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v1.0

decision-log

@maga2010⭐ 352 stars

Writes every KEEP/CUT/DEFER/PIVOT scope decision into an append-only team log with rationale, author, and timestamp. Use after scope-knife or any time the team changes direction.

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—/10

// RATINGS

⭐GitHub Stars
⭐⭐⭐ 352 on GitHubGitHub ↗

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// README

🏁 Hackathon Run

Ship the demo, not the dream.

A decision-making and execution system for hackathon teams operating under time pressure. Fifteen skills, one workflow: clarify, prize-target, scope, time-box, build, verify, demo, judge, ship, recover, pivot, retro, decide-log.

CI Version License: MIT Stars npm version npm downloads GitHub release


The problem

A hackathon is not a coding problem. It is a time-pressure, decision-making, execution problem.

  • 23 hours in, you have 8 unfinished features.
  • The demo crashes on stage and you have 60 seconds to recover.
  • A judge asks "what's novel here?" and you have no answer.
  • Your README references API keys that you cannot ship.
  • Your teammate has been debugging the wrong thing for 4 hours.

Hackathon Run does not help you write code faster. It helps you make the right cut, at the right time, every time.


How it works

Fifteen skills, mapped to the hackathon lifecycle:

idea-clarify (pre) pivot (mid-build redirect) │ │ ▼ ▼ scope-knife ─► time-box ─► fast-verify ─► demo-coach ─► judge-sim ─► ship-pack │ │ │ │ │ │ └──────────────┴────────────┴──────────────┴─────────────┴────────────┘ │ │ │ │ │ │ stack-picker (cold-start) retro (post-event) │ │ │ │ team-roster (build start) recovery-runbook (anytime) │ │ demo-rehearsal (final 2h)

SkillWhenOutput
idea-clarifyOne-paragraph brief, no demo_goal yet(artifact only)
scope-knifeToo many ideas, no MVP consensus, clock shrinkingKEEP/CUT/DEFER classification + demo path
fast-verify"Will this demo work?"Step-by-step verification, stops at first failure
demo-coach30/60/90-second pitch, no clear narrativeFlow script + risk flags
judge-simPre-submission self-review0-5 rating across 7 dimensions + fix priorities
ship-packSubmitting now, worried about secretsREADME check, secret scan, packaging command
recovery-runbookDemo fails on stageP0-P3 severity, fallback strategy, 30-second script
pivotMid-build direction changeRe-runs scope-knife with new constraints
time-box"How much time for each stage?"Schedule + per-stage checkpoints
stack-picker"What stack should we use?"Recommendation + 30-min bootstrap walkthrough
retroAfter submission, want ratios + action list4 ratios + keep_doing/stop_doing/try_next_time
demo-rehearsalFinal 2 hours, want a timed mock runPer-segment score + fix list
team-rosterBuild phase, >2 KEEP features, roles unclearRole assignments + bottleneck + rescuer
prize-strategyMulti-track hackathon, picks which prize to chaseTarget prize + 3-5 positioning actions
decision-logEvery cut needs a recorded "why"Append-only decision record with rationale

Each skill is independently invokable. You can run any of them at any time without running the others.


Agent workflow

Hackathon Run works best when an agent treats it as a harness, not as a menu of one-shot prompts. Four roles keep long-running work moving without letting the same agent both build and approve its own output: an initializer sets up the first session, a planner writes the default-FAIL contract, a generator builds one feature per sprint, and an evaluator verifies it from a fresh context.

The runtime follows the production agent-loop pattern used by ChatGPT and the OpenAI Agents SDK: context is assembled from sessions, every input and output passes a guardrail, tools return observable results, the loop is bounded by budget, and every meaningful step is traced. It also follows Anthropic's long-running harness pattern: the first session initializes the environment, every later session reads PROGRESS.md + git log, and the operator can stop or steer the loop from the outside.

Production agent loop

flowchart LR
  User(["User / trigger"]) --> InGuard{"Input guardrail\npolicy + budget + schema"}
  InGuard -->|"reject"| Block(["Blocked\nrefuse + explain"])
  InGuard -->|"accept"| Context["Context assembly\nsession + plan + skill"]
  Context --> Loop{"Agent loop\nmax_turns + budget"}
  Loop -->|"next turn"| Reason["Reason\nchoose action"]
  Reason --> Tools["Tool invocation\nskills / scripts / MCP / shell"]
  Tools --> Observe["Observe\nstdout / files / tests / evidence"]
  Observe -->|"loop"| Loop
  Loop -->|"final"| OutGuard{"Output guardrail\nJSON Schema + evidence"}
  OutGuard -->|"reject"| Loop
  OutGuard -->|"accept"| Output(["Final output\nstate + evidence"])
  Context -. "read / write" .-> Session[("Session\nhandoff + memory")]
  Loop -. "trace" .-> Trace[("Trace\nevents / spans")]

Harness runtime architecture

The production agent loop is organized into six layers: interface, context, agent loop, hands, durable state, and operator control. Every layer writes to or reads from the durable state store so a fresh context window can resume without the previous conversation.

flowchart TB
  classDef state fill:#fff7ed,stroke:#ea580c,color:#7c2d12;
  classDef gate fill:#eff6ff,stroke:#2563eb,color:#1e3a8a;
  classDef trace fill:#f0fdf4,stroke:#16a34a,color:#14532d;

  subgraph Interface["Interface Layer"]
    User(["User / trigger"]) --> InGuard{"Input guardrail\npolicy / budget / schema"}
    InGuard -->|"reject"| Reject(["Blocked\nrefuse + explain"])
    InGuard -->|"accept"| ContextAssembly["Context Assembly\nplan + session + skill + progress"]
  end

  subgraph Context["Context Layer"]
    Session[("session.json\nhandoff")]
    Progress[("PROGRESS.md\nagent log")]
    Git[("git log\ncommit history")]
    ContextAssembly -. "reads" .-> Session
    ContextAssembly -. "reads" .-> Progress
    ContextAssembly -

// HOW IT'S BUILT

KEY FILES

skills/decision-log/SKILL.mdREADME.md

// REPO STATS

352 stars