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grahama1970/acceptance-contract

Turn a client brief, zip bundle, directory, or single requirements file into a typed acceptance-contract bundle with extracted requirements, acceptance checks, open questions, an immutable-goal draft, and a create-report-backed decision report. Use when users say acceptance contract, brief to requirements, freeze the goal, create immutable goal, amend immutable goal, build a Battle requirements bundle, or extract requirements from this bundle.

acceptance-contract란 무엇인가요?

acceptance-contract is a Antigravity agent skill that turn a client brief, zip bundle, directory, or single requirements file into a typed acceptance-contract bundle with extracted requirements, acceptance checks, open questions, an immutable-goal draft, and a create-report-backed decision report. Use when users say acceptance contract, brief to requirements, freeze the goal, create immutable goal, amend immutable goal, build a Battle requirements bundle, or extract requirements from this bundle.

지원 대상~Claude Code~Codex CLI~CursorAntigravity
npx skills add https://github.com/grahama1970/agent-skills/tree/main/skills/acceptance-contract

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문서

acceptance-contract은(는) 무엇을 하나요?

Use this before implementation when a brief, ticket, email, README, zip bundle, or evidence packet needs to become executable acceptance criteria.

First command for implementation work:

skills/acceptance-contract/run.sh ensure <zip|directory|file> \
  --out /tmp/acceptance-contract \
  --project-name oai-trial \
  --goal-mode create

ensure is the mechanical guard: it validates an existing acceptance_bundle.json, auto-extracts one when missing, and fails closed when the supplied source bundle has changed since the frozen contract was written. Its JSON receipt includes an acceptance_contract.progress.v1 meter with percent, checks, outstanding, and next_steps. Do not implement from a brief until ensure returns status=PASS and the progress meter says what remains.

Pass the client brief, zip bundle, or deliberately staged spec directory as input. The CLI refuses repository roots by default so implementation files cannot silently become the acceptance source. Use --allow-repo only when the human explicitly asks for a repository-wide contract.

For high-stakes client briefs, run a second-line $ask roundtable review on the brief/zip plus acceptance_bundle.json before implementation. Include WebGPT as one reviewer when available. Ask plainly: "Are these contract requirements correct, and did we miss anything?" Require reviewers to check whether any client obligation is missing from the frozen contract, especially representation classes, typed values, boundary conditions, and disqualifying failure modes. The review is advisory evidence; deterministic gates still own PASS/FAIL. The oai-trial review found that typed-scalar coverage must also require canonical equivalence: formatted policy strings, digit-only numeric scalars, decimal forms, scientific notation, and SQLite numeric values cannot be separate acceptance universes.

What it does

skills/acceptance-contract/run.sh extract <zip|directory|file> \
  --out /tmp/acceptance-contract \
  --project-name oai-trial \
  --goal-mode create

Outputs:

  • acceptance_bundle.json — pydantic-validated source of truth
  • acceptance_report.jsoncreate_report.report.v1 JSON
  • acceptance_report.md — rendered through $create-report
  • IMMUTABLE_GOAL.draft.md — draft goal text or amendment proposal

Commands:

  • extract always writes a fresh draft bundle/report from the supplied source.
  • ensure validates or creates the frozen bundle and rejects stale source hashes.
  • validate validates an existing acceptance_bundle.json only and prints the progress meter.
  • status reads an existing acceptance_bundle.json and prints only the machine-readable progress meter.

Goal policy

Default behavior is draft, not mutate.

  • --goal-mode create writes a new immutable-goal draft.
  • --goal-mode amend writes an amendment proposal, not a silent edit.
  • --goal-mode none extracts requirements without goal text.

A real immutable goal should be created or amended only after human approval. That rule exists because oai-trial failed when implementation-defined tests stood in for the client brief.

Boundary

This skill extracts clear, source-backed requirements from supplied local files. It does not claim the extracted contract is complete when the source bundle is ambiguous. Ambiguity becomes open_questions[] and a Needs Changes report. It refuses repo roots by default because oai-trial failed when code-shaped checks stood in for the delivered brief.

For Battle, pass the resulting acceptance_bundle.json as the arena acceptance_floor and map each acceptance_cases[].id to one or more generator case ids in the Battle campaign profile's required_case_ids. Those mapped cases are the contract floor that must pass before fuzz or beyond-contract attacks can be credited. Do not make Battle invent the requirements.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

grahama1970/agent-ecosystem

Canonical map and shared contracts for the agent-governance ecosystem: the pi.receipt_envelope.v1 boundary envelope, the component graph, and the rules for which component owns which schema. Use when wiring a skill or extension into the shared receipt world, when asking how shame, triage-error, tau, ask, project-watchdog, ops-herdr, ponytail, and Memory fit together, or when validating an envelope.

grahama1970/agentic-evals

Agentic evaluation of skills using multi-trial fixtures, deterministic command assertions, trajectory checks, safety constraints, and evidence-backed readiness scoring. Use when users ask for agentic evals, multi-trial skill evaluation, skill trajectory validation, or readiness scoring for a skill workflow.

grahama1970/agent-inbox

File-based inter-agent messaging with headless dispatch. Check inbox, send bugs/requests to other projects, automatically spawn headless agents to fix bugs, and track progress via task-monitor.

grahama1970/agents-registry

Generate and query the centralized agent identity registry. Scans .pi/agents/*/AGENTS.md, parses frontmatter, outputs agents-registry.json and optionally syncs to /memory for semantic search.

grahama1970/agent-status

Artifact-driven status surfaces for long-running project-agent work. Maintains status.json, events.jsonl, proof manifests, and a stale-aware STATUS.html so humans can tell where the agent is, what passed, what is still unproven, and what decision or action is next — without dashboard theater.

grahama1970/align

Round-based context alignment before execution. Use when the human, project agent, WebGPT, scillm, ask, dogpile, memory, or project-knowledge may each hold different facts about a task; especially before ambiguous design, infographic, product workflow, high-stakes implementation, plan-iterate, project-infographic, or multi-review work.

grahama1970/analytics

Flexible data science analytics for any dataset. Auto-discovers schema, recommends charts, exports to create-figure. Works with JSONL, JSON, CSV from any source.

grahama1970/analyze-chatterbox-emotions

Evaluate generated Chatterbox voice files as voice-quality artifacts: affect match, arousal/valence proxies, pause placement, intelligibility inputs, clipping, loudness, and discontinuity flags. Use when reviewing Chatterbox emotional tags, pauses, Turbo/base affect delivery, Persona Dream utterance renders, or whether generated speech matches an intended product-facing affect.

grahama1970/analyze-elf

Reverse-engineer features from ELF binaries. Extracts CLI commands, state machines, protocols, Zod schemas, and data models. Automatically generates a /create-walkthrough prosecution brief with Mermaid diagrams. Uses /treesitter for AST analysis of bundled JS/TS source.

grahama1970/animation-vocabulary

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.

grahama1970/anonymize-data

Anonymize supported CSV, JSON, UTF-8 text, and SQLite files using an explicit policy through the oai-trial project. Use for anonymize data, pseudonymize exports, redact policy literals, or discover and explicitly approve fuzzy name aliases. The skill is a thin CLI/Docker interface, not another engine.

grahama1970/anvil

Heavy-duty "No-Vibes" debugging and hardening orchestrator. Use this for complex, stubborn bugs where `review-code` has failed, or for "Red Teaming" (hardening) a codebase. Runs multiple agents in parallel (Thunderdome) using git worktree isolation.

grahama1970/apple-design

Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

grahama1970/argue

Multi-persona structured debate orchestrator. Personas research via /dogpile, consult colleagues via /ask, and argue toward nuanced synthesis on complex questions.

grahama1970/arxiv

Search arXiv for papers and extract knowledge into memory. Use `search` to find papers, `learn` to extract knowledge.

grahama1970/ask

Use when the user asks to query project memory, ask an oracle, use supported browser-backed reviewers, run Tau roundtable/single-handler workflows, ask Pi-native subagents from within Pi, run persona/deep-review workflows, generate image prompts, check OS/project health through composed skills, or run an ask DAG. This skill is the executable /ask runtime; do not replace it with an informal subagent, plain web search, or hand-written review; inside Pi, explicit Pi-native subagent targets are routed through the pi-subagents tool as an Ask target type.

grahama1970/assess

Step back and critically reassess project state. Use when asked to "assess", "step back", "fresh eyes", "check alignment", "sanity check", "health check", "prune documentation", or "evaluate what's working". Offers documentation pruning and doc-code alignment analysis. Offer to run after major changes (don't auto-run).

grahama1970/assistant

Shared GPT + classifier inference gateway for persona monitor tasks. Routes validation and classification through a 4-tier cascade: heuristic → classifier → local GPT → scillm.

grahama1970/assistant-lab

Self-improvement workbench for /assistant. All the tools needed to diagnose, train, evaluate, and promote models in a continuous loop. The "warm pond" where /assistant evolves its own inference stack.

grahama1970/batch-quality

Pre-flight validation and quality gates for batch LLM operations. ACTUALLY tests samples through LLM before burning tokens. Uses SPARTA contracts for DuckDB validation queries. Integrates with task-monitor for enforced quality gates.

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