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 ramboz/jig --skill clarifygit clone --depth 1 https://github.com/ramboz/jigWrote 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/ramboz/jig/clarify)<a href="https://agentmods.dev/skills/ramboz/jig/clarify"><img src="https://agentmods.dev/badge/skills/ramboz/jig/clarify/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/ramboz/jig/clarify"><img src="https://agentmods.dev/badge/skills/ramboz/jig/clarify.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 | $0.00153 | $0.04448 |
| Opus 5.5 | $0.00061 | $0.01779 |
| Sonnet 5.5 | $0.00031 | $0.00890 |
| Haiku 4.5 | $0.00015 | $0.00445 |
Grade A, and why
clarify 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 — 404 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spec 023 introduces this skill as jig's pre-spec ambiguity scan. It is the sixth non-stub active jig skill that ships without a
.pyhelper — clarify is fundamentally a judgment skill, and the determinism it needs (locate the target doc, find an existing## Clarificationssection if any, append new entries) Claude can run inline via Read + Edit. The skill slots betweenworkflow.py new <slug>(stub reservation) and theREADY_FOR_REVIEWtransition (spec body ready for first review).Per user direction on 2026-05-18, jig's clarify ships as a standalone baseline, not a deferral surface. Power users who want spec-kit's
/speckit.clarifyinstall spec-kit and invoke it explicitly under/speckit.*. There is no category-based deferral hint in this skill's description.
What this skill does
Runs a structured ambiguity scan against a DRAFT-state spec document
across six categories aligned with jig's slice template (Scope &
Boundaries / Acceptance Criteria Testability / Dependencies & Blockers /
Non-functional Requirements / Edge Cases & Failure Modes / Terminology
Consistency). The skill rates each category Clear / Partial / Missing,
selects up to five prioritized questions (Partial/Missing weighted
above Clear), asks them one at a time recording the user's verbatim
answer, and appends a ## Clarifications section to the target document
with the Q/A entries plus a coverage summary table.
The skill is breadth over depth: catch the obvious unanswered
questions across the six taxonomy categories in a few minutes, leave
deep domain-specific clarification (legal review, regulatory edges,
multi-stakeholder facilitation) to the dev's judgment or to a richer
team process. The five-question budget exists to keep the scan
lightweight enough to actually run before each READY_FOR_REVIEW
transition — not so heavy it becomes a chore the team skips.
When to use vs. when to defer
There are four sibling skills people often confuse with this one. Pick the right one:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +12 lines 032496281dd4
- 1mo ago First seen · 392 lines · 153 tokens per session scan A 89d0fc1e7d50
clarify is a skill published in the GitHub repository ramboz/jig (7 stars, last pushed 3d ago), licensed MIT. It adds 153 tokens to every session and 4,448 once invoked, about $0.0006 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 2026-08-31.
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