p1-spec-research-policy

p1-spec-research-policy is a skill for Claude Code from babyworm/rtl-agent-team. It costs 23 tokens per session (4,095 once invoked), scanned A, original, MIT.

An internal policy reference for researching and clarifying hardware specifications. It requires resolving ambiguities with the user and documenting choices before requirements are finalized.

In plain words
What is it for?
It guides structured interviews, requirement approval, and comparisons of alternative hardware implementation approaches.
Why use it?
It prevents unapproved assumptions from passing from the specification into architecture, RTL, and verification.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: names the AskUserQuestion tool.

Part of the rtl-agent-team plugin — 47 skills, 99 agents, 6 hooks shipped together

Good fit It guides structured interviews, requirement approval, and comparisons of alternative hardware implementation approaches.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/babyworm/rtl-agent-team/p1-spec-research-policy
Install

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.

Any agent
npx skills add babyworm/rtl-agent-team --skill p1-spec-research-policy
Clone the repo
git clone --depth 1 https://github.com/babyworm/rtl-agent-team

Made for: Claude Code.

Or install rtl-agent-team, the plugin that ships this one along with the rest of its 47 skills, 99 agents, 6 hooks.

Wrote 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.

agentmods badge for p1-spec-research-policy

README.md
[![agentmods](https://agentmods.dev/badge/skills/babyworm/rtl-agent-team/p1-spec-research-policy/github.svg)](https://agentmods.dev/skills/babyworm/rtl-agent-team/p1-spec-research-policy)
Your own site
<a href="https://agentmods.dev/skills/babyworm/rtl-agent-team/p1-spec-research-policy"><img src="https://agentmods.dev/badge/skills/babyworm/rtl-agent-team/p1-spec-research-policy/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.

agentmods 80×15 button for p1-spec-research-policy

Your own site · 80×15
<a href="https://agentmods.dev/skills/babyworm/rtl-agent-team/p1-spec-research-policy"><img src="https://agentmods.dev/badge/skills/babyworm/rtl-agent-team/p1-spec-research-policy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,095 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 152
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00023 $0.04095
Opus 5 $0.00012 $0.02048
Sonnet 5 $0.00005 $0.00819
Haiku 4.5 $0.00002 $0.00409

Measured 8d ago against content hash 19b6d14af177, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

p1-spec-research-policy 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 8d 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.

skills/p1-spec-research-policy/SKILL.md · 309 lines

How it starts

The opening of the file, as written. The whole thing — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Phase 1 Research Policy

Core Principles

  • AskUserQuestion-First: Resolve every ambiguity, design choice, or scope decision via AskUserQuestion BEFORE proceeding. Never assume — a wrong assumption cascades to all later phases.
  • Structured Interview (before spec parsing): Interview the user first — one question per message covering goal, scope, constraints, priority trade-offs, verification strategy, and dependencies (full interview protocol owned by the goal-clarifier agent). Record answers in docs/phase-1-research/design-intent.md; resolve ambiguous spec language with the user's stated intent, never agent assumptions.
  • Approach Comparison for Open Items: When the spec allows multiple implementation paths, present a per-OPEN comparison table (Approach | Pros | Cons | Area Est. | Latency Est. | Recommendation) and let the user select via AskUserQuestion. Record choice + rationale in open-requirements.json resolution_rationale.
  • Incremental Requirement Approval: Seek approval in stages — interface/IO → functional (per block) → performance → open items — so users correct misinterpretations before they propagate. Finalize iron-requirements.json only after all stages are approved.
  • Domain-Consult-First: Invoke domain-consult for algorithms, standards, coding tools, and HW trade-offs — never research in isolation. Experts provide knowledge; spec-analyst captures it as structured artifacts.
  • Propose, Do Not Decide: Present algorithm/tool candidates with trade-offs; the user makes final selections. Architecture-level decisions (pipeline, partitioning, memory hierarchy) belong to Phase 2 — Phase 1 surveys and recommends.
  • Exhaustive Tree Exploration: Spawn maximum parallel agents to explore every feasible solution path before committing. Skip ONLY if the user specifies exact algorithm + architecture (even then, explore at least 2 variants for validation).

Spec Refinement Criteria

AskUserQuestion MUST cover these areas (skip items already provided by user):

  • Target codec, profile, level (e.g., H.264 High Profile Level 4.1)
  • Target resolution and framerate (e.g., 1080p@60fps, 4K@30fps)
  • Encoder, decoder, or both
  • Interface protocol (AXI4, AXI4-Lite, APB, custom)
  • Clock frequency target and process node (ASIC vs FPGA)
  • Feature scope restrictions (e.g., "TQ only", "intra-only")
  • Priority trade-off preference (throughput vs area vs power vs quality)

Read the full file on GitHub · 309 lines

Changes

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.

  1. 8d ago First seen · 309 lines · 23 tokens per session scan A 19b6d14af177

Subscribe to this mod's changes

p1-spec-research-policy is a skill published in the GitHub repository babyworm/rtl-agent-team (51 stars, last pushed 18d ago), licensed MIT. It adds 23 tokens to every session and 4,095 once invoked, about $0.0001 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 2026-09-03.

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