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-discoveringnpx skills add zalom/plastic --skill intent-discoveringgit 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-discovering)<a href="https://agentmods.dev/skills/zalom/plastic/intent-discovering"><img src="https://agentmods.dev/badge/skills/zalom/plastic/intent-discovering.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 | $0.00085 | $0.00725 |
| Opus 5 | $0.00043 | $0.00362 |
| Sonnet 5 | $0.00017 | $0.00145 |
| Haiku 4.5 | $0.00009 | $0.00072 |
Grade A, and why
plastic-intent-discovering 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intent Discovery — What-stage context deposit
Runs once, at intent activation, after the lock is armed and before Why. It gathers what is already known so Why does not start cold, and deposits it as a resource the Why-stage brainstorming agent reads.
When it fires
Inside plastic-intent-starting, right after the bridge is armed, under the
lock. Dispatched as the plastic-intent-discovery background agent.
Precondition for skipping: a size of S already on record, and no graph edges. Read the
activating intent's chain and sources frontmatter fields first. When a Tier: S line is
already stamped at the top of spec.md AND both fields are empty, the pass does not run:
write the single line no chain/sources, discovery skipped to
resources/discovery--<slug>.md and stop there. Sizing happens at Why, which is after this
stage, so a first activation usually has no size yet and the full pass runs. Never guess a
size to unlock the skip. At M or L, or when either field is filled, run the full pass below.
What it does
- Read the intent's links. Load the activating intent file's
chainandsourcesfrontmatter fields. If both are empty and a size of S is already on record, apply the skip above and stop. - QMD-first discovery. Search the Plastic stores with
scripts/qmd-sync search "<terms>"(or theqmdskill), scoped to the relevantplastic-*collections, across completed predecessor work named inchain/sourcesand any related parked/future intents in INDEX.md. Fall back to ripgrep over the stores only when QMD is absent. - Deposit, never author. Write findings to
resources/discovery--<slug>.mdin the intent directory ONLY. Do not write the intent file, spec.md, or any lifecycle deliverable. The lock-owner-only write rule stays intact; the Why-stageplastic-brainstormingagent reads the deposit and enriches## Context. Shape the deposit tabular-first perPLASTIC.md(## Tabular-First Reporting, intent 160).
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 · 57 lines · 85 tokens per session scan A 98d3133a2d1a
plastic-intent-discovering is a skill published in the GitHub repository zalom/plastic (10 stars, last pushed 3d ago), licensed MIT. It adds 85 tokens to every session and 725 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.
Other skills, from other repositories
adk-unit-design
Writes an as-built architecture document for one ADK code unit — purpose, execution flow, data flow, cross-class dependencies, extension points, and the parts that must not change — to docs/design/{topic}/{unit}/index.md. It describes the code as implemented, not a proposed design, and its reader is a developer about…
artifact-deploy
One-click deploy a user's pre-built app/artifact into their OWN AWS account and get a global public HTTPS link (Vercel-like), with a default TTL and promote-to-persistent. Use when the user says "deploy this", "ship this demo", "give me a public link", "share this externally", or "deploy to AWS".
explain-for
Explain a topic, a piece of code, an error, or a design decision calibrated to one named audience — a 5-year-old, a 5th grader, a manager, a designer, a graduate student, a parent. Resolves who the explanation is for (from the request, or from what memory already records about that person), establishes the ground…
session-summaries
What the chat right-panel session summary shows, what it costs, and how to make a session summarize well. Load when the user asks about the session summary panel, why a summary looks wrong or empty, or how to turn it on.
ai-discover
Parallel discovery of performance hotspots (perf track) and failure surfaces (bug track) for the auto-improvement loop. Fans out one subagent per hot-path area or failure surface; each returns ONE concrete, behavior-preserving fix candidate (perf) or a reproducing test plus fix (bug). Discovery only — no code changes…
choosing-swarm-patterns
Use when coordinating multiple AI agents with Agent Relay's workflow engine and need to pick the right orchestration pattern - covers the 10 core patterns (fan-out, pipeline, hub-spoke, consensus, mesh, handoff, cascade, dag, debate, hierarchical) plus 14 specialized ones, with decision framework and accurate…