseDir}/scripts/merge_sarif.py "$OUTPUT_DIR/raw" "$OUTPUT_DIR/results/results.sarif" \
  --scans "$OUTPUT_DIR/scans.json"

# important-only, once the JSON post-filter has run over every file in raw/
uv run --no-project {baseDir}/scripts/merge_sarif.py "$OUTPUT_DIR/raw" "$OUTPUT_DIR/results/results.sarif" \
  --important --scans "$OUTPUT_DIR/scans.json"

--scans drops the output of scans listed under .failed. A scan that died part-way may still have written a .sarif, and under --important that file has no post-filter beside it, which is an error rather than an empty filter. Without the flag one dead scan denies every healthy scan a merged result. The excluded files are named on stdout, so they can go in the report.

The post-filter reads metadata SARIF does not carry, so it cannot be re-run against the merged file; --important instead keeps the findings the JSON filter kept, matched on (rule, file, line). Without it results.sarif is unfiltered while the JSON side is not.

Workflow and agents

Component	Purpose
`scripts/run-scans.sh`	Builds every scan command from the approved rulesets, runs them in batches, and writes `scans.json`

Step 4 is a Bash call. No subagent runs any part of the scan: exit codes and finding counts are read from the processes and the JSON they wrote.

Rationalizations to Reject

Shortcut	Why It's Wrong
"User asked for scan, that's approval"	Original request ≠ plan approval. Present plan, use AskUserQuestion, await explicit "yes"
"Step 3 task is blocking, just mark complete"	Lying about task status defeats enforcement. Only mark complete after real approval
"I already know what they want"	Assumptions cause scanning wrong directories/rulesets. Present plan for verification
"Just use default rulesets"	User must see and approve exact rulesets before scan
"Add extra rulesets without asking"	Modifying approved list without consent breaks trust
"Third-party rulesets are optional"	Trail of Bits, 0xdea, Decurity catch vulnerabilities not in official registry — REQUIRED
"Use --config auto"	Sends metrics; less control over rulesets
"I'll just run the semgrep commands myself"	`run-scans.sh` is what enforces `--metrics=off`, the `--include` rule and the output-directory `--exclude`. Hand-written commands drop them silently
"The script failed, I'll run semgrep directly to get something"	A non-zero exit means no scan succeeded. Report that and stop; a hand-run subset reads as a full scan
"Some scans failed, the run still finished"	`failed` and `skipped` are part of `scans.json`. Report them or the user reads a partial scan as a clean one
"Pro is too slow, skip --pro"	Cross-file analysis catches 250% more true positives; worth the time
"Semgrep handles GitHub URLs natively"	URL handling fails on repos with non-standard YAML; always clone first
"Cleanup is optional"	Cloned repos pollute the user's workspace and accumulate across runs
"Use `.` or relative path as target"	Subagents need absolute paths to avoid ambiguity
"Let the user pick an output dir later"	Output directory must be resolved at Step 1, before any files are created

Reference Index

File	Content
rulesets.md	Complete ruleset catalog and selection algorithm
scan-modes.md	Pre/post-filter criteria and jq commands

Workflow	Purpose
scan-workflow.md	Complete 5-step scan execution process
`scripts/run-scans.sh`	The scan runner Step 4 calls

Success Criteria

[ ] Output directory resolved (user-specified or auto-incremented default)
[ ] All generated files stored inside $OUTPUT_DIR
[ ] Languages detected with file counts; Pro status checked
[ ] Scan mode selected by user (run all / important only)
[ ] Rulesets include third-party rules for all detected languages
[ ] User explicitly approved the scan plan (Step 3 gate passed)
[ ] run-scans.sh exited 0 and wrote $OUTPUT_DIR/scans.json
[ ] failed and skipped from scans.json are empty, or listed in the report
[ ] Scans marked partial in scans.json are none, or listed in the report — they ran with some of their rules failing to compile
[ ] Every semgrep command used --metrics=off
[ ] Approved plan written to $OUTPUT_DIR/rulesets.json at the Step 3 gate, and passed to the scanner unchanged
[ ] coveredNothing from scans.json is empty, or listed in the report
[ ] oversized from scans.json is empty, or listed in the report — those files exceed --max-target-bytes and no ruleset opened them
[ ] Raw per-scan outputs stored in $OUTPUT_DIR/raw/
[ ] results.sarif exists in $OUTPUT_DIR/results/ and is valid JSON
[ ] Important-only mode: post-filter applied before merge, merge run with --important, unfiltered results preserved in raw/
[ ] Results summary reported with severity and category breakdown
[ ] Cloned repos (if any) cleaned up from $OUTPUT_DIR/repos/

Files

ATTRIBUTION.mdAdded by AutoGineer923 B
LICENSELicence file added by AutoGineer19.7 KiB
SKILL.md15.3 KiB
agents/openai.yaml229 B
assets/trail-of-bits-mark.svg3.0 KiB
references/rulesets.md7.5 KiB
references/scan-modes.md8.5 KiB
scripts/merge_sarif.pyScript13.7 KiB
scripts/run-scans.shScript33.6 KiB
scripts/test_merge_sarif.pyScript22.4 KiB
workflows/scan-workflow.md24.1 KiB

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Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Names Claude Code tools, Needs Python

Security

What our scanner noticed· Low risk

Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by

Trail of Bits

Publisher

Trail of Bits (Official)

Source

trailofbits/skills · plugins/static-analysis/skills/semgrep · commit e4dd0bb

Licence

Creative Commons Attribution-ShareAlike 4.0 International (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Trail of Bits.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download semgrep.zip