signal-write

$npx mdskill add github/awesome-copilot/signal-write

Emits structured agent signals

  • Solves desk-operator communication
  • Depends on JSON and journal APIs
  • Decides based on desk status
  • Delivers signals to dashboard

SKILL.md

.github/skills/signal-writeView on GitHub ↗
---
name: signal-write
description: 'Emit structured agent signals — hands-up, blocked, done, checkpoint, partnership. Signals are written as JSON to .signals/ for dashboard consumption and noted in the journal for persistence.'
---

# Agent Signals

Emit structured signals from a desk to the operator or other desks.

## When to use

- A desk needs operator attention (hands-up, blocked)
- Work is complete and ready for review (done)
- Significant progress worth noting (checkpoint)
- Two desks disagree and can't resolve it (hands-up)
- The TA is reporting coordination quality (partnership)

## Signal types

### `hands-up`
Two desks disagree and can't settle it against external facts.
This is the system working — the operator reads where desks
*disagree*, not where they perform confidence.

### `blocked`
A desk can't proceed without input — missing access, ambiguous
scope, need a decision only the operator can make.

### `done`
Work is complete and ready for review. Artifacts are on the bench.

### `checkpoint`
Significant progress worth the operator knowing about, but work
continues. Not blocked, not done — just a marker.

### `partnership`
Used by the TA (room coordinator) to report coordination quality.
Self-assessment scores reflect coordination, not code accuracy:
- **intent** — understood what the operator needed
- **confidence** — right work went to the right desks
- **accuracy** — dispatched work produced the right outcome
- **completeness** — nothing fell through the cracks

## How to emit

### 1. Write a JSON signal file to `.signals/`

This is the primary output — it's what the dashboard reads.
Create `desks/<desk-name>/.signals/<timestamp>.json`:

```json
{
  "signal_type": "execution",
  "subtype": "checkpoint",
  "timestamp": "2026-07-19T21:30:00Z",
  "run_id": "<optional; set to pair this with an outcome signal>",
  "agent_name": "<desk-name>",
  "self_assessment": {
    "intent": 4,
    "confidence": 5,
    "accuracy": 4,
    "completeness": 3
  },
  "patterns": {
    "what_worked": "description of what went well",
    "what_was_hard": "description of challenges",
    "skill_gap": "areas for improvement"
  },
  "escalation": {
    "reason": null,
    "blocked_on": null,
    "recommendation": null
  }
}
```

### Signal type mapping

| Signal    | `signal_type`   | `subtype`      |
|-----------|-----------------|----------------|
| hands-up  | `"escalation"`  | `"hands-up"`   |
| blocked   | `"escalation"`  | `"blocked"`    |
| done      | `"execution"`   | `"done"`       |
| checkpoint| `"execution"`   | `"checkpoint"` |
| partnership| `"partnership"` | `"partnership"`|

The `subtype` field preserves the specific signal state for
dashboard consumers. `signal_type` controls sort priority
(escalation → top).

> **Note:** The signals-dashboard canvas extension reads `subtype`
> when present and falls back to `signal_type` for display. If
> consuming signals in your own tooling, prefer `subtype` for the
> specific state.

> **Ordering:** include a `timestamp` (ISO 8601 UTC). The dashboard
> orders signals by it and falls back to file mtime only when it's
> absent — a git clone/checkout resets mtimes, so mtime alone is not a
> dependable clock.

### 2. Note the signal in the journal

Also append a short marker to the desk's journal for persistence:

```markdown
## <date> — [signal:<type>] <summary>
- <key details>
```

The journal note is the trail marker. The JSON file is the
machine-readable signal.

## Outcome signals (calibration)

The signals-dashboard can pair a desk's self-assessment with an
*outcome* — an independent rating of the realized result — and show
the **honesty gap** (how far the desk's confidence was from the
delivered quality). Outcome signals are optional and are usually
emitted by a reviewer/evaluator, not the desk itself.

Write them to the **same** `.signals/` directory:

```json
{
  "signal_type": "outcome",
  "run_id": "<same run_id as the signal it rates>",
  "agent_name": "<reviewer name>",
  "quality_rating": 4,
  "effort_to_merge": "minimal",
  "issues_found": ["optional short strings"],
  "timestamp": "2026-07-19T22:00:00Z"
}
```

- **`run_id`** correlates an outcome with the execution/partnership
  signal it rates — set the same `run_id` on both. If it's absent, the
  dashboard falls back to the nearest outcome emitted shortly after the
  latest signal.
- **`quality_rating`** (0–5) is the realized quality; the dashboard
  compares it to the desk's self-assessed `confidence` to compute the
  honesty gap.
- **`effort_to_merge`** — `"minimal"`, `"moderate"`, or `"significant"`.
- **`issues_found`** — optional array of short strings.

## Principles

- Signals are structured, not chatty. Short, factual, actionable.
- hands-up is not failure — it's the most valuable signal. It
  means the system caught something one frame alone would have
  missed.
- Don't signal for routine progress. Signals are for state
  changes that affect the room, not status updates.
- blocked means truly blocked — not "I'd prefer input." If you
  can proceed with a reasonable default, proceed and note it.
- Self-assessment scores should be honest, not optimistic. A 3/5
  is fine. A 5/5 on everything is suspicious.

More from github/awesome-copilot

SkillDescription
acquire-codebase-knowledgeUse this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
acreadiness-assessRun the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
acreadiness-generate-instructionsGenerate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.
acreadiness-policyHelp the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
ad-campaign-analyzerUse this skill when the user shares ad campaign performance data and asks what to cut, scale, or test. Trigger for prompts like "analyze my ad campaigns", "where am I wasting ad spend", "reallocate my ad budget", "which ads are actually working", or "ROAS analysis". Do not trigger for campaign planning or creative generation without performance data.
add-educational-comments'Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.'
adobe-illustrator-scriptingWrite, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
agent-architectureDesign AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review.
agent-governance|
agent-owasp-compliance|