Convert Prompt To Saill

Skill: /convert-prompt-to-saill

Model preference: #midcost (per horizon_aios_model_prefs.md; overridable by a prompt directive).

Turn "here's my prompt" into its SAILL form — the standard, shareable agent-team notation (roles + model groups + flags + boxes + loops + -context-). The deliverable is the compact, portable representation behind a plain-English request, which the user can then run, save as a team, or share. Authoritative grammar: documentation/system/agent_teams.md (SAILL, §8) and the flag catalog $HORIZON_ETC/agent_team_flags.md.

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When to invoke

/convert-prompt-to-saill , or the user asks to turn a prompt into SAILL / express work as an agent team.

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Step 1 — Load the vocabulary (do not invent flags)

1.1 Recognized flags: python $HORIZON_BIN/resolve_agent_teams.py --flags. 1.2 Existing teams (reuse one if it fits): python $HORIZON_BIN/resolve_agent_teams.py --json. Use only flags in the registry and #groups defined in horizon_aios_model_prefs.md.

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Step 2 — Analyze the prompt

Decompose the prompt into a flow:

1. Roles — the distinct units of work; for each, a short charter (what it does).
2. Order — sequential vs concurrent; mark concurrency with parallel + a following wait.
3. Iteration — "until it passes / retry" → a **Loop:** back to a named role, with a cap.
4. Gates — "ask me / approve first" → ask user; "only if needed" → if needed; "only if I ask" → if asked.
5. Sub-agents / nesting — group related roles in a box [ … ]; name it for an ephemeral sub-team Name[ … ]; nest as deep as the prompt implies.
6. Context values — anything left to runtime (a scope, a pass condition, a cap) → -context- / -context-.

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Step 3 — Map to SAILL

3.1 If the prompt matches a shipped team (e.g. "investigate and fix X"), reuse it by name and add only the prompt's specifics (scope, flags) — do not reinvent.

3.2 Otherwise compose roles. Assign each a #group by the kind of work (mirror horizon_aios_model_prefs.md Task-Class Routing): security / architecture / review → #highcap; research / exploration → #investigate; mechanical / edits / summarizing → #lowcost; debugging / coding → #debug; otherwise → #midcost.

3.3 Apply the flags, boxes, loops, and -context- from Step 2. Prefer named loop targets and the most one-line-readable form that stays faithful.

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Step 4 — Output

4.1 The SAILL block — either the agent_teams.md role format (### Name + numbered roles) or a one-line box expression, whichever is clearer for this flow.

4.2 A short gloss: map each role / flag back to the phrase in the prompt it came from.

4.3 Call out any -context- placeholders and what fills them at run time.

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Step 5 — Offer next steps

Offer to save it as a reusable team (/agent-teams), run it ("send this team …"), or test it (/test-agent-teams ). Do NOT execute the work here — this skill only converts.

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Notes for the executing agent

1. Use ONLY flags from the registry and #groups from model-prefs — never invent vocabulary.

If the prompt needs a new one, offer /agent-teams (add a flag) or /model-prefs (add a group).

1. SAILL governs control flow; a role's work is charter prose (§1.2) — keep "run skill X" /

"scope to Y" in the charter or as -context-, never as a flag.

1. Keep the output terse and human-readable — the value of SAILL is a compact, shareable form.
2. This converts only; it never runs the flow.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: HorizonBrute
• Source: HorizonBrute/StandardizedAILooping_Language-SAILL
• License: MIT
• Homepage: https://horizonbrute.github.io/StandardizedAILooping_Language-SAILL/

Install and usage instructions live in the source repository linked above.