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grahama1970/agent-status

以工件驅動的狀態呈現工具,專為長時間運行的專案代理工作設計。能維護 status.json、events.jsonl、proof manifests 以及一個具備過期感知能力的 STATUS.html,讓人類清楚了解代理的進度、哪些項目已通過、哪些尚未驗證,以及下一步的決策或行動,無需華而不實的儀表板。

agent-status 是什麼?

grahama1970/agent-status 是一個專為長時間運行的專案代理工作(例如在 Claude Code、Coder、Codex 等平台上執行的多步驟任務)所設計的狀態呈現技能。其核心原則是「工件第一,渲染第二」:透過維護 status.json(事實來源)、events.jsonl(僅可附加的狀態轉換日誌)、proof_manifest.json(證明工件清單)和 STATUS.html(由前述工件產生的靜態網頁)等機器可讀的工件,來建立一個誠實且可稽核的事實表面(truth surface)。與常見的儀表板不同,此技能嚴格禁止使用百分比、模糊的「完成」或綠燈狀態;每個通過的門檻都必須指向明確的證明工件,每個閒置或等待人類決策的狀態都必須說明原因或提供選項,以消滅「儀表板幻象」。它支援一個包含 not_started、running、needs_attention、blocked、failed、passed_scoped_gate、done 和 idle 的狀態模型,並能透過 CLI 命令(如 run.sh init、update、gate-passed、needs-human、render)進行操作。當代理需要進行超過一個指令的複雜工作時,使用此技能能讓開發者直接透過靜態 HTML 檢視實際進度與證據,提升透明度和信任度。

相容平台~Claude Code~Codex CLI~Cursor✓Antigravity✓OpenCode
npx skills add https://github.com/grahama1970/agent-skills/tree/main/skills/agent-status

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說明文件

agent-status 是做什麼的?

每當專案代理正在執行多步驟工作,且人類需要知道實際發生情況時,請使用此技能。

此技能刻意設計為非儀表板產生器。它產生由機器可讀工件支援的事實表面:

.plan-iterate/<campaign-id>/status/
  status.json
  events.jsonl
  proof_manifest.json
  STATUS.html

HTML 僅呈現目前的狀態工件。它絕不能虛構進度、百分比或完成聲明。

核心原則

先有狀態工件 → HTML 渲染工件 → 人類可檢查證明

永遠不要這樣做:

先有華麗頁面 → 模糊的綠色文字 → 人類推測進度

第一視區必須回答的問題

產生的狀態頁面必須回答:

  1. 代理試圖完成什麼?
  2. 我們目前確切在哪個階段?
  3. 剛剛通過了什麼?
  4. 什麼尚未被證明?
  5. 下一個動作是什麼?
  6. 代理正在運行、被阻擋、空閒,還是正在等待人類?
  7. 當前聲明有何證明為依據?
  8. 代理下一步不應該做什麼?

狀態模型

允許的狀態:

  • not_started
  • running
  • needs_attention
  • blocked
  • failed
  • passed_scoped_gate
  • done
  • idle

重要區別:

passed_scoped_gate != done

只有當最終陳述的目標有明確的最終證明工件時,才使用 done。

常用指令

初始化一個任務:

./run.sh init \
  --campaign refactor-harness-e2e \
  --goal "完成範圍內的 refactor harness E2E 證明"

將閘門標記為運行中:

./run.sh update \
  --campaign refactor-harness-e2e \
  --state running \
  --current-step "運行強化後的並發 LLM + OpenCode 總結閘門"

記錄一個範圍內的通過閘門:

./run.sh gate-passed \
  --campaign refactor-harness-e2e \
  --label "A01-A18 強化後的並發 LLM + OpenCode 總結" \
  --verdict PASS_SPEC \
  --proof ".plan-iterate/refactor-harness-e2e/proof/refactor-concurrent-mixed.json" \
  --next-action "開始 reviewer 封包 + reviewer fan-in 階段" \
  --not-proven "Transport Room B1 UI" \
  --not-proven "build_review_packet reviewer fan-in" \
  --not-proven "完整 OpenCode 訊息傳遞"

向人類請求決策:

./run.sh needs-human \
  --campaign refactor-harness-e2e \
  --question "代理下一步應該運行哪個任務?" \
  --option "A=繼續 harness: reviewer 封包 + reviewer fan-in" \
  --option "B=Transport UI: 開始 Transport Room B1" \
  --option "C=僅穩定化: 設立檢查點並停止"

再次渲染狀態頁面:

./run.sh render --campaign refactor-harness-e2e

反儀表板幻象規則

  1. 無百分比。
  2. 除非有最終目標證明存在,否則不得顯示綠色「完成」。
  3. 每個 PASS 都必須指名其證明工件。
  4. 每個狀態都必須顯示哪些項目尚未被證明。
  5. 每個空閒狀態都必須說明代理為何空閒。
  6. 每個等待人類的狀態都必須包含一個決策選單。
  7. 每個被阻擋的狀態都必須指名缺失的工件、服務、閘門或決策。
  8. 僅當頁面讀取 status.json 時,才允許自動重新整理。
  9. 如果 status.json 過期,頁面必須顯示過期。
  10. 審查者的判決是收據,而非完成證明。

使用時機

在以下情況使用此技能:

  • /plan-iterate
  • /orchestrate
  • /review-design
  • /review-code
  • /debugger
  • /ask deep-review
  • /code-runner
  • /subagent-runner
  • 任何持續時間超過一個專注指令的多步驟專案代理工作

工件期望

status.json 是事實來源。

events.jsonl 是僅可附加的,記錄狀態轉換。

proof_manifest.json 列出狀態聲明所提及的證明工件。

STATUS.html 是產生的、具備過期感知能力,且可在瀏覽器中安全開啟。

完成標準

對於長時間運行的代理工作,最終答案應總結目前的狀態工件,並在可能時連結或引用 STATUS.html 路徑。

除非滿足下列條件,否則請勿聲明完成:

  • 狀態為 done;
  • last_completed.proof_path 指向一個最終證明工件;
  • not_proven 為空或明確標示為範圍之外;
  • blockers 為空。

Individual skills in this repo

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

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.

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/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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