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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.

What is agent-status?

agent-status is a Antigravity agent skill that 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.

Works with~Claude Code~Codex CLI~CursorAntigravityOpenCode
npx skills add https://github.com/grahama1970/agent-skills/tree/main/skills/agent-status

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Documentation

What does agent-status do?

Use this skill whenever a project agent is doing multi-step work and the human needs to know what is actually happening.

This skill is intentionally not a dashboard generator. It produces a truth surface backed by machine-readable artifacts:

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

The HTML only renders the current status artifact. It must not invent progress, percentages, or completion claims.

Core principle

status artifact first → HTML renders artifact → human can inspect proof

Never do this:

pretty page first → vague green language → human infers progress

Required first-viewport answers

A generated status page must answer:

  1. What is the agent trying to finish?
  2. Where exactly are we?
  3. What just passed?
  4. What is still not proven?
  5. What is the next action?
  6. Is the agent running, blocked, idle, or waiting for the human?
  7. What proof backs the current claim?
  8. What should the agent not do next?

State model

Allowed states:

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

Important distinction:

passed_scoped_gate != done

Only use done when the final stated goal has an explicit final proof artifact.

Common commands

Initialize a campaign:

./run.sh init \
  --campaign refactor-harness-e2e \
  --goal "Finish scoped refactor harness E2E proof"

Mark a gate as running:

./run.sh update \
  --campaign refactor-harness-e2e \
  --state running \
  --current-step "Run hardened concurrent LLM + OpenCode summarize gate"

Record a scoped passing gate:

./run.sh gate-passed \
  --campaign refactor-harness-e2e \
  --label "A01-A18 hardened concurrent LLM + OpenCode summarize" \
  --verdict PASS_SPEC \
  --proof ".plan-iterate/refactor-harness-e2e/proof/refactor-concurrent-mixed.json" \
  --next-action "Start reviewer packet + reviewer fan-in phase" \
  --not-proven "Transport Room B1 UI" \
  --not-proven "build_review_packet reviewer fan-in" \
  --not-proven "Full OpenCode message delivery"

Ask the human for a decision:

./run.sh needs-human \
  --campaign refactor-harness-e2e \
  --question "Which next campaign should the agent run?" \
  --option "A=Continue harness: reviewer packet + reviewer fan-in" \
  --option "B=Transport UI: start Transport Room B1" \
  --option "C=Stabilize only: checkpoint and stop"

Render the status page again:

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

Anti-dashboard-theater rules

  1. No percentages.
  2. No green “complete” unless final goal proof exists.
  3. Every PASS must name its proof artifact.
  4. Every status must show what is not proven.
  5. Every idle state must say why the agent is idle.
  6. Every human-wait state must include a decision menu.
  7. Every blocked state must name the missing artifact, service, gate, or decision.
  8. Auto-refresh is allowed only if the page reads status.json.
  9. If status.json is stale, the page must say stale.
  10. Reviewer verdicts are receipts, not completion proof.

When to use

Use this for:

  • /plan-iterate
  • /orchestrate
  • /review-design
  • /review-code
  • /debugger
  • /ask deep-review
  • /code-runner
  • /subagent-runner
  • any multi-step project-agent work lasting more than one focused command

Artifact expectations

status.json is the source of truth.

events.jsonl is append-only and records state transitions.

proof_manifest.json lists proof artifacts named by status claims.

STATUS.html is generated, stale-aware, and safe to open in a browser.

Completion standard

A final answer for long-running agent work should summarize the current status artifact and link or quote the STATUS.html path when available.

Do not claim completion unless:

  • state is done;
  • last_completed.proof_path points to a final proof artifact;
  • not_proven is empty or explicitly out of scope;
  • blockers are empty.

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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