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/zalom/plastic/dashboardnpx skills add zalom/plastic --skill dashboardgit clone --depth 1 https://github.com/zalom/plasticWrote 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/zalom/plastic/dashboard)<a href="https://agentmods.dev/skills/zalom/plastic/dashboard"><img src="https://agentmods.dev/badge/skills/zalom/plastic/dashboard.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 | $0.00089 | $0.02432 |
| Opus 5 | $0.00044 | $0.01216 |
| Sonnet 5 | $0.00018 | $0.00486 |
| Haiku 4.5 | $0.00009 | $0.00243 |
Grade A, and why
plastic-dashboard 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dashboard — Plastic Work Cockpit
A deterministic overview of the intent store(s). It answers three questions at a glance: where we are (recently worked), where we go next (the most-valuable next work), and how to conduct it (a disposition per intent). The human-facing surface is Markdown, because the user's UI renders Markdown natively but collapses raw tool-call stdout.
The script does all the data work. The agent fills a Markdown template from the script's payload with near-zero reasoning and presents the filled board in its reply. Same store state → byte-identical payload, regardless of model. Do NOT hand-summarize intents.
When to Use
- User invokes
/plastic-dashboard - User asks "where are we", "what's next", "what should I work on", "show me the intents"
plastic-project-continuinglands on the board on resumeplastic-autoreads--jsonto choose the next dispatchable intent
Procedure (the Markdown board — default human surface)
Step 1 — Get the data payload
ruby ~/.plastic/scripts/dashboard.rb [continue|project <slug>] --data
continue(default) → the global board payload (mode: "global").project <slug>→ that project board payload (mode: "project").
The payload is read-only JSON. Global-board fields: date, store_health, summary,
next_work, next_total, next_shown, counts, projects, project_totals, footer.
Project-board fields: slug, store_health, description, summary, counts, active,
active_total, active_shown, next_work, next_total, next_shown, footer. summary
and footer are finished prose strings (2-3 sentences and one line respectively), built in
dashboard.rb and substituted verbatim, exactly like {{date}}/{{description}} already
are - never re-worded or re-derived by the skill. Each list carries cell-ready fields for
its table: next_work rows are
{id, intent, scope, lifecycle, value, disposition, flags, what, flags_label, line};
active rows carry
{id, intent, created, bullet, scope, what, stage, worker, activity, line}. The what,
scope, worker, activity, and flags_label cell fields arrive pipe-escaped and
whitespace-normalized.
What ships with it
4 files 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 · 170 lines · 89 tokens per session scan A 429e7ad5cf8f
plastic-dashboard is a skill published in the GitHub repository zalom/plastic (10 stars, last pushed 3d ago), licensed MIT. It adds 89 tokens to every session and 2,432 once invoked, about $0.0004 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 2026-08-31.
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