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 skills add dr-robert-li/cowork-wordpress-expert --skill intakegit clone --depth 1 https://github.com/dr-robert-li/cowork-wordpress-expertWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dr-robert-li/cowork-wordpress-expert/intake)<a href="https://agentmods.dev/skills/dr-robert-li/cowork-wordpress-expert/intake"><img src="https://agentmods.dev/badge/skills/dr-robert-li/cowork-wordpress-expert/intake/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dr-robert-li/cowork-wordpress-expert/intake"><img src="https://agentmods.dev/badge/skills/dr-robert-li/cowork-wordpress-expert/intake.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00030 | $0.02016 |
| Opus 5 | $0.00015 | $0.01008 |
| Sonnet 5 | $0.00006 | $0.00403 |
| Haiku 4.5 | $0.00003 | $0.00202 |
Grade A, and why
intake scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intake Skill: Context Gathering
You gather context from the user before running diagnostics. This produces a structured understanding of what's wrong, what matters, and what to focus on — so diagnostic skills run smarter, not just harder.
Philosophy
Ask conversationally, not as a checklist. Extract what you need from what the user already said. Only ask about gaps. If the user says "just scan it" or "skip", bypass entirely and proceed with defaults.
Section 1: Load Prior Context
Before asking anything, check for prior diagnostic history on this site.
SITE_NAME="${1:-default-site}"
MEMORY_DIR="memory/${SITE_NAME}"
CASE_LOG="${MEMORY_DIR}/case-log.json"
# Check for prior cases
if [ -f "$CASE_LOG" ]; then
LAST_CASE=$(jq -r '.cases[-1]' "$CASE_LOG" 2>/dev/null)
LAST_DATE=$(echo "$LAST_CASE" | jq -r '.date // empty')
LAST_CONCERN=$(echo "$LAST_CASE" | jq -r '.concern // empty')
LAST_GRADE=$(echo "$LAST_CASE" | jq -r '.health_grade // empty')
OPEN_ITEMS=$(echo "$LAST_CASE" | jq -r '.open_items[]? // empty')
fi
If prior context exists, reference it naturally:
- "Last time I looked at this site ({date}), it scored a {grade}. The main concern was: {concern}."
- "Open items from last scan: {open_items}"
- "Is this related to a previous finding, or something new?"
If no prior context, skip this step entirely.
Section 2: Context Dimensions
Gather information across these six dimensions. You do NOT need to ask about all of them — extract what you can from the user's initial message and only probe gaps.
Dimension 1: Symptoms
What to understand: What's happening vs. what the user expected.
Probe questions (use 1-2, not all):
- "What are you seeing that brought you here?"
- "Is this a specific error, or more of a general concern?"
- "Can you describe what happens when [the problem occurs]?"
Extract: symptom description, error messages, affected functionality.
Dimension 2: Timeline
What to understand: When it started and what changed.
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.
- 9d ago First seen · 245 lines · 30 tokens per session scan A 28e6f7d7639b
intake is a skill published in the GitHub repository dr-robert-li/cowork-wordpress-expert (28 stars, last pushed 6mo ago), licensed MIT. It adds 30 tokens to every session and 2,016 once invoked, about $0.0002 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.
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