hacker

Hacker

Define a problem. Aggregate all best practices. Hack it.

Drop this skill into Cursor. Your agent diagnoses prompt failures, searches the world for fixes, and applies them one at a time — with full regression protection. No guesswork.

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A developer spends two hours tuning a support-ticket classifier prompt. It works on three test cases. They deploy. A customer sends an all-caps complaint — the prompt classifies "URGENT BUG FIX" as positive feedback. They add a rule: "ignore all-caps text." Next morning, normal acronyms (API, HTTP, SQL) are being ignored too. Three old cases silently regressed. They didn't know, because they only re-tested the new case.

This is what prompt engineering looks like without a regression gate. Every fix is a blind bet. You can't prove version B is better than version A — you can only hope nothing broke.

What changes

The developer writes 8 test cases with expected outputs — including the all-caps edge case — and tells the agent:

"Run hacker against my classifier using corpus.md"

They walk away. When they come back, the scratchpad reads:

Cycle 3  ACCEPT  avg 6.2 → 7.1
  diagnosis: T4 false positive — all-caps acronyms treated as noise
  explored:  arxiv.org/abs/2301.xxxxx — case-folding preserves acronyms
  mutation:  replaced "ignore caps" with Unicode category filter
  regressed: none

Cycle 5  ACCEPT  avg 7.1 → 7.8
  diagnosis: T2 missed implicit complaint ("this is ridiculous")
  explored:  lobehub.com/skill/sentiment-boundary — negative-example pattern
  mutation:  added implicit-negative examples from public skill
  regressed: none

Cycle 8  ACCEPT  avg 7.8 → 8.0
  diagnosis: no case below 6.0 — entering pruning
  mutation:  deleted rule with no measurable effect across 3 epochs
  result:    prompt 18% shorter, same scores

They didn't write a single new rule. The agent diagnosed each failure, searched for evidence-backed solutions — a paper, a public skill, a community pattern — and applied them one cut at a time. Every change has a source URL, a hypothesis, and a before/after score.

This README was itself written using Hacker's methodology — 7 cycles of scoring against NDD narrative dimensions, with evidence-sourced mutations and accept/rollback gates. The evolution log is in this repo.

Built for the Magpie agent pipeline, where it evolves sub-agent prompts in production.

  [Define]                    [Aggregate]              [Hack]
      ↓                            ↓                      ↓
 Corpus + Labels ──▶ Score ──▶ Diagnose ──▶ Explore ──▶ Mutate ──▶ Re-run
                                                                      ↓
                                                              accept / rollback

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Try It

# Drop the skill into your project
cp -r hacker/ .cursor/skills/hacker/

# Or clone globally
git clone https://github.com/ERerGB/hacker.git ~/.cursor/skills/hacker

Then tell your agent:

"Run hacker against my support_bot.md using corpus.md"

It enters an optimization loop — diagnose, explore, mutate, verify — and logs every decision to a scratchpad you can audit after.

Continuous runs: Hacker does not bundle a loop driver. Pair it with your environment's re-invocation mechanism (e.g. Cursor Ralph Loop on .cursor/ralph/scratchpad.md, Claude Code's loop, or a shell wrapper) so each automated turn executes one full Hacker cycle. See SKILL.md — Integration with Iterative Loop Runners.

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The Cycle (10 Steps)

See SKILL.md for the full executable protocol.

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Configuration — Not Policy

Hacker's core is an abstract optimization loop. Concrete policies — scoring dimensions, stage gates, mutation operators, thresholds — are project-level configuration, not baked into the skill.

You configure:

• Evaluation: dimensions, weights, hard checks
• Explore: search providers, budget, evidence schema
• Mutation: available operators, evidence linking requirements
• Acceptance: gate thresholds, regression tolerance
• Stages: optional phased curriculum (e.g., recall → precision → quality → pruning)

See references/config-schema.ts for the TypeScript interface. It re-exports types and APIs from subagent-harness (^0.4.0) so prompts stay aligned with the published package.

cd hacker && pnpm install   # or npm install
pnpm run typecheck

Default profiles are provided as examples:

• references/stages.md — a 5-stage curriculum
• references/mutations.md — a 9-operator mutation menu

Composable sub-skills (for non-.agent.md candidates)

If your candidate is a SKILL.md section, a config file, or anything scored by a script (not subagent-harness workers), use the extracted references directly:

• references/hacker-core.md — 5-step cycle contract (no subagent-harness dependency)
• references/hacker-loop.md — Claude Code Agent template, Cursor Ralph, CI drivers

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Inspired By

The difference: those are research frameworks. This is a skill file you drop into your project and start using in 5 minutes.

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What's Next

Hacker is actively evolving (using its own methodology, naturally):

• Structured candidates — .agent.md format via subagent-harness for parse → mutate → serialize round-trips
• Evidence sourcing — SkillRank integration to search skill hubs during the Explore phase
• Pareto frontier — maintain multiple non-dominated prompt versions instead of a single best

Try it on your own prompt and share your scratchpad log. The best way to understand Hacker is to watch it think — and the best way to improve it is more real-world evolution logs.

License

MIT