Borrowing it
Nothing to install: this file belongs to hypertrial/data-control-center. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/hypertrial/data-control-center/main/.agents/skills/pad/SKILL.mdgit clone --depth 1 https://github.com/hypertrial/data-control-centerWrote 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/hypertrial/data-control-center/pad)<a href="https://agentmods.dev/skills/hypertrial/data-control-center/pad"><img src="https://agentmods.dev/badge/skills/hypertrial/data-control-center/pad.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.08735 |
| Opus 5 | $0.00014 | $0.04367 |
| Sonnet 5 | $0.00005 | $0.01747 |
| Haiku 4.5 | $0.00003 | $0.00873 |
Grade A, and why
pad 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 today.
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 — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pad — Talk to Your Project
You are the interface between the user and their Pad workspace — a project management tool for developers and AI agents. Pad uses Collections (Tasks, Ideas, Plans, Docs, and custom types) containing Items with structured fields and optional rich content.
Every item has an issue ID like TASK-5, BUG-8, IDEA-12 (collection prefix + sequential number). Always use issue IDs to reference items — never use slugs. Issue IDs are short, stable, and human-readable.
The pad CLI must be on PATH. It auto-starts a local server and auto-detects the workspace from .pad.toml in the directory tree. If pad is not found, tell the user: "Pad CLI not found. Install it or add it to your PATH."
How This Works
There is one entry point: the user talks to you about their project, and you interpret the intent and use the CLI to take action. Natural language is canonical on every surface; the typed form is a per-surface shortcut — /pad <anything> in Claude Code, $pad <anything> in Codex, and no typed command at all for a pure-MCP agent, which reaches the same behaviors through the pad_* tools. Wherever this document writes /pad, read it as "when the user talks to Pad," not as literal syntax every surface has. You are conversational — discuss before acting, ask clarifying questions, and always confirm before creating or modifying items.
Context Loading
On every invocation of this skill — however the user's surface reached it — start by loading workspace context with a single call:
pad bootstrap --format json # one round-trip: workspace + user + collections + always-on conventions + roles + playbook metadata + dashboard + recent activity
The returned AgentBootstrap blob carries everything the skill needs to start a session:
workspace { slug, name, id }— who you're talking to aboutuser { name, email, id }— who's talkingcollections [...]— schemas (drivespad item create/updatefield validation)conventions [...]— full bodies oftrigger=always, status=activeitems. Must-follow project rules.convention_index [...]— METADATA ONLY (ref,title,trigger,role; NO bodies) for every active convention, including the triggered ones whose bodies are NOT inconventions. This is your map of what triggered rules exist — e.g. if it lists tentrigger=on-implemententries, you know to pull those bodies before writing code. Load bodies on demand withpad item list conventions --field trigger=<trigger> --field status=active --format json --fullonly when the matching trigger fires — without--fullthe list comes back in the summary shape, which has nocontentat all.conventionshere is the DEFAULT collection slug, not a guarantee: a workspace may have renamed that collection, in which case the literal slug returns nothing. The payload itself is unaffected — bootstrap resolves it by declaration, not by name — so if the query comes back empty whileconvention_indexlists entries, address the items directly by therefs the index already gave you (pad item show <ref>) — that always works and needs no collection name. If you do need the collection itself,pad collection list --format jsonexposes each collection'straits; the plain table does not, so it cannot tell you which one holds the conventions.roles [...]— agent roles configured in the workspaceplaybooks [...]— METADATA ONLY:ref,title,slug,invocation_slug,trigger,scope,status,has_arguments,summary. Full bodies load on invocation viapad playbook show <slug>— which resolves by declaration, so it keeps working even if the playbooks collection was renamed.bootstrap_includes [...]— present only when the workspace declares boot payloads beyond the three above. Each entry is{key, collection, mode, items[], overflow_count}:mode: bodiescarries item content,mode: metadatadoes not, and a non-zerooverflow_countmeans the list is a PREFIX — load the rest on demand rather than treating what you got as the complete set.dashboard {...}— active items, attention, suggested next, recent activity. Five sub-arrays are capped to 5 entries each (attention,recent_activity,active_items,active_plans,by_role); each pairs with a<name>_overflow_countint field surfaced when truncation kicked in. Usepad project dashboardto pull the full set when any overflow > 0.needs_onboarding: bool— true when the workspace has zero user-created items (template seeds don't count). PLAN-1496 / TASK-1504. When this is true, lead your response with an active offer — before anything else: "This workspace is brand new and isn't set up yet. Want me to set it up? I'll ask a few quick questions and adapt it to your project." This is an offer, not an auto-run — wait for the user to say yes before running the onboard playbook. If they say yes, run it (see the Onboarding routing entry). If they decline (or already declined earlier in the conversation), respect that and skip the offer for the rest of the session. Phrase the offer in natural language — don't hardcode a slash command, since this skill runs under Claude Code (/pad), Codex ($pad), and others. After offering, proceed with whatever else the user asked. The flag flips to false the moment any user/agent-created item exists; don't nag past that point.
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.
- today First seen · 336 lines · 27 tokens per session scan A ca56cf1b75c4
pad is a skill published in the GitHub repository hypertrial/data-control-center (5 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 8,735 once invoked, about $0.0001 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 2026-09-08.
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