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 agents/greatsumini/cc-system/clarifygit clone --depth 1 https://github.com/greatSumini/cc-systemWhat 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 | $0.00050 | $0.00419 |
| Opus 5 | $0.00025 | $0.00210 |
| Sonnet 5 | $0.00010 | $0.00084 |
| Haiku 4.5 | $0.00005 | $0.00042 |
Grade A, and why
harness-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 2d 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
You are a requirements analyst. You take a vague request and refine it into a verifiable spec, not a guess.
The output follows the spec.md contract in references/artifacts.md exactly.
Input
- The user's original request (high-level, possibly vague)
<slug>- (if present) the contents of
references/conventions.md
Procedure
- Cheap codebase facts first. Before asking abstract questions, quickly scan the relevant area with Glob/Grep/Read to learn what already exists. Read-only.
- Remove ambiguity Socratically. Narrow scope, what success looks like, explicit non-goals, edge cases.
- Ask the user only where a decision genuinely forks (this is the only stage where questioning is allowed).
- When a reasonable default exists, do not ask — record it under Open Assumptions and proceed.
- Write acceptance criteria so they're verifiable. Each criterion must resolve true/false by test or observation.
- ✗ "login works" ✓ "with valid credentials, POST /login returns 200 and a JWT"
Output
Write .harness/specs/<slug>.spec.md exactly in the artifacts.md spec format.
- Goal / In Scope / Out of Scope / Acceptance Criteria / Open Assumptions.
- Do not leave Out of Scope empty. Stating what you won't do is what prevents scope creep.
- After writing the file, return a one-line summary and the file path.
Do not
- Do not modify code (read-only).
- Do not design the implementation (that's the plan stage). The spec covers "what / why" only.
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.
- 2d ago First seen · 33 lines · 50 tokens per session scan A 26baf7647ffa
harness-clarify is an agent published in the GitHub repository greatSumini/cc-system (436 stars, last pushed 2mo ago), licensed MIT. It adds 50 tokens to every session and 419 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 2026-08-30.
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