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/lantisprime/claude-sdlc/scopingnpx skills add lantisprime/claude-sdlc --skill scopinggit clone --depth 1 https://github.com/lantisprime/claude-sdlcWrote 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/lantisprime/claude-sdlc/scoping)<a href="https://agentmods.dev/skills/lantisprime/claude-sdlc/scoping"><img src="https://agentmods.dev/badge/skills/lantisprime/claude-sdlc/scoping.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 | $0.00000 | $0.00451 |
| Opus 5 | $0.00000 | $0.00226 |
| Sonnet 5 | $0.00000 | $0.00090 |
| Haiku 4.5 | $0.00000 | $0.00045 |
Grade A, and why
scoping 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.
What it actually says
Scoping
Turn a vague ask into something a plan can be written against.
When to trigger
Proactively, whenever the request is missing any of:
- Trigger condition (when does this happen / when should it happen?)
- Expected behavior
- Actual behavior (for bugs)
- Specific user flow or entry point
- Environment (dev / staging / prod)
- Measurable success criteria
Don't guess. Claude's guesses become scope creep later.
The clarification checklist
Ask only the questions whose answers aren't already clear. Typical set:
- What is the user trying to do? (business outcome, not technical symptom)
- What happens now? (current behavior, reproduction steps if a bug)
- What should happen? (target behavior with acceptance criteria)
- Which users or flows? (scope of impact)
- Which environment? (local, staging, prod)
- How will we know it's done? (test or observation)
- What's explicitly out of scope? (prevents "while you're at it")
Output
Append the clarified scope to the plan artifact under a Clarifications section. If no plan exists yet, create one via the plan skill using the clarified inputs.
What this skill must NOT do
- Do not fill in ambiguous fields with plausible-sounding defaults.
- Do not ask every question every time — only the ones not already answered.
- Do not proceed to Plan until the blocking ambiguities are resolved.
References
skills/plan/SKILL.mdtemplates/plan.md
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 · 49 lines · 0 tokens per session scan A 77f6961093ba
scoping is a skill published in the GitHub repository lantisprime/claude-sdlc (3 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 451 tokens. 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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Guide the user through verifying Grainulator's MCP servers are running, explain what each server does, handle optional dependencies, and initialize a first sprint if none exists. Use when the plugin is first installed, when MCP servers fail to connect, or when the user asks for help setting up Grainulator.
challenge
Adversarial testing of a specific claim. Try to disprove it.
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Sprint analytics — type distributions, stale claims, velocity, prediction scoring.
healthcheck
Fast pre-flight health check for all Grainulator MCP servers. Pings each server once, reports status in a table, and provides exact fix commands for any that are down. Use before starting any Grainulator session to avoid wasting time on MCP disconnections.
orchard
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blind-spot
Structural gap analysis -- find what the sprint has not considered.