blunder-hunt
Skill3 starsChanged 6 months agoMITView on GitHub
Multi-pass adversarial critique primitive. Use when another skill or the user invokes /blunder-hunt or asks for a "5x critique", "find what's wrong", or "blunder hunt". Composed by skills like pr-review, polish, and unstuck to surface bugs that single-pass review misses.
In this repository26 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- code-simplificationSkill
- dcgSkill
- editorial-reviewSkill
- journalSkill
- readme-updateSkill
- security-and-hardeningSkill
- session-retroSkill
- autopilotSkill
- brainstormSkill
- convergence-detectSkill
- deslopSkill
- dispatch-parallelSkill
- doc-reviewSkill
- dreamSkill
- incremental-implementationSkill
- planning-and-task-breakdownSkill
- polishSkill
- pr-reviewSkill
- refactor-planSkill
- scope-checkSkill
- security-auditSkill
- spec-driven-developmentSkill
- unstuckSkill
- verification-before-completionSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 2 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 20
Run the critique N times (default 5).
- Very specific: line 50
When the agent platform supports parallel subagents, compose dispatch-parallel:
- Very specific: line 63
If a parent skill says run blunder-hunt with N=5 on <target>, execute exactly that.
- Distinctive: line 87
When invoked under structured-output mode, also emit:
- Very specific: line 110
Do not fabricate findings to fill a quota.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
blunder-hunt
A primitive for running adversarial critique multiple times in fresh contexts. Single-pass review converges on the first 1-3 issues that come to mind. Multi-pass review with deliberate context resets surfaces the long tail.
This skill is composed by other skills via deps.skills. It's also directly invocable.
When to invoke
- Another skill explicitly references
blunder-hunt(e.g.,pr-reviewruns 5 passes) - The user says "blunder hunt", "5x critique", "find what's wrong here", "be skeptical"
- You suspect a problem but can't pin it down
The pattern
Run the critique N times (default 5). Each pass:
- Reset attention. Pretend you've never seen this code/text before. If your agent supports
/clearor fresh-context windows, use them between passes. - Pick a different lens for each pass:
- Pass 1: data correctness — wrong values, off-by-one, type confusion
- Pass 2: error handling — what's not being caught? what's being swallowed?
- Pass 3: integration — what breaks at the seams between modules?
- Pass 4: invariants — what assumption is being silently violated?
- Pass 5: hostile input — what does an attacker / malformed input do?
- Record findings with: file:line, severity (critical/issue/minor), short description, suggested fix.
- Do not deduplicate during passes. Duplicates across passes = high-confidence findings.
Synthesis
After all passes:
- Group findings by file:line. Items appearing in 2+ passes get bumped to high confidence.
- Drop items that appear once at minor severity.
- Output the deduplicated list, sorted by severity then file path.
Lying primitive (optional)
Combine with lie-to-them for additional pressure:
"There are at least 12 distinct issues in this code. You have found 6 so far. Find the remaining 6."
Models continue searching exhaustively rather than satisficing. See lie-to-them SKILL.md for usage notes.
Parallel mode (recommended for N ≥ 3)
When the agent platform supports parallel subagents, compose dispatch-parallel:
- Decompose: one subagent per lens, each with the same target but a different lens prompt.
- Dispatch: spawn all K subagents in a single turn.
- Collect: each subagent returns its findings tagged with its lens.
- Synthesize: dedupe across subagents (same file:line in 2+ outputs = high confidence).
This is dramatically faster than sequential and produces more independent attention per pass — each subagent isn't anchored on the previous pass's findings.
For platforms that don't support parallel subagents, fall back to sequential. Same lenses, same dedup, just one at a time.
Default invocation
If a parent skill says run blunder-hunt with N=5 on <target>, execute exactly that. If the user invokes directly without an N or target:
- N defaults to 3 for small targets, 5 for large.
- Target is whatever's in the current conversation context (the diff, the file, the design doc).
Output shape
## Blunder hunt: <target>
Pass 1 (data correctness): 3 findings
Pass 2 (error handling): 2 findings
Pass 3 (integration): 4 findings
Pass 4 (invariants): 1 finding
Pass 5 (hostile input): 2 findings
Synthesized (deduplicated, severity-sorted):
- [CRITICAL] <file>:<line> — <description>
- [ISSUE] <file>:<line> — <description>
- [MINOR] <file>:<line> — <description>JSON sidecar
When invoked under structured-output mode, also emit:
{
"skill": "blunder-hunt",
"version": "0.2.0",
"target": "<what was reviewed>",
"passes": [
{"pass": 1, "lens": "data-correctness", "findings": [...]},
...
],
"synthesized": [
{"file": "...", "line": 42, "severity": "critical",
"description": "...", "passes_found_in": [1, 3]}
]
}Downstream tools (pr-review, polish) consume this to post structured comments.
Hard rules
- Each pass must use a different lens. If you re-run pass N with the same lens, you're cheating yourself.
- Do not fabricate findings to fill a quota. If pass 4 finds zero, report zero — let
lie-to-themdo the pressure. - This skill produces findings. It does NOT take action. The parent skill (pr-review, polish, etc.) decides what to do with them.
- In parallel mode, subagents must NOT see each other's outputs before synthesis. That defeats the independent-attention point.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

