Track and surface sub-agent dispatch calibration ratios. Extract from JSONL transcript or read existing ledger. Per-agent clean/blocked/errored/partial trends.
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Transform AI-generated documentation into human-readable prose with personality. USE WHEN: Converting rigid AI docs (checklist spam, CAPS LOCK emphasis, perfect symmetric structures, template dumps) into flowing, conversational writing that feels colleague-authored. Different from /humanizer — that skill detects AI writing patterns (Wikipedia taxonomy). This skill does aggressive deformalization of document structure and flow. PRIMARY TRIGGERS: "humanize this doc", "make this readable", "remove AI patterns from doc", "add human touch to this" See also: humanizer skill for text-level AI pattern removal.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Transforms robotic AI-generated documentation into prose that sounds like an actual human wrote it. The approach is aggressive deformalization — not just tweaking tone, but restructuring how information flows to break AI's predictable patterns.
This is a different tool than the humanizer skill. Humanizer removes AI writing tells at the sentence/phrase level (em-dashes, rule-of-three, vague attributions, sycophantic openers). Humanize-docs attacks structural AI-ness: checkbox spam, template overload, perfect symmetry, emotional flatness across an entire document.
Clear signals you're dealing with AI-generated docs:
- [x] Task 1: Do the thing (AC: #1)Quick test — does the doc have 5+ of these?
If yes, proceed. If no, it might already be human-written — don't over-transform.
Execute in order. Don't apply all of them everywhere — humans are inconsistent. Some sections stay formal, others get playful. That asymmetry is the point.
1. Burstiness injection Mix 5-word punches with 30-word reflections. AI writes at one rhythm. Humans don't.
Before:
The system processes requests through a standardized pipeline that validates inputs and returns structured responses to the calling service.
After:
It's a pipeline. Request comes in, gets validated, structured response goes back out. Nothing exotic — but the validation step is where things quietly break.
2. Structure dismantling Break perfect 3-paragraph blocks. Add digressions. Not every section needs the same shape.
3. Checkbox annihilation Convert checklists to flowing prose with "anyway, you'll need..." or "first thing is..."
Before:
- Install package A (required for Task 3)
- Verify installation with command X
- Proceed to next task only after confirmation
After:
Install package A first — Task 3 depends on it. Run command X to verify it worked. Once you get confirmation, move on. If it errors out, the next step breaks, so don't skip this.
4. Template contextualization Replace code dumps with "here's what worked for me..." framing. Give the template a reason to exist before showing it.
5. Vocabulary swap Kill: utilize, leverage, facilitate, ensure, enable, enhance, robust, seamless Use: use, use, help, make sure, let, improve, solid, smooth
Read the output aloud. Does it sound like you'd actually say this to someone?
Red flags the transformation failed:
Good signals:
Intensity varies by doc type:
| Doc type | Intensity |
|---|---|
| READMEs, internal team docs, onboarding guides, tutorials | High |
| API docs, help docs, technical specs | Moderate |
| Legal, compliance, medical, financial | Gentle only |
Just need to make something readable without full transformation:
Not perfect, but 70% better readability.
Don't:
Do:
AI writes like it's afraid to break rules. Humans write like they're explaining something to a friend while occasionally remembering they should sound professional. Capture that tension.
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Track and surface sub-agent dispatch calibration ratios. Extract from JSONL transcript or read existing ledger. Per-agent clean/blocked/errored/partial trends.
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Compose the AIgent's body state by reading existing signals. Lazy compute. Never polled. Reports the five pressures + recommended reflex.
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Generate a structured delegation brief for any task
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Quarterly skill that walks the skill-index against the actual SKILL.md files on disk, reports drift (skills indexed that no longer exist, skills on disk that aren't enrolled, skills whose descriptions have changed since enrollment). Hardens the auto-enrollment pipeline against silent rot.
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When a [CADDY] hint fires and the principal is confused why, /caddy-explain walks the scoring transparently — which trigger matched, what points it earned, what the threshold was, what other skills were close. Pairs with the legibility thesis. Caddy's decisions should be inspectable on demand.
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Enroll a skill into Caddy's index so it can be surfaced when the right prompt arrives. Use when a new skill is created, copied in, or when a skill needs updated triggers. Usage — "/caddy-enroll <path to SKILL.md>".
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