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/marcusrbrown/systematic/deepen-plannpx skills add marcusrbrown/systematic --skill deepen-plangit clone --depth 1 https://github.com/marcusrbrown/systematicWrote 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/marcusrbrown/systematic/deepen-plan)<a href="https://agentmods.dev/skills/marcusrbrown/systematic/deepen-plan"><img src="https://agentmods.dev/badge/skills/marcusrbrown/systematic/deepen-plan.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 | $0.00076 | $0.04860 |
| Opus 5 | $0.00038 | $0.02430 |
| Sonnet 5 | $0.00015 | $0.00972 |
| Haiku 4.5 | $0.00008 | $0.00486 |
Grade A, and why
deepen-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 3d 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 — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deepen Plan
Introduction
Note: The current year is 2026. Use this when searching for recent documentation and best practices.
ce:plan does the first planning pass. deepen-plan is a second-pass confidence check.
Use this skill when the plan already exists and the question is not "Is this document clear?" but rather "Is this plan grounded enough for the complexity and risk involved?"
This skill does not turn plans into implementation scripts. It identifies weak sections, runs targeted research only for those sections, and strengthens the plan in place.
document-review and deepen-plan are different:
- Use the
document-reviewskill when the document needs clarity, simplification, completeness, or scope control - Use
deepen-planwhen the document is structurally sound but still needs stronger rationale, sequencing, risk treatment, or system-wide thinking
Interaction Method
Use the platform's question tool when available. When asking the user a question, prefer the platform's blocking question tool if one exists (question in OpenCode, request_user_input in Codex, ask_user in Gemini; in Pi, use the blocking-question extension if available, otherwise present numbered options in chat and wait). Otherwise, present numbered options in chat and wait for the user's reply before proceeding.
Ask one question at a time. Prefer a concise single-select choice when natural options exist.
Plan File
<plan_path> #$ARGUMENTS </plan_path>
If the plan path above is empty:
- Check
docs/plans/for recent files - Ask the user which plan to deepen using the platform's blocking question tool when available (see Interaction Method). Otherwise, present numbered options in chat and wait for the user's reply before proceeding
Do not proceed until you have a valid plan file path.
Core Principles
- Stress-test, do not inflate - Deepening should increase justified confidence, not make the plan longer for its own sake.
- Selective depth only - Focus on the weakest 2-5 sections rather than enriching everything.
- Prefer the simplest execution mode - Use direct agent synthesis by default. Switch to artifact-backed research only when the selected research scope is large enough that returning all findings inline would create avoidable context pressure.
- Preserve the planning boundary - No implementation code, no git command choreography, no exact test command recipes.
- Use artifact-contained evidence - Work from the written plan, its
Context & Research,Sources & References, and its origin document when present. - Respect product boundaries - Do not invent new product requirements. If deepening reveals a product-level gap, surface it as an open question or route back to
ce:brainstorm. - Prioritize risk and cross-cutting impact - The more dangerous or interconnected the work, the more valuable another planning pass becomes.
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.
- 3d ago First seen · 411 lines · 76 tokens per session scan A 81bf1e813176
deepen-plan is a skill published in the GitHub repository marcusrbrown/systematic (24 stars, last pushed today), licensed MIT. It adds 76 tokens to every session and 4,860 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 2026-08-30.
Other skills, from other repositories
credit-based-billing
Complete guide for giving customers included, free, prepaid, promotional, or top-up credits using grants, balances, ledger deductions, rollover, expiry, alerts, and overage.
checkout-integration
Guide for starting hosted Checkout Sessions, payment links, and overlay or inline checkout for one-time and recurring products; use subscription-integration for post-checkout lifecycle management.
subscription-integration
Guide for managing recurring subscriptions after checkout, including trials, lifecycle states, plan changes, cancellation, failed-payment recovery, proration, mandates, and on-demand charges.
refunds-and-disputes
Guide for issuing refunds, handling disputes and chargebacks, and reconciling customer access with Dodo Payments.
customer-management
Guide for managing customer records, saved payment methods, monetary wallets, and hosted customer-portal sessions; subscription lifecycle and custom billing UI are covered separately.
usage-based-billing
Guide for charging directly per measured API call, token, storage unit, or other consumption using meters, stable usage events, aggregation, free thresholds, and metered subscriptions.