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ai-goal

Use when the user wants to build in an autonomous loop — "loop this feature", "run these overnight", "write the goal for this" — pinning the loop contract: what

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Price unconfirmed★ 55 GitHub starsRegistry updated · Oct 9, 2026agent-skill

Overview

Use when the user wants to build in an autonomous loop — "loop this feature", "run these overnight", "write the goal for this" — pinning the loop contract: what it consumes, which gates it closes, when it stops, and what it reports, without ever reimplementing the loop itself (the surface's native goal mode runs it). Not for one-shot verification — use /ai-verify. Not for writing the plan the loop executes — use /ai-plan.

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ai-goal — the loop contract (Loop Engineering)

The loop is NOT reimplemented: the surface's native goal mode runs it. This skill pins the loop contract from three source skills that cover the full method: feature setup with two human stops (mock and approval), the goal condition for one feature, and an overnight multi-feature queue.

Feature setup: folder, approved mock, re-entrant spec → new-feature/SKILL.md
The goal condition for one feature: pointer, reporting clause, met condition → goal-writer/SKILL.md
Multi-feature queue with a builder subagent + adversary verifier → feature-batch/SKILL.md

Step 0 — find the contract, never be told it

A fresh session is launched as /ai-goal and nothing else. The slot is the briefing, so discovery comes first, in this order, before any step is taken:

ai-eng doctor — the spec slot line is the gate. contract approved (sha256 in lock) means there is a loop to run. live spec.html WITHOUT approval means STOP 1 is pending: the loop does not start, and it says so. No spec at all means nothing to execute.
.ai-engineering/spec.html — the WHAT, immutable once approved (self-protect denies the edit, and re-opening it costs a human). Read its 00 Context chain block first: it links the research and the handshake doc this milestone consumes, so those are read before the first step, never after the first failure.
.ai-engineering/plan.html — the HOW. The loop consumes it, takes the first step that is not green, and marks it in place as gates close. This file is the agent's workbench and is exempt from self-protect; spec.html is not.
.ai-engineering/brainstorm.md — the handshake doc behind the gates: the decisions already taken, the ones still open, and what is out of scope. A step that depends on an open unknown stops and asks; it never invents the answer to keep moving. This slot lives beside spec.html and plan.html and dies with them at spec close, so the loop reads it while it is live and never writes a second map next to it.
[budget] in .ai-engineering/config.toml — the ceiling and the stop list. An absent section means no pinned ceiling: say it once, then the human is the clock.

None of this belongs in the prompt. What the loop needs lives in the slot, which is why /ai-goal by itself is the whole invocation.

Lifecycle

Lane: standard, full Writes: .ai-engineering/plan.html Read by: the human, the next iteration of the loop Dies: with the milestone, when plan.html dies Next: ai-proof after each step, then the closing chain — ai-verify, the fired triggers and ai-visual-recap — once plan.html is fully green (see The closing chain)

Source: Loop-Engineering, Loop Salon demo repo (attributed — no author or URL declared upstream; open license issue H4).

The ai-engineering seam

The loop is NOT reimplemented: a surface with a native goal mode runs it (contract mode), and a surface without one runs under facilitator mode below — ai-goal fixes the contract either way.
It consumes .ai-engineering/plan.html and closes .ai-engineering/spec.html gates with receipts (ai-eng spec run). Each iteration marks that artifact in place: the markup and the style come from ai-design › references/artifact-design.md, and the loop adds none of its own.
The three stops of blueprint §5.1 become exit conditions: approved contract / loop guard deny / destructive action above budget.
The token/turn budget comes from .ai-engineering/config.toml ([budget]), not from the conversation.
The closing report is produced by ai-visual-recap (.ai-engineering/recap.html) — step 5 of the closing chain below, which the loop runs to the end rather than stopping at the last green gate.
The "enough" criterion is KISS/YAGNI: the smallest thing that closes the gate.

The closing chain

Green gates are not the end of the loop. The loop ends here, in this order, and it yields nowhere inside this chain except at one of the three stops:

ai-eng spec run — every gate, rechecked. A red gate is the next step, never a report.
Read the greens. A CHECK that cannot fail for the reason its own text states is a finding about the contract, not evidence: an ls of an artifact any earlier run already satisfies, a proof script whose case is never exercised, a -t filter that matches no test. Name it to the human with the gate id and what would settle it. spec.html is approved, so that CHECK is theirs to reopen — never the loop's to quietly count as met.
ai-verify — the requirement-level pass, run by an agent that did not build it: one verdict per requirement (MET / PARTIAL / DIVERGED / MISSING / UNASKED / UNVERIFIABLE), each with file:line and the command that proves it. DIVERGED is a decision for the human, not a fix.
The fired triggers' own artifacts, per the spec's trigger list — security → ai-security (findings.json + REPORT.md), public-interface → ai-write, arch-change → the arch rules and their test. A fired trigger left without its artifact keeps the milestone open, whatever the gates say.
ai-visual-recap → .ai-engineering/recap.html. The loop does not write this file by hand: the recap is that skill's artifact, and its markup comes from the artifact design, never from the loop's taste.
Hand over the close. ai-eng spec close is the human's: it is the act that unfreezes the contract, and un-freezing a contract nobody signed off is not the loop's call.

In facilitator mode the chain is the same, run once at the end of the plan. Between steps it is still one step, its receipt, then stop.

Facilitator mode

The surface's declared capability decides which mode applies. A surface that declares a native goal loop runs contract mode: this skill pins the contract and the loop executes it. A surface that declares none is not handed off to a loop — this skill runs facilitator mode itself and walks .ai-engineering/plan.html one step at a time inside the session.

Facilitator mode changes who advances the loop, not what the loop guarantees. The same three stops apply (approved contract / loop guard deny / destructive action above the [budget]), every closed gate gets the same receipt (ai-eng spec run), and the human is the clock: one step, its receipt, then stop and wait. No timer, no unattended queue, no implicit approval.

An unverified surface runs facilitator mode until a receipt says otherwise. Native goal capability is a claim like any other, and the first honest receipt is what upgrades it; until that receipt exists, the slow path is the true one.

Routing

In scope: autonomous construction loops, overnight batches, goal contracts with gates and stop conditions. Not for: diagnosing a failure (/ai-debug), verifying a finished diff (/ai-verify), deciding what to build (/ai-brainstorm then /ai-plan).

View source ↗

File metadata

name: ai-goal
description: >-
  Use when the user wants to build in an autonomous loop — "loop this feature", "run these
  overnight", "write the goal for this" — pinning the loop contract: what it consumes, which
  gates it closes, when it stops, and what it reports, without ever reimplementing the loop
  itself (the surface's native goal mode runs it). Not for one-shot verification — use
  /ai-verify. Not for writing the plan the loop executes — use /ai-plan.
license: MIT

View original text

---
name: ai-goal
description: >-
Use when the user wants to build in an autonomous loop — "loop this feature", "run these
overnight", "write the goal for this" — pinning the loop contract: what it consumes, which
gates it closes, when it stops, and what it reports, without ever reimplementing the loop
itself (the surface's native goal mode runs it). Not for one-shot verification — use
/ai-verify. Not for writing the plan the loop executes — use /ai-plan.
license: MIT
---

# ai-goal — the loop contract (Loop Engineering)

The loop is NOT reimplemented: the surface's native goal mode runs it. This skill pins the
loop contract from three source skills that cover the full method: feature setup with two
human stops (mock and approval), the goal condition for one feature, and an overnight
multi-feature queue.

- Feature setup: folder, approved mock, re-entrant spec → [new-feature/SKILL.md](new-feature/SKILL.md)
- The goal condition for one feature: pointer, reporting clause, met condition → [goal-writer/SKILL.md](goal-writer/SKILL.md)
- Multi-feature queue with a builder subagent + adversary verifier → [feature-batch/SKILL.md](feature-batch/SKILL.md)

## Step 0 — find the contract, never be told it

A fresh session is launched as `/ai-goal` and nothing else. The slot is the briefing, so
discovery comes first, in this order, before any step is taken:

1. `ai-eng doctor` — the `spec slot` line is the gate. `contract approved (sha256 in lock)`
means there is a loop to run. `live spec.html WITHOUT approval` means STOP 1 is pending:
the loop does not start, and it says so. No spec at all means nothing to execute.
2. `.ai-engineering/spec.html` — the WHAT, immutable once approved (self-protect denies the
edit, and re-opening it costs a human). Read its `00 Context chain` block first: it links
the research and the handshake doc this milestone consumes, so those are read before the
first step, never after the first failure.
3. `.ai-engineering/plan.html` — the HOW. The loop consumes it, takes the first step that is
not green, and marks it in place as gates close. This file is the agent's workbench and is
exempt from self-protect; spec.html is not.
4. `.ai-engineering/brainstorm.md` — the handshake doc behind the gates: the decisions already
taken, the ones still open, and what is out of scope. A step that depends on an open unknown
stops and asks; it never invents the answer to keep moving. This slot lives beside spec.html
and plan.html and dies with them at `spec close`, so the loop reads it while it is live and
never writes a second map next to it.
5. `[budget]` in `.ai-engineering/config.toml` — the ceiling and the stop list. An absent
section means no pinned ceiling: say it once, then the human is the clock.

None of this belongs in the prompt. What the loop needs lives in the slot, which is why
`/ai-goal` by itself is the whole invocation.

## Lifecycle

Lane: standard, full
Writes: .ai-engineering/plan.html
Read by: the human, the next iteration of the loop
Dies: with the milestone, when plan.html dies
Next: ai-proof after each step, then the closing chain — ai-verify, the fired triggers and ai-visual-recap — once plan.html is fully green (see The closing chain)

Source: Loop-Engineering, Loop Salon demo repo (attributed — no author or URL declared
upstream; open license issue H4).

## The ai-engineering seam

1. The loop is NOT reimplemented: a surface with a native goal mode runs it (contract mode),
and a surface without one runs under facilitator mode below — ai-goal fixes the contract
either way.
2. It consumes `.ai-engineering/plan.html` and closes `.ai-engineering/spec.html` gates with
receipts (`ai-eng spec run`). Each iteration marks that artifact in place: the markup and
the style come from [ai-design › references/artifact-design.md](../ai-design/references/artifact-design.md), and the loop adds none of its own.
3. The three stops of blueprint §5.1 become exit conditions: approved contract / loop guard
deny / destructive action above budget.
4. The token/turn budget comes from `.ai-engineering/config.toml` (`[budget]`), not from the
conversation.
5. The closing report is produced by ai-visual-recap (`.ai-engineering/recap.html`) — step 5
of the closing chain below, which the loop runs to the end rather than stopping at the
last green gate.
6. The "enough" criterion is KISS/YAGNI: the smallest thing that closes the gate.

## The closing chain

Green gates are not the end of the loop. The loop ends here, in this order, and it yields
nowhere inside this chain except at one of the three stops:

1. **`ai-eng spec run`** — every gate, rechecked. A red gate is the next step, never a
report.
2. **Read the greens.** A CHECK that cannot fail for the reason its own text states is a
finding about the contract, not evidence: an `ls` of an artifact any earlier run already
satisfies, a proof script whose case is never exercised, a `-t` filter that matches no
test. Name it to the human with the gate id and what would settle it. `spec.html` is
approved, so that CHECK is theirs to reopen — never the loop's to quietly count as met.
3. **ai-verify** — the requirement-level pass, run by an agent that did not build it: one
verdict per requirement (MET / PARTIAL / DIVERGED / MISSING / UNASKED / UNVERIFIABLE),
each with file:line and the command that proves it. `DIVERGED` is a decision for the
human, not a fix.
4. **The fired triggers' own artifacts**, per the spec's trigger list — `security` →
ai-security (`findings.json` + `REPORT.md`), `public-interface` → ai-write,
`arch-change` → the arch rules and their test. A fired trigger left without its artifact
keeps the milestone open, whatever the gates say.
5. **ai-visual-recap** → `.ai-engineering/recap.html`. The loop does not write this file by
hand: the recap is that skill's artifact, and its markup comes from the artifact design,
never from the loop's taste.
6. **Hand over the close.** `ai-eng spec close` is the human's: it is the act that unfreezes
the contract, and un-freezing a contract nobody signed off is not the loop's call.

In facilitator mode the chain is the same, run once at the end of the plan. Between steps it
is still one step, its receipt, then stop.

## Facilitator mode

The surface's declared capability decides which mode applies. A surface that declares a
native goal loop runs **contract mode**: this skill pins the contract and the loop executes
it. A surface that declares none is not handed off to a loop — this skill runs **facilitator
mode** itself and walks `.ai-engineering/plan.html` one step at a time inside the session.

Facilitator mode changes who advances the loop, not what the loop guarantees. The same three
stops apply (approved contract / loop guard deny / destructive action above the `[budget]`),
every closed gate gets the same receipt (`ai-eng spec run`), and the human is the clock: one
step, its receipt, then stop and wait. No timer, no unattended queue, no implicit approval.

An `unverified` surface runs facilitator mode until a receipt says otherwise. Native goal
capability is a claim like any other, and the first honest receipt is what upgrades it;
until that receipt exists, the slow path is the true one.

## Routing

In scope: autonomous construction loops, overnight batches, goal contracts with gates and
stop conditions. Not for: diagnosing a failure (/ai-debug), verifying a finished diff
(/ai-verify), deciding what to build (/ai-brainstorm then /ai-plan).

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Review before install: Avoid automatic install

License: MIT

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Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 55 GitHub stars
Stars/forks activity: 55 stars, 3 forks; issue activity unavailable in current metadata
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Source & usage notes

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Source repository: arcasilesgroup/ai-engineering
License: MIT
Version: Unknown
Last GitHub push: Sep 16, 2026
Registry updated: Oct 9, 2026
Instruction path: skills/ai-goal/SKILL.md @ 954a7b827fc6

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Quality

59/100

Promising

Trust

64/100

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Audit

75/100

Needs review

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Permission surface may require sandboxing
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GitHub adoption: 55 GitHub stars
Stars/forks activity: 55 stars, 3 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Permission surface may require sandboxing",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 55 GitHub stars",
      "Stars/forks activity: 55 stars, 3 forks; issue activity unavailable in current metadata",
      "Permission surface: secrets or environment access, shell or command execution",
      "Review status: AI review approval is missing"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 59,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Browser automation",
    "maintenance": "28d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use ai-goal in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 72/100 Strong shortlist",
      "Audit: 75/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "arcasilesgroup-ai-goal (ai-goal)",
      "install_command": "npx skills add arcasilesgroup/ai-engineering --skill ai-goal",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "arcasilesgroup-ai-goal",
      "task": "Use ai-goal in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/arcasilesgroup-ai-goal",
    "api": "https://www.openagentskill.com/api/agent/skills/arcasilesgroup-ai-goal",
    "audit": "https://www.openagentskill.com/skills/arcasilesgroup-ai-goal/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=arcasilesgroup-ai-goal&task=Use%20ai-goal%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-goal%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ai-goal%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/arcasilesgroup-ai-goal/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/arcasilesgroup-ai-goal"
  }
}

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Creator: arcasilesgroup
Source: arcasilesgroup/ai-engineering
Indexed by: OpenAgentSkill community index

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Overview

ai-goal — the loop contract (Loop Engineering)

Step 0 — find the contract, never be told it

Lifecycle

The ai-engineering seam

The closing chain

Facilitator mode

Routing

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Price & running costs

Start with one small task

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