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/intent-researchingnpx skills add zalom/plastic --skill intent-researchinggit 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/intent-researching)<a href="https://agentmods.dev/skills/zalom/plastic/intent-researching"><img src="https://agentmods.dev/badge/skills/zalom/plastic/intent-researching.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 | $0.00035 | $0.01064 |
| Opus 5 | $0.00017 | $0.00532 |
| Sonnet 5 | $0.00007 | $0.00213 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
plastic-intent-researching 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 5d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
Investigate a topic related to the active intent. Choose the right depth, produce a cited report.
Active Intent Gate
Before proceeding, resolve the active intent:
- Detect store: Read
~/.plastic/projects.yml, match CWD against registered project paths. If match → project store at~/.plastic/projects/{slug}/store/. If no match → global store at~/.plastic/store/. - Find active intent: Read
INDEX.mdfrom the detected store. Look under## Active. If exactly one → use it. If multiple → ask which. If none → refuse: "No active intent. Create one first with /plastic-intent-creating" - Resolve intent directory:
{store}/store/{id}--{slug}/
Check Prior Work First
QMD-first (when available): before scanning the store with grep/Read or searching the web, run
ruby ~/.plastic/scripts/qmd-sync search "<terms>" to surface candidate, prior, or related intents
and existing research, then open the authoritative intent file for any hit you act on. Reusing a
prior report beats re-deriving it. The command is a no-op when QMD is absent, so fall back to the
existing INDEX.md / file scan.
Depth Decision
Before starting research, assess the question against these criteria:
Shallow Research
Use when:
- The question is narrow and factual (e.g., "what's the API for X?", "does library Y support Z?")
- A single source or quick search is sufficient
- The answer is likely well-documented
- Time budget: minutes, not hours
Method: Direct web search + codebase exploration + single-pass synthesis.
Deep Research
Use when:
- The question needs multiple sources and cross-verification
- It's a landscape survey, competitive analysis, or architectural decision
- The user explicitly asks for thorough/exhaustive/comprehensive research
- Conflicting information exists and needs adversarial verification
- Time budget: significant, user expects depth
Method: Delegate to the native agent's deep research capability.
- On Claude Code: invoke the
deep-researchskill viaSkilltool - On other agents: use the agent's equivalent (see intent 7 for cross-agent mapping)
- If no native deep research exists: fan out multiple web searches manually, cross-reference, synthesize
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.
- 5d ago First seen · 124 lines · 35 tokens per session scan A e5bf366bd162
plastic-intent-researching is a skill published in the GitHub repository zalom/plastic (10 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 1,064 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 2026-08-31.
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