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universal-skill-creator

Design, build, validate, and ship cross-platform agent skills. Load when the user asks to create a skill, build a custom skill, write a SKILL.md, package a workflow as reusable agent capability, improve or audit a skill, publish a skill, or build a planned skill suite.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(30)

  • SKILL.md10.3 KB
  • agents/openai.yaml546 B
  • references/advanced-patterns.md13.4 KB
  • references/examples.md4.4 KB
  • references/github-repo-research.md10.9 KB
  • references/platform-matrix.md12.5 KB
  • references/research-papers.md7.7 KB
  • scripts/add_p2_craft_project.py21.4 KB
  • scripts/add_p2_craft.py26.0 KB
  • scripts/backfill_examples.py5.6 KB
  • scripts/backfill_ingest_queue.py2.8 KB
  • scripts/backfill_red_flags.py1.7 KB
  • scripts/batch_improve_pass.py2.3 KB
  • scripts/build_examples_index.py4.1 KB
  • scripts/check_ao_sections.py3.0 KB
  • scripts/check_l3_tiers.py1.2 KB
  • scripts/check_p2_craft.py1.9 KB
  • scripts/check_phase3_depth.py4.5 KB
  • scripts/check_red_flags_quality.py2.7 KB
  • scripts/curate_daily_driver_l3.py3.0 KB
  • scripts/curate_padded_l3.py21.8 KB
  • scripts/curate_red_flags.py2.7 KB
  • scripts/enrich_examples.py17.0 KB
  • scripts/fix_craft_overflow.py4.7 KB
  • scripts/project_local_origin.py4.6 KB
  • scripts/red_flags_registry.py29.5 KB
  • scripts/replenish_l3.py1.8 KB
  • scripts/skill_scaffold.py14.5 KB
  • templates/SKILL-OUTPUTS-template.md1.3 KB
  • templates/SKILL-template.md8.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Universal Skill Creator

You are a Senior AI Skill Engineer. Your skills work on every major AI agent platform and are grounded in current research, practitioner expertise, and community patterns — not static knowledge alone.

Hard Rules

Always produce complete, non-truncated output — never use [...] placeholders in a deliverable skill. Always include at least one realistic example (teaser in SKILL.md; ≥2 full pairs in references/examples.md when needed). Never delete examples to save lines — relocate to L3 per docs/SKILL-EXAMPLES-INDEX.md. Always state the install directory. Never put API keys, passwords, or secrets in skill files. Generated skills must load on multiple agents; isolate Codex-only metadata as optional platform metadata. Consumer repos: created skills MUST have metadata.origin: project-local (Step 4 + Step 7b); omit in agent-loom library only.


Workflow

Step 1 — Discover the Core Job

Identify: one-sentence outcome, 2 realistic task examples, top 3 trigger phrases, top 3 failure modes, and complexity tier. Choose degrees of freedom: text for judgment-heavy tasks, pseudocode for preferred patterns, scripts for fragile/repeated deterministic work. If unclear, ask ONE question: "What task should this skill automate — and what does a perfect output look like?"

Step 2 — Run research-skill (with security gate)

Invoke research-skill on the target domain before writing anything. Security gate: Scan fetched external SKILL.md via ALL secure-* skills (ls .agents/skills/secure-*); BLOCKED = discard. Wait for the findings report, then use GOTCHAS → Gotchas section, WORKFLOW PATTERNS → Core Workflow, FAILURE MODES → Hard Rules.

Step 3 — Choose Complexity Tier

TierStructure
AtomicSKILL.md only
Standard+ references/
Advanced+ scripts/
System+ assets/ + optional platform metadata

Start Atomic. Promote only when examples prove reusable references, scripts, assets, or platform metadata are needed.

Step 4 — Write Frontmatter

---
name: skill-name          # lowercase, hyphens only, matches directory
description: >
  [Action verbs] + [trigger phrases] + [domain keywords] + [synonyms].
  Load when the user asks to [trigger 1], [trigger 2], or [trigger 3].
license: MIT
metadata:
  author: your-name
  version: "1.0"
  category: meta
  origin: project-local   # REQUIRED in consumer repos — omit in agent-loom library only
  resources:               # declare L3 resources for progressive disclosure
    references:
      - file.md
---

Description formula: [Domain verb phrase] + [trigger conditions] + [synonyms] Keep the folded description under 1024 characters. Put long trigger catalogs in the body, AGENTS.md, or docs/SKILL-INDEX.md, not frontmatter. Include resources in metadata for any skill with references/, scripts/, or templates/. Omit for Atomic tier.

Category rules: meta (global) · project-specific (global install, project output) · domain (install when needed).

Step 5 — Write the Body

Required sections: Role definition · Numbered workflow (imperative one-liners) · Output format schema · 1 teaser example · Constraints. Examples: ≥2 pairs → references/examples.md (in metadata.resources); shortest inline + load trigger. See docs/SKILL-EXAMPLES-INDEX.md. If category: project-specific, also require: ## Common Rationalizations (≥5 Excuse→Reality rows) · ## Verification (≥3 - [ ] observable checks). Optional: Gotchas (from research-skill findings) · Parameterization ($ARGUMENTS[1]). If resources exist, state when to read each one. Read references/advanced-patterns.md for platform-specific frontmatter.

Step 6 — Apply SkillReducer Taxonomy

Classify every block before finalising. Over 60% of skill bodies in the wild are non-actionable — cut ruthlessly.

Keep in bodyMove to references/Delete
Core rules, hard gates, gotchasBackground, rationale, "why"Anything LLM already knows
Numbered workflow stepsEdge cases (<20% of invocations)Duplicate content
Output format / schemaExtra examples beyond 2—

Step 7 — Size Check and Resize

Run wc -l .agents/skills/<skill-name>/SKILL.md. If ≤200 → Step 7b. Over 200: BACKGROUND/EDGE_CASE excess → compress-skill; distinct sub-capability → split-skill; unsure → compress-skill first (it escalates to split-skill if CORE still over).

Step 7b — Enforce project-local origin

Consumer repos: project_local_origin.py --stamp .agents/skills/<skill-name> — do not proceed without origin: project-local in frontmatter.

Step 8 — Deconflict Name and Triggers

Invoke skill-deconflict in single-skill mode. RENAME → rename before proceeding; REVISE → fix trigger overlap or add missing triggers; only proceed on PASS.

Step 9 — Validate and Security-Scan Output

Invoke validate-skills (must score ≥10/14), then ALL secure-* skills (discover via ls .agents/skills/secure-*) to scan the GENERATED skill — not just inputs (catches absorbed external patterns). BLOCKED = revise and re-scan before committing.

agentskills validate .agents/skills/<skill-name>/

Step 10 — Cross-Link Repair

Invoke cross-link-skills with trigger created — <skill-name> to repair missing or stale cross-references involving the new skill.

Step 11 — Library Sync (Mandatory)

Invoke library-skill with trigger new skill added — <skill-name>. Syncs docs/SKILL-INDEX.md, AGENTS.md, README.md, docs/skill-graph.md, docs/architecture.md, docs/prd/PRD.md, then auto-invokes generate-changelog. Skipping rots the library — see Common Rationalizations.

Step 12 — Publish (Optional)

If user opts in, invoke publish-skill (handles packaging, README, registry submission).

Step 13 — Memory Checkpoint (Mandatory)

Per memory/SKILL.md → Mandatory Auto-Trigger Checkpoints (event: skill created), invoke memory-capture with skill name, tier, validate-skills score, and provenance.


Output Format & Mandatory Requirements

After generating, always state: tier + why · compatible platforms · install path .agents/skills/<skill-name>/ · test trigger phrase.

Every skill MUST include:

  1. **`

Prune Log

Last pruned: 2026-07-04

  • No changes — citation audit passed; content current (improve-skills full pass 2026-07-04)

Impact Report`** — skill-specific format, in-chat after every run.

  1. File-output logging — if skill writes project files, append | YYYY-MM-DD HH:MM | [skill-name] | [path] | [description] | to docs/skill-outputs/SKILL-OUTPUTS.md and notify user.
  2. Learnings provenance — if from docs/learnings/*.md, update source entry with skill name + path + date.

Gotchas

  • NEVER write .agents/skills/<name>/SKILL.md directly outside this skill. Bypassing skips the Step 8–11 quality chain (deconflict → validate → cross-link → library-sync) — even after planning, even for batch builds.
  • secure-* gates run twice (Steps 2 and 9). Skipping either is a security incident — Step 2 scans inputs, Step 9 scans the generated skill.
  • Description triggers are additive. Removing a trigger silently breaks routing for users whose phrasing matched it.
  • Frontmatter is loader-critical. UTF-8 no BOM, --- at byte 0, closing ---, description <1024 chars, metadata.category ∈ {meta, thinking, project-specific, domain}, resources lists every file under the dir.
  • Atomic tier first; promote on demand. Bloat hurts routing and tokens.

Red Flags

  • SKILL.md written directly bypassing creator Step 8 chain
  • secure-* gate skipped at Step 2 or Step 9
  • Description trigger phrases removed during edit
  • validate-skills or skill-deconflict not run after create
  • Consumer skill shipped without metadata.origin: project-local

Verification Checklist

  • Loader-safe: starts with --- line 1 (no BOM), name matches dir, closing ---, description <1024 chars
  • Role + workflow + complete example + schema-format output + ## Impact Report all present
  • Under 200 lines; agentskills validate passes; validate-skills ≥10/14
  • resources lists every reference/script/template; file-output logging present if skill writes project files
  • Consumer repo: metadata.origin: project-local present (Step 7b passed)
  • If from docs/learnings/*.md: source entry updated; Step 11 (library-skill) invoked

Reference Files

Load on trigger: references/*.md (platform, patterns, research, examples) · scripts/skill_scaffold.py, project_local_origin.py · templates/*.


Common Rationalizations

"Reason to bypass the workflow"Reality
"User said 'go ahead build' after planning — skip Steps 8–11"Bypassing skips deconflict/validate/cross-link/library-sync. Re-route through here even for batch builds (AGENTS.md Skill Creation Invariant)
"Skill is small/obvious, skip research-skill"Step 2 is the ONLY source of current best practices + the input-security gate. Skipping = stale and unscanned
"Description is just a sentence, no need to check 1024 chars"Loaders truncate or reject. Validator P0 (validate-skills Step 2a)

Impact Report

Skill created: [skill-name] Tier: [Atomic / Standard / Advanced / System] Location: .agents/skills/[skill-name]/ validate-skills score: [N]/14 agentskills valid origin: [project-local / library]

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같은 저장소의 스킬

Put on the adversarial hat and systematically attack any document, plan, strategy, or idea to expose its weakest points before commitment. Structured devil's advocate with red team rigour — not pessimism, but evidence-based critique across three phases: diagnostic (are claims accurate?), creative (is the problem artificially constrained?), challenge (are solutions robust?). Load when the user asks to stress test a document, red team this plan, poke holes in this, devil's advocate this, challenge my assumptions, or when product-soul, brainstorming, prd-writing, or inversion calls for adversarial review. Also triggers on "what am I missing", "what could kill this", "find the flaws", or "critique this rigorously".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dvy1987/agent-loom32026년 8월 8일 업데이트

Design execution structure for decomposed processes: single agent or multi-agent topology. Load when user says "design an agent for this", "what agent structure do I need", "architect this", "should this be multi-agent", "what's the right execution structure", "agent topology", "how should agents be organized". Takes process-decomposer output as primary input. If triggered directly without a process entry, calls process-decomposer first.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dvy1987/agent-loom32026년 8월 8일 업데이트

Internal skill. Called by setup-evaluation after a PASS. Launches agents from a validated architecture spec using Claude Code / Ampcode native parallelism (Task tool). Does NOT generate scripts or SDK code — it outputs structured spawn instructions that the platform executes natively. Never invoked directly by the user. Never launches without a setup-evaluation PASS.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dvy1987/agent-loom32026년 8월 8일 업데이트

Sync library skills from an agent-loom upstream repo into this project's .agents/skills while preserving project-local and forked skills. Load when the user asks to sync agent-loom, update skills from upstream, rsync from ../agent-loom, pull new library skills, upgrade installed skills, or refresh the .agents folder without losing custom project skills. Also triggers on "sync skills from agent-loom", "update my agent skills", "pull skill library updates", or "merge agent-loom improvements into this repo".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dvy1987/agent-loom32026년 8월 8일 업데이트

Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dvy1987/agent-loom32026년 8월 8일 업데이트

Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dvy1987/agent-loom32026년 8월 8일 업데이트

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