Write, edit, refactor, or audit prompt-first agent skills so they stay simple by default, self-contained, anti-heuristic, and distinct from nearby peers. Use when a Codex, OpenClaw, OpenAI, Anthropic, or repo-local skill needs stronger intent, trigger boundaries, packaging, references, runtime metadata, or validation before shipping.
Instalación
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Write, edit, refactor, or audit prompt-first agent skills so they stay simple by default, self-contained, anti-heuristic, and distinct from nearby peers. Use when a Codex, OpenClaw, OpenAI, Anthropic, or repo-local skill needs stronger intent, trigger boundaries, packaging, references, runtime metadata, or validation before shipping.
Use this skill when the work is creating or repairing an agent skill, not generic documentation.
Skills are reusable prompt contracts first. Default to the smallest prompt-only
package that preserves the user's intent and leaves room for agent judgment.
This skill is intentionally self-contained. Use the references in this folder
and, for agent-using packages, the installed sibling shared policy named below;
do not rely on external repo docs while doing the runtime work.
Install
git clone git@github.com:aelaguiz/arch_skill.git
cd arch_skill
make install
When to use
The user wants a new shared or local skill with a real SKILL.md contract.
An existing skill needs better triggers, scope boundaries, packaging, or references.
The user wants to decide whether a workflow should be a skill rather than a prompt, repo note, or ordinary doc.
A skill's role is blurry relative to sibling skills, a suite guide, or a nearby tool wrapper.
The user wants a findings-first audit of a skill's leverage, progressive disclosure, self-containment, or validation plan.
The user wants to refactor an overgrown or heuristic skill without losing useful workflow knowledge.
The user wants to remove accidental scripts, parameters, fake blockers, or harness behavior from a skill that should be simple prompt guidance.
The target runtime is OpenClaw and the skill needs slash-command behavior, loader gating, metadata rules, or per-agent/shared installation guidance.
When not to use
The task is ordinary copy editing, summarization, or README writing rather than skill design.
The right artifact is repo-local AGENTS.md guidance, a project plan, or a one-shot prompt rather than a reusable skill.
The workflow is so small or one-off that the packaging overhead would exceed the leverage.
The work is only an OpenClaw config or tools.allow change; capabilities and permissions still live in the host runtime, not in skill prose.
The existing skill package or binding brief cannot be inspected.
Non-negotiables
Start from 2-3 concrete user asks and the leverage claim before designing files.
Decide whether a skill is the right mechanism before writing one.
Apply $prompt-authoring discipline to all skill prose, including SKILL.md, references, bundled agents, and output contracts.
Default to simple prompt engineering. Add references, scripts, or harness behavior only after the lean prompt contract proves insufficient.
Generalize from the user's intent, not from the first concrete incident. Preserve common-sense interpretation instead of adding formal inputs for natural-language asks.
Teach reusable workflow, judgment, and invariants; do not replace reasoning with slogans, keyword rules, canned menus, or giant checklists.
Treat the description field as runtime trigger logic, not marketing copy.
Keep the frontmatter description inside the runtime length cap; treat over-1024-character descriptions as invalid unless the target runtime documents a stricter cap.
Shape trigger boundaries against the visible peer group when related skills exist; do not judge a skill only in isolation.
Encode runtime-specific behavior in machine-readable fields when the host supports them; do not hide load, gating, or invocation rules only in prose.
Keep the shipped skill self-contained; do not depend on repo docs, hidden context, or local prompt packs at runtime.
If a skill creates, resumes, replaces, or coordinates model agents, make the running agent read the installed sibling ../_shared/agent-orchestration-policy.md before dispatch and apply $prompt-authoring to the actual populated child brief. That policy owns shared transport, starting-context, brief-authority, continuation, isolation, topology, and integration semantics; the skill should retain only its role, domain judgment, task slicing, handoffs, and result contract.
Do not copy a local orchestration mini-policy into an agent-using skill or add a dispatcher, controller, or skill-local script that owns those cross-skill decisions. Runtime adapters may still own narrow deterministic invocation and receipt mechanics when that is their actual job.
Keep SKILL.md lean and move heavy detail into references/.
Add scripts/ only when deterministic reliability or repeated complexity justifies them, and record why a simple prompt-only package is not enough.
Do not create a runner, launcher, controller, or formal parameter schema unless the user explicitly asked for orchestration or the workflow genuinely cannot be expressed as prompt guidance.
Treat any script the skill ships as part of the agent's prompt budget; default to a high-signal verdict and a handle for detail, not an inline blob.
Debug undertriggering, overtriggering, and poor execution separately.
When an edit changes a skill's visible contract, inspect and co-edit the relevant agents/openai.yaml metadata, especially its default prompt, instead of letting runtime metadata drift from SKILL.md.
Validate the skill on representative tasks without leaking the intended answer into the evaluation surface.
If the brief or existing package is missing, stop and say so instead of inventing a skill contract.
First move
Classify the job as author, edit, refactor, or audit.
Read references/skill-pattern-contract.md.
Apply $prompt-authoring best practices: single job, mission-level intent, success/failure, section-correct guidance, and anti-heuristic checks.
If the target skill dispatches model agents, read ../_shared/agent-orchestration-policy.md and separate shared orchestration semantics from the skill's role-local workflow before editing.
Read the smallest additional reference that matches the job:
references/workflow-and-modes.md for mode routing and output expectations
references/leverage-and-scope.md for deciding what the skill should own and whether it should exist at all
references/peer-groups-and-boundaries.md when related sibling skills, suite routing, or overlap are in scope
references/packaging-trigger-and-validation.md for file layout, trigger quality, and shipping checks
references/openclaw-skills.md when the target runtime is OpenClaw or mirrors OpenClaw skill semantics
references/script-output-economy.md when the skill ships a script, command wrapper, or tool whose stdout the agent will read
references/examples-and-anti-examples.md when you need grounded examples or want to sanity-check framing
Workflow
Lock the job-to-be-done, the leverage claim, and 2-3 canonical user asks before touching wording.
Choose the right mechanism: skill, prompt, AGENTS.md, plan doc, or ordinary docs.
Start with the lean prompt-only shape. Add references only when they reduce repeated prompting or keep SKILL.md small.
If the skill has visible peers, name the target lane and the nearest lookalike before rewriting trigger prose.
Define scope, one strong out-of-scope lookalike, and the runtime boundaries.
For an agent-using skill, point to the shared policy once, require
$prompt-authoring on the actual dispatch brief, keep role-local doctrine
in the owning skill, and leave launch-time choices to the agent applying
both contracts.
Write the trigger description so it says what the skill does, when to use it, and when not to.
Encode any runtime-specific load, gating, slash-command, or invocation behavior in the runtime's machine-readable schema before treating the prose as done.
Build the minimum viable package: SKILL.md first, then only the references/, scripts/, assets/, and agents/ metadata the workflow truly needs.
Co-edit agents/openai.yaml whenever the package's visible contract changes enough to make its current metadata inaccurate or incomplete.
Use progressive disclosure aggressively: core contract in SKILL.md, deeper guidance in references/, determinism in scripts/.
Before adding a script, runner, launcher, or formal input interface, write the proof that prompt guidance is insufficient. If the proof is weak, keep it prompt-only.
Validate trigger quality, package integrity, runtime-specific behavior, execution quality, and anti-heuristic quality before shipping.
Output expectations
author: return or create the finished skill package with the leanest viable file set.
edit: patch the existing skill and briefly explain which part of the contract changed.
refactor: preserve useful workflow knowledge while re-homing it into the correct layer.
audit: return findings first. For each finding, state the problem, why it matters, and which file or layer should change.
Reference map
references/skill-pattern-contract.md - the contract for high-impact, anti-heuristic skills
references/workflow-and-modes.md - choose the right mode and keep the output shape honest
references/leverage-and-scope.md - decide whether the skill should exist and what it should own
references/peer-groups-and-boundaries.md - place a skill inside its visible peer group without turning boundaries into routing tables
