Skills DirectorySkills Directory
SkillsLearnSecurityCategoriesDocsCommunityBlog
Sign InSubmit Skill
Skills Directory

Security-tested agent skills for Claude, coding agents, and AI workflows.

Directory

  • Browse Skills
  • All Skills A–Z
  • Claude Skills
  • Claude Code Skills
  • Agent Skills
  • Categories
  • Submit a Skill

Learn

  • Learn Hub
  • Install Claude Skills
  • Write SKILL.md
  • Skills vs MCP
  • Directories Compared

Security

  • Security
  • Methodology
  • Secure Claude Skills
  • Security Badges

Company

  • About
  • Community
  • Blog
  • API Docs
  • Advertise

2026 Skills Directory. All rights reserved.

Back to skills

Full

ASecurity

Full writing-quality workflow — scope (audience/tone) -> multi-facet review (structure, readability, visuals, tone, humanize) as parallel subagents -> fix loop with fresh re-verification -> report. Entry point for improving any piece of writing, not just design docs.

8 stars
0 votes
0 copies
0 views
Added 9/20/2026
ai-agentsgo

Security Analysis

A100/100

Scanned 9/20/2026

Install to Claude Code

$npx -y skills add tstapler/dotfiles --skill full --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Full?

Add the live security badge to your README — it updates automatically with every re-scan.

Security grade badge for Full
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/tstapler-full-918acf5d/badge)](https://www.skillsdirectory.com/skills/tstapler-full-918acf5d)

More formats (shields.io, HTML) on the badges page.

Download Zip
Files
SKILL.md
---
description: Full writing-quality workflow — scope (audience/tone) -> multi-facet review (structure, readability, visuals, tone, humanize) as parallel subagents -> fix loop with fresh re-verification -> report. Entry point for improving any piece of writing, not just design docs.
---

# writing:full

Orchestrator for the general-purpose writing pipeline: establish who this is for and how it should sound, then run every applicable quality check as independent lean agents (see the `lean-agent-loop` skill), then fix and re-verify until clean or a round cap is hit. Mirrors `sdd:full`'s phase structure — each phase below delegates to its own skill and never duplicates that skill's logic — and reuses `design-doc-review:review`'s pipeline wholesale when the target is a design doc, rather than re-implementing structure/readability/visuals checks a second time.

**Target**: {{args}} — a file path. If omitted, use the doc already in context.

See `../../CHECKLIST.md` for the running index of every mechanical check across this pipeline —
what it catches, the research/citation behind it, and where it's implemented. Add to that file
(not here) whenever a new pattern is noticed or researched; this file stays about orchestration.

## Phase 1 — Scope

Run `writing:scope` against the target. Get back `{audience, tone, doc_type, purpose}`. Do not proceed until this resolves — every later phase's agent prompts need it.

## Phase 2 — Dispatch (parallel lean agents)

**If `doc_type` is "design doc / RFC"**: this repo already has a purpose-built pipeline for that shape (Decision requested / Non-goals / Alternatives / Risks structure, publish-target compatibility). Don't duplicate it — dispatch `design-doc-review:review` as one of the parallel agents below, and layer only the checks it doesn't already do (`writing:tone`, `writing-humanize`) alongside it:

```
Agent "design-doc-review": run design-doc-review:review against <path>. Return its final report (already includes its own fix loop).
Agent "tone": run writing:tone against <path>. Scope: {audience, tone, doc_type, purpose}. Return only its JSON summary.
Agent "humanize": run writing-humanize against <path>. Return its structured audit output.
```

**Otherwise** (blog post, PR description, general doc, personal note): dispatch the general-purpose checks directly:

```
Agent "structure": run technical-writing-coach against <path>, focused on the SUCCESS framework and decision-oriented density for this doc_type/purpose. Return findings with severity.
Agent "tone": run writing:tone against <path>. Scope: {audience, tone, doc_type, purpose}. Return only its JSON summary.
Agent "humanize": run writing-humanize against <path>. Return its structured audit output.
```

Launch all agents for the chosen branch **in a single message** (tier A, per `lean-agent-loop`). If parallel dispatch is unavailable, drop to the next tier in that skill's degraded-mode table and say which tier ran.

Skip `writing-humanize` entirely when scope's `tone` is not "match my own voice" and the doc_type is a design doc or other structured technical document where AI-authorship is disclosed/expected (check the doc's own header, e.g. this repo's "Discovery — AI-accelerated" status convention) — running an AI-detection-evasion audit on a document that says it's AI-accelerated is answering a question nobody asked. Still run it when `tone` is "match my own voice" regardless of doc_type, since voice-preservation is the point either way.

## Phase 3 — Triage

Combine results. `design-doc-review:review` (if dispatched) has already triaged and fixed its own findings — treat its report as final for those facets, don't re-triage them. For `writing:tone` and `writing-humanize`/`technical-writing-coach` findings:

- **Mechanically fixable**: humanize's vocabulary/structural/rhythm flags, tone's register/jargon mismatches with a clear direction, technical-writing-coach's density/structure fixes.
- **Author-input-needed**: anything where the fix requires content only the author has (a missing anecdote for authenticity, a purpose the author hasn't actually decided on yet).

Present both lists before touching the file. Ask which mechanically-fixable findings to apply (default: all).

## Phase 4 — Fix loop

Same shape as `design-doc-review:review`'s Phase 3: apply approved fixes directly (this coordinator edits, it doesn't delegate the edit to a lean agent), then re-dispatch **fresh** agents for every check that wasn't already passing — no memory of the prior round's findings fed in. Round cap: 3. A finding that persists after round 3 gets reported, not silently dropped.

## Phase 5 — Report

```
writing:full: <path>
Scope: doc_type=<...>, audience=<...>, tone=<...>
Tier: <A|B|C|D>
Rounds run: <N> / 3

| Check | Round 1 | Final |
|---|---|---|
| design-doc-review (if applicable) | ... | ... |
| structure (technical-writing-coach, if applicable) | fail (N) | pass |
| tone | fail (N) | pass |
| humanize | ... | ... |

Fixed automatically: <count>
Needs author input: <count>
Persisted after 3 rounds: <count, if any>
```

Attribution

tstaplertstapler
View sourceMore from tstapler →
SSkills DirectorySkills Directory

Your tool, in front of Claude Code builders.

3 founder slots · $299/mo · GSC-verified traffic · sponsors can never buy grades.

See placements

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments (0)

No comments yet. Be the first to comment!

SSkills DirectorySkills Directory

Your tool, in front of Claude Code builders.

3 founder slots · $299/mo · GSC-verified traffic · sponsors can never buy grades.

See placements

Related Skills

Caveman

Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.

1023331 votes

Hyperplan

Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...

686011 votes

Mcp Code Execution

Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.

3331 votes

catchup

Recovers prior coding-agent session context by running `catchup <agent> --since-compact`, which extracts a clean summary of a previous Codex, Claude Code, Antigravity, OpenCode, or Pi Agent session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", or asks to recover/summarize a previous session before continuing. Do NOT use for the current conversation, git history, or any non-agent log.

611 votes

math-skill

A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling

381 votes
View all in ai-agents →