outline-driven-development
Outline-Driven Development - opinionated methodology between vibes and specs. AST tooling + context engineering + loop engineering on your hands. Plugins for Claude / Codex / Gemini / Cursor.
Install / Use
npx skills add OutlineDriven/outline-driven-developmentInstalls into whichever agent you are using.
Devin Config
Devin AI agent config
Quality Score
Category
Development & EngineeringSupported Platforms
Skill content
View source on GitHubYARA branch detail
Branch-specific procedure detail for yara-rule-authoring. Each section applies only when the run takes that branch.
Reviewing an existing rule
Run yara_lint.py and atom_analyzer.py before reading the rule by eye. Quote the codes they emit. Then evaluate whether the strings identify this specific family and whether the condition can fire on generic strings alone.
Migrating from legacy YARA
Run yr check --relaxed-re-syntax to identify issues. Fix each one, then verify without relaxed mode. Common fixes:
| Issue | Legacy | YARA-X fix |
|---|---|---|
| Literal { in regex | /{/ | /\{/ |
| Invalid escapes | \R silently literal | \\R or R |
| Base64 strings | Any length | 3+ chars required |
| Negative indexing | @a[-1] | @a[#a - 1] |
| Duplicate modifiers | Allowed | Remove duplicates |
CRX module (Chrome extensions)
Requires YARA-X v1.5.0+, or v1.11.0+ for permhash(). Key APIs: crx.is_crx, crx.permissions, crx.permhash(). Red flags: nativeMessaging + downloads, debugger permission, content scripts on <all_urls>.
DEX module (Android)
Requires YARA-X v1.11.0+. Not compatible with legacy YARA's dex module; the API is completely different. Key APIs: dex.is_dex, dex.contains_class(), dex.contains_method(), dex.contains_string(). Red flags: single-letter class names (obfuscation), DexClassLoader reflection, encrypted assets.
When strings fail, pivot to structure
If extraction returns only API names and generic paths, use math.entropy() on specific sections for high-entropy detection, use pe.imphash() for import-hash clustering, check PE structure anomalies (section names, sizes, characteristics), and examine metadata (version info, timestamps, resources). If nothing unique remains, the sample may not be detectable with YARA alone.
Rationalizations to reject
When these appear in the agent's own thinking, stop and reconsider:
| Rationalization | Response |
|---|---|
| "This generic string is unique enough" | Unique in one sample does not mean unique across the ecosystem. Test against goodware. |
| "yarGen gave me these strings" | yarGen suggests; validate each one manually; expect to discard 80%. |
| "It works on my 10 samples" | 10 samples is not production. Use a goodware corpus. |
| "One rule to catch all variants" | Causes FP floods. Target specific families. |
| "I'll make it more specific if we get FPs" | Write tight rules upfront. A weak rule deployed is damage done. |
| "This is just for hunting" | Hunting rules become detection rules. Same quality bar. |
| "The API name makes it malicious" | Legitimate software uses the same APIs. Need behavioral context. |
| "any of them is fine for these common strings" | Common strings + any = FP flood. Use any of only for individually unique strings. |
| "This regex is specific enough" | /fetch.*token/ matches all auth code. Add an exfil destination requirement. |
| "I'll use .* for flexibility" | Unbounded regex = performance disaster plus memory explosion. Use .{0,30}. |
| "Performance doesn't matter" | One slow rule slows the entire ruleset. Optimize atoms. |
| "I'll use --relaxed-re-syntax everywhere" | Masks real bugs. Fix the regex instead. |
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