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 tobihagemann/turbo --skill interpret-feedbackgit clone --depth 1 https://github.com/tobihagemann/turboWrote 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/tobihagemann/turbo/interpret-feedback)<a href="https://agentmods.dev/skills/tobihagemann/turbo/interpret-feedback"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/interpret-feedback/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/tobihagemann/turbo/interpret-feedback"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/interpret-feedback.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 | $0.00064 | $0.01051 |
| Opus 5 | $0.00032 | $0.00526 |
| Sonnet 5 | $0.00013 | $0.00210 |
| Haiku 4.5 | $0.00006 | $0.00105 |
Grade A, and why
interpret-feedback 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 9d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interpret Feedback
Run two independent interpretations of third-party feedback in parallel (internal + codex peer), then reconcile into enriched items with clear intent summaries. Designed for feedback where the author's intent is ambiguous or the correctness of suggestions is uncertain.
Step 1: Identify Feedback Items
Determine the feedback to interpret:
- If feedback items are in conversation context, use them
- If a file path or URL was provided, read or fetch the content
- If called by another skill, use the items passed in
For each item, collect whatever context is available: code snippets, diffs, surrounding discussion, file paths, line numbers. More context produces better interpretation.
Step 2: Run Two Interpretations in Parallel
Emit both Agent tool calls below in one assistant message. Each Agent call uses model: "opus" and no name. Wait for every agent to report before continuing. Do not begin the next step on a partial set, and do not relaunch an agent that has not yet reported. That is two Agent tool calls total. Both agents' prompts must direct them to treat the shared working tree and its git index as read-only and to interpret by reading and reasoning. HEAD stays where it is: read other refs with git show <ref>:<path> rather than git checkout or git switch.
Internal Interpretation
Spawn a subagent with the feedback items and all available context. Instruct it to:
- Read all referenced code and surrounding context
- For each feedback item, produce:
- Intent: What the feedback author most likely wants changed and why (one to two sentences)
- Correctness: Whether the suggestion is technically sound — flag concerns if the reviewer may be mistaken, with evidence
- Ambiguity: Note where the intent is unclear or where multiple valid readings exist
- Return structured results per item
Run /peer-review Skill
Launch an Agent tool call whose prompt instructs the subagent to invoke /peer-review via the Skill tool. Describe the request in natural language:
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.
- 9d ago First seen · 94 lines · 64 tokens per session scan A de2df60779ad
interpret-feedback is a skill published in the GitHub repository tobihagemann/turbo (402 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 1,051 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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