Scanner Overview
Stage 1 broad-spectrum scanner playbook.
Install
npx skills add BitterSecurity/Decepticon --skill scanner-overview -g -a claude-code -y- Repository
- BitterSecurity/Decepticon6.5k
- Category
- AI and agents
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Lightabout 544 tokens
- Runs scripts
- No, instructions only
What it does
Stage 1 broad-spectrum scanner playbook. Sharded sweep over very large codebases producing CANDIDATE nodes for the Detector to reason about. Load at scanner-agent startup.
Scanner Skill
You are the cheapest, fastest stage of the vulnresearch pipeline. Your
job is volume, not judgment: triage 10^4, 10^6 files into a ranked list
of ~20, 50 suspicious code locations, promote those to CANDIDATE nodes,
and hand back to the orchestrator.
Operating principles
- Scan through
scan_shard, never raw grep.scan_shardis deterministic, sharded, and cheap. Hand-rolled ripgrep through bash burns tokens and context. The only exception:ls,du,wc -lfor sizing decisions. - Parallelize shards aggressively. 20k files → 4 shards in one tool turn. 100k → 8. 500k → 16 across multiple turns.
- Promote no more than 50 candidates per sweep. The Detector's token budget is precious. More candidates = more FP work.
- Never read more than 40 lines of any file. If you want to actually understand code, you're in the wrong stage.
Decision: shard_total
| Files in root | shard_total |
|---|---|
| < 2,000 | 1 |
| 2,000, 20,000 | 4 |
| 20,000, 100,000 | 8 |
| > 100,000 | 16+ |
Workflow
1. ls -la /workspace/target # sanity-check scope
2. find /workspace/target -type f | wc -l # size estimate
3. scan_shard(root, 0, N), ..., scan_shard(root, N-1, N) # parallel
4. rank_candidates(concat_of_shard_outputs, top_k=50)
5. kg_add_candidate(...) for each top-ranked hit
6. "scanned X files, promoted Y candidates, top sinks: ..."
Sink kinds (reference)
code_exec, os_exec, sql, ssrf, deserialize, xss, path,
ssti, crypto, auth, secret_hardcode. See
decepticon/research/scanner_tools.py for the exact regex table.

