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/griddynamics/rosetta/hitlnpx skills add griddynamics/rosetta --skill hitlgit clone --depth 1 https://github.com/griddynamics/rosettaWhat 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 | $0.00168 | $0.01600 |
| Opus 5 | $0.00084 | $0.00800 |
| Sonnet 5 | $0.00034 | $0.00320 |
| Haiku 4.5 | $0.00017 | $0.00160 |
Grade A, and why
hitl 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.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Invoke as
<core_concepts>
- "WHY" loop: idea → requirements → working software → learn → evolve
- "HOW" loop: specs → code → tests → stories → features
- Human gatekeeps every artifact in HOW loop. Good: human judgement breaks agent spirals fast. Bad: human becomes bottleneck, review time can exceed generation savings.
- Internal quality matters not for its own sake — messy code makes agents spiral, costing time and money, resulting in bad UX of product.
- Intermediate artifacts (code, tests, designs) are means to an end, not deliverables.
- When output is wrong, fix the harness — not the artifact
- YOU MUST FOLLOW HITL even if in
danger-full-accessor approval policyneveror default mode or similar. - The cost of mistakes is VERY HIGH, assumptions are the top contributor — show to user for prior approval
- Asking questions is a repetitive process: every time something comes up, every time ambiguity comes back, do not rush!
- Right after discovery and before implementation: interview user relentlessly about every aspect of his task until we reach a full shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide recommended and alternative answers, which are enterprise-ready, strict, specific, following best practices. Ask only few questions at a time. If a question can be answered by web search, exploring the codebase, checking knowledge sources, do it first. Keep facts, document concise, valuable, highly compressed, cut wording, use terms and common patterns. Loop cycles until NO gaps or ambiguities left without nitpicking.
</core_concepts>
Questioning:
- Ask until assumptions, ambiguities, gaps, conflicts resolved.
- Skip LOW or NIT PICKING.
- Prioritize: scope > security/privacy > UX > technical.
- 5-10 targeted MECE questions per batch.
- One decision per question.
- Include why it matters and safe default.
- Group related questions into a single interaction.
- Track open questions using todo tasks.
- After each answer, restate understanding in context and adapt remaining questions.
- Mark unanswered as assumption and continue.
- Persist Q&A in relevant files.
- If CRITICAL and HIGH priority questions remain after initial round, proceed with another one.
- STOP and escalate unresolved critical blockers.
- MUST NOT assume anything—even reasonably. Task must be crystal clear. Suggest and confirm instead of guessing.
- MUST BE critical to your own suggestions and user input; ask questions to resolve gaps/inconsistency/ambiguity/vague language.
- MUST use ask user question tools if available.
Approval:
- MUST NOT assume approval — user message (questions, suggestions, edits) = review, not approval. User questions are only questions.
- Accepted:
Yes, I approve,Approve, the plan was reviewed, etc. - To approve and start implementation, use longer sentences: "Yes, I reviewed the plan" or "Approve, the plan and specs were reviewed" (to enforce an action).
- Do not proceed to the next phase unless the user explicitly approves, DO NOT ASSUME it is approved.
- Require explicit approval: for each requirement unit, spec, or design artifact before it is marked
Approved; before implementation begins; after implementation before closing the task. - Present small batches for review; do not batch too much and lose review quality.
- Keep status
Draftuntil approved. - Proactively review new or updated content with user as a narrative.
- Clearly separate user-provided vs AI-inferred.
- High+ risk: require EXACT sentence to type.
- Additional scope requires ADDITIONAL approval.
- By request size: SMALL = HITL after specs; MEDIUM = full HITL; LARGE = full + major decisions.
- USER may review by directly providing comments in the files.
HITL gates (required at minimum):
- Ambiguous, conflicting, or unclear intent.
- Risky, destructive, or irreversible action.
- Scope change or de-scoping proposed.
- Critical tradeoffs needing MoSCoW decision.
- Missing acceptance criteria, hidden assumptions, or non-measurable thresholds.
- Conflicting requirement clauses are found.
- Requirement appears stale or contradictory.
- Final acceptance on requirement coverage is required.
- Adaptation has no direct target equivalent.
- Architecture or design tradeoffs are ambiguous.
- Simulation or review exposes major behavioral risk.
- Context conflicts with stated user intent.
- Confidence below reliable threshold.
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 · 134 lines · 168 tokens per session scan A 1df0e2a1fc2d
hitl is a skill published in the GitHub repository griddynamics/rosetta (341 stars, last pushed 4d ago), licensed Apache-2.0. It adds 168 tokens to every session and 1,600 once invoked, about $0.0008 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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