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 Justinmendezai/The-Adam-Repo --skill research-and-plangit clone --depth 1 https://github.com/Justinmendezai/The-Adam-RepoWrote 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/justinmendezai/the-adam-repo/research-and-plan)<a href="https://agentmods.dev/skills/justinmendezai/the-adam-repo/research-and-plan"><img src="https://agentmods.dev/badge/skills/justinmendezai/the-adam-repo/research-and-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/justinmendezai/the-adam-repo/research-and-plan"><img src="https://agentmods.dev/badge/skills/justinmendezai/the-adam-repo/research-and-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 | $0.00055 | $0.00997 |
| Opus 5 | $0.00028 | $0.00498 |
| Sonnet 5 | $0.00011 | $0.00199 |
| Haiku 4.5 | $0.00006 | $0.00100 |
Grade A, and why
research-and-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 12d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
research-and-plan
Token-cheap planning. The high-level agent (you) reads almost nothing directly. You dispatch explore subagents in parallel, each with a narrow research question, then fold their summaries into a plan.
When to use
- Right after
packet-intakeproducesscratch/intake-notes.md. - Or when the user says "plan it" with a packet present.
- Not for tiny one-file changes — drop straight to
slice-to-tasksfor those.
Inputs
packet/PACKET.mdscratch/intake-notes.md- The repo at
tech_context.repo_path
Workflow
1. Decompose into research questions
Read the packet and intake notes. Generate a list of narrow research questions, each scoped tightly enough that an explore subagent can answer it in one pass. Examples:
- "What is the existing auth flow? List the entry points, the session/token shape, and the middleware that enforces it."
- "Find every place we mutate the
userstable. List file:line." - "What testing framework does this repo use? Where do tests live? What's the run command?"
- "Survey existing UI primitives — buttons, form fields, modals. Where are they defined?"
Aim for 5–12 questions. More than that means the questions aren't narrow enough.
2. Fan out explore subagents in parallel
Use the Task tool with subagent_type: explore and readonly: true. One subagent per question. Send them all in a single batch.
Each subagent's prompt should include:
- The narrow question.
- The repo path.
- A required output shape: short summary + bulleted findings +
file:linecitations. - Instruction to NOT speculate beyond what they read.
3. Fold results
Once subagents return, merge their summaries into:
plan/CONTEXT.md— the shared language doc. Define every term you'll use. (Seegrill-with-docsfor the format.)plan/plan.md— the implementation plan. Sections:- Approach (one paragraph)
- Architecture (a Mermaid diagram or short list of modules and their boundaries)
- Risks
- Slice candidates (rough —
slice-to-taskswill refine) - Open questions remaining
plan/adr/NNNN-<short-name>.md— one ADR per non-obvious decision (DB choice, framework choice, isolation boundaries, async vs sync, etc.).
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.
- 12d ago First seen · 96 lines · 55 tokens per session scan A 9c3e175396dc
research-and-plan is a skill published in the GitHub repository Justinmendezai/The-Adam-Repo (12 stars, last pushed 18d ago), licensed Apache-2.0. It adds 55 tokens to every session and 997 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.
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