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Getting started

Install

Requires Node.js 22+. The recipe below uses pnpm; npm and yarn work the same way.

deepsec lives in a .deepsec/ directory at the root of your codebase — checked into the same git repo, so config, project context, and custom matchers travel with the code. Generated scan output (findings, runs, reports) stays gitignored.

From the root of the codebase you want to scan:

npx deepsec init                                   # creates .deepsec/ + registers this repo
cd .deepsec
pnpm install                                       # installs deepsec

init lays down a minimal scaffold inside .deepsec/: package.json, deepsec.config.ts (one projects[] entry pointing at .., id derived from your repo dir's basename), data/<id>/INFO.md (template with section placeholders), data/<id>/SETUP.md (per-project agent prompt), workspace-level AGENTS.md, .env.local, .gitignore. No custom matchers in the scaffold — add those later, only when a real finding shapes one for you.

Open .env.local and fill in AI_GATEWAY_API_KEY. Get one from Vercel AI Gateway — one token covers both Claude and Codex. Prefer Anthropic directly? Set ANTHROPIC_AUTH_TOKEN=sk-ant-… and ANTHROPIC_BASE_URL=https://api.anthropic.com instead. If claude or codex is already logged in on this machine, non-sandbox runs (process / revalidate / triage) skip the token and reuse the subscription. See vercel-setup.md.

To scan a different codebase from the same .deepsec/, run pnpm deepsec init-project <path> — relative paths resolve against .deepsec/'s parent.

Fill in INFO.md

INFO.md is what makes deepsec project-aware. It's injected into the AI prompt for every batch — vague content here means vague findings.

Option A: let your coding agent do it (recommended)

Open the parent repo (the codebase you scanned, not .deepsec/) in your coding agent (Claude Code, Codex, Cursor, …) and paste the prompt that deepsec init printed. It walks the agent through:

  1. Read .deepsec/node_modules/deepsec/SKILL.md to understand the tool.
  2. Open .deepsec/data/<id>/SETUP.md for project-specific instructions.
  3. Skim the codebase, then replace each section of .deepsec/data/<id>/INFO.md.

The same prompt is shown in the project root README and is what init prints to stdout after scaffold.

Option B: by hand

The processor auto-loads data/<id>/INFO.md from the workspace's data dir. Edit it directly — no extra wiring needed in deepsec.config.ts. INFO.md is optional but worth keeping; even a paragraph noticeably improves the AI's output.

Run a scan

Before the first command: deepsec writes per-project state to ./data/<project-id>/ next to your config — files/ (one JSON per scanned source file), runs/, plus project.json and the optional INFO.md / config.json. The directory is gitignored by default; see data-layout.md for the full schema.

pnpm deepsec scan

--project-id is auto-resolved when the config has a single project (the common case). Pass --project-id <id> once you've registered more than one project. Pass --root <path> to override the resolved path — useful for one-off scans against a different checkout.

scan runs ~110 regex matchers across the codebase. There are no AI calls at this stage. On a 2,000-file project it takes ~15s. Output goes to data/<id>/files/ as one JSON file per scanned source file (called a FileRecord).

pnpm deepsec status

shows the current state: how many files were scanned, how many are pending AI investigation, etc.

Run the AI investigation

pnpm deepsec process --concurrency 5

Defaults: Claude Opus, 5 files per batch, 5 batches in parallel = 25 files in flight at once. You can lower the parallelism (--concurrency 1) or set --limit 50 to budget-cap.

A rough cost guide (Opus, default settings):

Files Approx cost Approx wall time
100 $25–60 5–15 min
500 $130–300 25–60 min
2,000 $500–1200 1.5–4 hr

Costs swing 2–3x based on file complexity. Run --limit 50 first to calibrate before committing to the full pass.

For a cheaper backend:

pnpm deepsec process --agent codex --model gpt-5.5

Codex is the OpenAI-flavored backend. Same prompt, same JSON output, different agent loop. Try both on a small sample to see which catches shapes you care about. See models.md for the full backend / model matrix, refusal handling, and how to swap in newer models.

Triage and revalidate

pnpm deepsec triage --severity HIGH
pnpm deepsec revalidate --min-severity HIGH
  • triage: classifies findings P0/P1/P2 without re-reading the code. ~1¢/finding.
  • revalidate: re-reads the code and the git history, then emits a TP/FP/Fixed/Uncertain verdict. Comparable cost to process. Cuts FP rate by 50%+ on most repos.

Both optional, but worth running on the HIGH/CRITICAL set.

Get the findings out

pnpm deepsec export --format md-dir --out ./findings
pnpm deepsec export --format json   --out findings.json

md-dir writes one markdown file per finding under ./findings/{CRITICAL,HIGH,MEDIUM,…}/. json writes a single array suitable for piping to a downstream issue tracker.

For a quick aggregate look:

pnpm deepsec metrics

(Each of these commands accepts --project-id <id> if your config has multiple projects; the auto-resolution only kicks in when there's exactly one.)

Next

  • docs/writing-matchers.md — prompt your coding agent to compare data/ matches against the target repo and write matchers that close the entry-point coverage gaps.
  • docs/configuration.md — every field on deepsec.config.ts and data/<id>/config.json.
  • docs/models.md — defaults, --agent / --model, refusal handling, future models.
  • docs/plugins.md — for org-specific patterns that don't belong in the public matcher set.