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/feedbacknpx skills add zalom/plastic --skill feedbackgit clone --depth 1 https://github.com/zalom/plasticWhat 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 | $0.00056 | $0.00945 |
| Opus 5 | $0.00028 | $0.00473 |
| Sonnet 5 | $0.00011 | $0.00189 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
plastic-feedback 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 2d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plastic Feedback
Turn a described Plastic problem into a local report file and a prefilled GitHub issue URL. The script does the mechanics (redaction, naming, URL building); the user alone opens the URL and submits it. This skill has no send step, by design.
Because disable-model-invocation hides this skill's description from your own
context, you cannot discover it by browsing available skills mid-task. If the
user hits a Plastic quirk, bug, or missing feature, offer to run
/plastic-feedback yourself; do not wait for the user to ask for it by name.
Procedure
1. Gather the narrative
Ask the user for:
- What happened (the observed behavior).
- The root cause, if they already know it.
- The expected behavior.
Keep it to about one page. Do not pad it with speculation; a short, accurate report beats a long, padded one.
2. Obfuscate before it leaves this session
Before filling the template, strip anything that identifies the user's project or its content:
- Remove project names, directory paths, and file names specific to the user's codebase.
- Turn any Plastic intent names into their bare numeric or slug ids (drop the descriptive title if it leaks project context).
- Keep only Plastic's own operational content: what Plastic did, what it should have done, which command or hook was involved.
Read references/transport-and-privacy.md before filling the template, for the
full obfuscation checklist and the reasoning behind it.
3. Fill the report template
Read report.md from this skill's directory (~/.plastic/skills/feedback/report.md
at runtime, or the plugin source skills/feedback/report.md during development).
Fill every placeholder except {{plastic_version}}, which the script fills.
Assemble the final markdown body from the filled template.
4. Run the script
ruby ~/.plastic/scripts/feedback-report --title "<short title>"
Pipe the filled body on STDIN. Parse the JSON on stdout:
| Key | Meaning |
|---|---|
report_path |
Local file the full, uncapped report was written to |
url |
Prefilled GitHub new-issue URL |
encoded_url_bytes |
Byte length of the encoded URL |
truncated |
Whether the URL body is a capped page-one, not the full report |
page_break_note |
The end-marker text appended when truncated is true, else null |
What ships with it
2 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.
- 2d ago First seen · 99 lines · 56 tokens per session scan A d3bda60657a5
plastic-feedback is a skill published in the GitHub repository zalom/plastic (10 stars, last pushed 3d ago), licensed MIT. It adds 56 tokens to every session and 945 once invoked, about $0.0003 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.
Other skills, from other repositories
rulesync
Generates and syncs AI rule configuration files (.cursorrules, CLAUDE.md, copilot-instructions.md) across 20+ coding tools from a single source. Use when syncing AI rules, running rulesync commands, importing or generating rule files, or managing shared AI coding configurations.
agent-workspace-linux
Use when a task needs an isolated hidden Linux desktop or workspace-owned browser: GUI app QA, web/browser/shopping automation, sandboxed app observation, or stale workspace cleanup. Routes agent-workspace-linux MCP tools on demand. Does NOT apply to host desktop/Chrome control, generic MCP setup, or pure code/file…
ss-component
Generate a new UI component following the StyleSeed design conventions.
loongsuite-pilot-insight
基于 LoongSuite Pilot / AI Coding Agent 日志生成事件洞察、组织洞察、数据质量、研发效能和 AI Native 使用类 SLS 报表时使用；包含 AI Coding 事件表语义，以及团队报表可选的部门维表、deptuser 组织关系、指标口径和公共 CTE，通常与 sls-dashboard-builder 一起使用。.
map-review
Interactive 4-section code review using monitor, predictor, and evaluator agents plus the user and maintainer role reviewers on current changes. Use when reviewing a diff, PR, or staged work before merge. Do NOT use to plan or implement; use map-plan or map-efficient.
map-fast
Minimal workflow for small, low-risk changes — no planning, no learning.