grill
Skill1 starsChanged 5 months agoMITView on GitHub
Adversarial code review — challenge your code harshly, find what will break in production. Inspired by Boris Cherny's setup.
- Reads credentials
We split this skill into the rules it gives an agent
3 rules. 0 are common, 2 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 66 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 18
No args → Full 3-agent review of changed files
- Distinctive: line 32
If no files found: ask what to review and stop.
- Very specific: line 150
If tests fail after fixes, revert the failing fix (git checkout -- <file>) and note in report.
“Common” is measured: kept by at least 6 in 10 of the 66 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Grill (/grill)
Adversarial code review that actively tries to break your code.
Time budget: 10 min total (agents ~5 min, synthesis + fixes ~5 min). Abort at 10 min with partial report.
Arguments
Parse $ARGUMENTS string:
--security→ Security-only mode (just Agent 2)--perf→ Add performance stress-test agent (Agent 4)--files "a.ts,b.ts"→ Target specific files instead of git diff--report-only→ No auto-fixes, just report- No args → Full 3-agent review of changed files
Instructions
Step 1: Identify Target
git diff --name-only HEAD~1 # committed changes
git diff --name-only # unstaged changes
git diff --name-only --cached # staged changesMerge all three lists, deduplicate. If --files was passed, use those instead.
If no files found: ask what to review and stop.
Filter out non-code files (images, lockfiles, .env). If >20 files, ask user to narrow scope.
Step 2: Dispatch Adversarial Reviewers
Launch agents in parallel (Task tool, subagent_type: "general-purpose", model: "sonnet"). Each agent prompt MUST include: "Read each file using the Read tool before analyzing."
Agent 1 — The Breaker:
You are a hostile QA engineer. Your job is to BREAK this code. Read each file listed below using the Read tool, then find:
- Edge cases that will crash in production
- Race conditions and concurrency issues
- Input combinations that cause unexpected behavior
- State transitions that lead to corruption
- Error paths that swallow failures silently
Files to read and review: [list with full paths]
Be specific. Show the exact input/scenario that breaks it.
Do NOT suggest improvements — only find what's broken.
Rate each: Critical / High / Medium / Low.
If nothing is broken, say "No issues found" — don't invent problems.Agent 2 — The Security Auditor:
You are a penetration tester reviewing this code. Read each file listed below using the Read tool, then find:
- Injection vulnerabilities (SQL, XSS, command)
- Authentication/authorization bypasses
- Data exposure through error messages or logs
- Missing input validation at trust boundaries
- Secrets or credentials in code
- OWASP Top 10 violations
Files to read and review: [list with full paths]
Rate each finding: Critical / High / Medium / Low.
Show the attack vector for each.
If the code has no security surface (e.g. pure utility), say "No security issues found."Agent 3 — The Skeptic:
You are a senior engineer who thinks this code is wrong. Read each file listed below using the Read tool, then challenge:
- Does this actually solve the stated problem?
- Are the types correct or just making the compiler happy?
- Will this scale to 10x/100x current load?
- Are error messages helpful or misleading?
- Is the abstraction level right or over/under-engineered?
Files to read and review: [list with full paths]
For each concern, explain the concrete consequence of not fixing it.
Rate each: Critical / High / Medium / Low.
If the code is solid, say so — don't manufacture concerns.Agent 4 — The Stress Tester (only if --perf flag):
You are a performance engineer. Read each file listed below using the Read tool, then find:
- O(n^2) or worse algorithms on unbounded input
- Missing pagination/limits on queries
- N+1 query patterns
- Unbounded memory growth (accumulating arrays, missing cleanup)
- Blocking operations in async paths
Files to read and review: [list with full paths]
Rate each: Critical / High / Medium / Low.Skip Agent 1 and 3 if --security flag is set.
Step 3: Synthesize Findings
Deduplicate across agents. Merge into severity-sorted report:
## Grill Report — [date]
Files reviewed: [list]
Agents: [which ran]
### Critical (fix before shipping)
- [finding] — [file:line] — [agent]
### High (fix soon)
- [finding] — [file:line] — [agent]
### Medium (track for later)
- [finding] — [file:line] — [agent]
### Low (nice to fix)
- [finding] — [file:line] — [agent]
### Clean
- [things that passed all reviews]
### Verdict: SHIP / HOLD / BLOCKVerdict rules: any Critical → BLOCK. 3+ High → HOLD. Otherwise → SHIP.
Step 4: Fix (unless --report-only)
- Critical items: Present each fix to user for confirmation before applying. Never auto-fix security code silently.
- High items: List with recommended fixes, let user choose which to apply.
- Medium/Low: Note in report only.
Step 5: Re-verify
Detect project type and run tests:
package.jsonwith bun.lockb →bun testpackage.jsonwithout bun.lockb →npm testpytest.ini/pyproject.toml/tests/with .py →pytestGemfile→bundle exec rspecMakefilewith test target →make test
If tests fail after fixes, revert the failing fix (git checkout -- <file>) and note in report.
Other versions81 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 81
- kirodotdev/KiroCrew★ 4.7ksame name
- crbnos/carbon★ 2.8ksame name
- JuliusBrussee/cavekit★ 1.1ksame name
- SethGammon/Citadel★ 927same name
- nagi-studio/skills★ 387same name
- KerberosClaw/kc_ai_skills★ 79same name
- alamops/skills★ 53same name
- Kanevry/session-orchestrator★ 53same name
- oakreader/oakreader★ 46same name
- manusco/resonance★ 38same name
71 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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