Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/avibebuilder/claude-prime/create-docnpx skills add avibebuilder/claude-prime --skill create-docgit clone --depth 1 https://github.com/avibebuilder/claude-primeWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00149 | $0.00711 |
| Opus 5 | $0.00075 | $0.00356 |
| Sonnet 5 | $0.00030 | $0.00142 |
| Haiku 4.5 | $0.00015 | $0.00071 |
Grade A, and why
create-doc scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ultrathink
Process
Check conversation context and skip completed steps.
1. Identify the doc job
Figure out the document type, audience, purpose, whether to update an existing doc or create new, and what source material it draws from. If any of these would materially change the output and are unclear, ask.
If the request is still evaluating options, stop and discuss — drafting docs before a decision is made locks in the wrong answer.
If the doc type is a recognized structured format (runbook, ADR, postmortem, onboarding guide, API/architecture doc), read references/doc-types.md now — it has the required sections for each type.
2. Ground in evidence
Before writing factual claims, read relevant sources — existing docs, code, config, tickets, PRs, prior discussion. Don't present guesses as fact. Label uncertain details explicitly, or collect them in an "Open questions" section rather than hedging every sentence.
3. Choose destination
Prefer updating the canonical existing doc when one exists.
For new documents, MUST default to docs/ at the repo root (create it if missing). Only deviate when:
- A more specific existing doc home clearly fits (ADR directory, changelog, README section, established project docs tree) — use it
- The repo already follows a convention of keeping docs next to the code they explain — match that
- The doc is event-like (postmortem, incident note) — use a timestamped filename
When you need a fresh timestamp, use date +%Y%m%d%H%M%S.
4. Outline first when substantial
For large or structurally ambiguous docs, propose a title and section outline before drafting. For small or routine docs, write directly.
5. Write the artifact
Write the file at the path from Step 3 — don't paste in chat without creating the file. Adapt structure to the document type (ADRs, runbooks, postmortems, API docs, etc.) and include only sections that earn their keep.
A strong document is accurate, concise, audience-aware, scannable, explicit about why something matters, and clear about what is current behavior vs. decision vs. open question. Write for the intended reader, not for completeness theater. Prefer concrete repo-specific details over generic filler. When sources conflict, name the conflict instead of quietly picking one.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 47 lines · 149 tokens per session scan A 838a1e5b52d2
create-doc is a skill published in the GitHub repository avibebuilder/claude-prime (119 stars, last pushed 3mo ago), licensed MIT. It adds 149 tokens to every session and 711 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
etsy-category-listing
Etsy category page scraper: given an Etsy category URL (e.g. https://www.etsy.com/c/jewelry) and optional page number, returns paginated product listings with listingId, shopId, title, url, image, salePrice, originalPrice, currency, rating, reviewCount, shopName, isAd, freeShipping, badge from category and subcategory…
agent-memory
../../../engineering/agent-memory/skills/agent-memory/SKILL.md.
agile-product-owner
../../../product-team/agile-product-owner/skills/agile-product-owner/SKILL.md.
amazon-reviews-api-skill
This skill helps users automatically extract Amazon product reviews via the Amazon Reviews API. Agent should proactively apply this skill when users express needs like getting reviews for Amazon product with ASIN B07TS6R1SF, analyzing customer feedback for a specific Amazon item, getting ratings and comments for a…
amazon-competitor-analyzer
Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.
audit-reproducibility
Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.