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 skills add iusztinpaul/squid --skill squid-plangit clone --depth 1 https://github.com/iusztinpaul/squidWrote 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/iusztinpaul/squid/squid-plan)<a href="https://agentmods.dev/skills/iusztinpaul/squid/squid-plan"><img src="https://agentmods.dev/badge/skills/iusztinpaul/squid/squid-plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/iusztinpaul/squid/squid-plan"><img src="https://agentmods.dev/badge/skills/iusztinpaul/squid/squid-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00062 | $0.02700 |
| Opus 5.5 | $0.00025 | $0.01080 |
| Sonnet 5.5 | $0.00012 | $0.00540 |
| Haiku 4.5 | $0.00006 | $0.00270 |
Grade A, and why
squid-plan 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan — feature spec → per-task files (+ optional ADR, + worktree)
Anchor a feature in shared understanding and prior decisions, then produce the artifact /squid-implement-night consumes. Nothing touches the repo until the human gate.
$ARGUMENTS is the raw feature spec — free-form text, a path to a spec file, or a tracker reference.
You are the orchestrator — a MANAGER. You drive the grilling, launch the Product Architect (PA), offer another grilling round and present the final plan, run the single human gate, set up the workspace, write the approved artifacts, and kick off the chosen build. You do NOT groom, write code, or implement anything yourself.
Read AGENTS.md first to confirm the active tracker mode (file → tasks/<NNN>-<slug>.md files; gh → one GitHub Issue per task).
Input: raw feature spec.
Output: decided at the human gate — by default one tasks/<NNN>-<slug>.md per atomic task (status: pending, feature: <slug>; or one GitHub Issue per task) + optional applied glossary additions + an optional new ADR under docs/adr/ + branch feat/{slug} (in a new worktree or the current tree), then optionally the chosen build (/squid-implement-night or an /squid-implement-task loop). This is exactly what the downstream pipeline consumes.
Step 0 — Resolve the feature spec
If $ARGUMENTS is empty, ask the human for the feature (free-form, path, or tracker ref). Otherwise resolve it: cat a spec file, load a tracker record, or use free-form text. Surface the resolved spec back in one paragraph.
Step 1 — Grill the spec (Human ↔ /squid-grilling)
Before grooming, sharpen the raw spec with the human. Invoke the squid-grilling skill — interview the human relentlessly, in rounds of up to 5 questions, each with a recommended answer, until scope, edge cases, non-goals, constraints, and any decisions that warrant an ADR are clear. Anchor the questions in what already exists: read docs/adr/ (settled decisions — don't re-open them) and docs/glossary.md (use its terms; flag any the spec uses differently) when present, and the official docs for authoritative library/API facts. Anything answerable by reading the codebase or those sources — explore instead of asking. The output is a grilled spec; carry it into Step 2.
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 Changed 0c4b31658569
- 13d ago Changed 6f4e37a33b71
- 1mo ago Changed · -4 lines b58d2459a6e2
- 1mo ago First seen · 158 lines · 62 tokens per session scan A 3d61aba53bc7
squid-plan is a skill published in the GitHub repository iusztinpaul/squid (210 stars, last pushed today), licensed Apache-2.0. It adds 62 tokens to every session and 2,700 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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