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 chrono-meta/forge-harness --skill deep-clarifygit clone --depth 1 https://github.com/chrono-meta/forge-harnessWrote 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/chrono-meta/forge-harness/deep-clarify)<a href="https://agentmods.dev/skills/chrono-meta/forge-harness/deep-clarify"><img src="https://agentmods.dev/badge/skills/chrono-meta/forge-harness/deep-clarify.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 5.1 | $0.00077 | $0.01430 |
| Opus 5 | $0.00039 | $0.00715 |
| Sonnet 5 | $0.00015 | $0.00286 |
| Haiku 4.5 | $0.00008 | $0.00143 |
Grade A, and why
deep-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 6d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
deep-clarify — Socratic Requirement Clarification
A skill that clarifies vague or open-ended requests through conversation to produce actionable spec documents. An independent extension of the "direction confirmation" protocol from agent-composer Step 0-a.
Triggers
/deep-clarify- "I don't know what to build", "How should I approach this?"
- "Organize the requirements", "Write a spec document"
- "What should I do with this?", "The direction is unclear"
- When agent-composer detects pre-dispatch clarification is needed and delegates automatically
Core Principles
Criteria for asking vs inferring:
- Things only the human can know (goal, priorities, constraints, completion criteria) → ask
- Things AI can infer (implementation method, file location, technology choices) → infer and present as
(inferred: X) - Questions: maximum 3 rounds, maximum 2 questions per round — do not overuse
Step 1. Request Analysis
Quickly identify the following from the user's request.
Clarity check:
□ Final state (what will be different when done?) — ask if unclear
□ Completion criteria (how will it be verified?) — ask if unclear
□ Constraints (what must not be done, what must not be touched) — ask if unclear
□ Priority (fast vs thorough, now vs later) — ask if unclear
Direct entry conditions (skip to Step 3 without questions):
- Request is specific and completion criteria are clear → draft spec document and confirm
- Clarity at the level of "add Y feature to file X"
Step 2. Socratic Dialogue
Round 1 — Goal / Completion Criteria (core 2 questions)
Clarification needed.
1. Completion criteria: What does a completed [task name] look like?
(inferred: [inferred completion criteria] — confirm if correct)
2. Scope: Which of [A / B / C] takes priority?
(inferred: [inferred choice] — reason: [rationale])
Round 2 — Constraints / Priority (only if needed)
Skip if Round 1 resolves everything.
1. Is there anything this task must absolutely not do?
2. Which takes priority: fast completion vs solid design?
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.
- 6d ago First seen · 164 lines · 77 tokens per session scan A 480849f926c0
deep-clarify is a skill published in the GitHub repository chrono-meta/forge-harness (14 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 1,430 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
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A project workflow skill for closing a TaiyiForge change and recording it in a CHANGELOG.md file. It checks review results, tests, and the state of the working tree before archiving the change.
taiyi-requirement
A requirements-analysis process that turns a proposed change into a REQUIREMENT.md document. It records user needs, acceptance checks, terminology, triggers, and dependencies, with different levels of detail for different project sizes.
taiyi-test
A project workflow skill for verifying an implementation and producing a TEST.md record. TDD means writing a failing test, implementing the change, and then making the test pass; this skill checks that process and other regression cases.
taiyi-change
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taiyi-ui-design
A design-planning guide for describing how an application's user interface should look and behave. It produces a UI-DESIGN.md document covering layouts, components, interactions, accessibility, and error states.
taiyi-compress
A workflow tool for shrinking large coding-agent conversations and work files into shorter context notes. It can also coordinate separate agents for parallel development and create handoff notes for continuing work in a new session.