Interpret third-party feedback by running parallel internal and peer interpretations to surface intent, correctness concerns, and ambiguities. Use when the user asks to \"interpret feedback\", \"interpret comments\", \"what does this feedback mean\", \"clarify reviewer intent\", \"understand this review\", or \"interpret these suggestions\".
npx skills add https://github.com/tobihagemann/turbo --skill 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.
Determine the feedback to interpret:
For each item, collect whatever context is available: code snippets, diffs, surrounding discussion, file paths, line numbers. More context produces better interpretation.
Use the Agent tool to launch both agents below in a single assistant message so they run concurrently. Run them in the foreground so all their results return in this turn. Each Agent call uses model: "opus" and no name. 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.
Spawn a subagent with the feedback items and all available context. Instruct it to:
/peer-review SkillLaunch an Agent tool call whose prompt instructs the subagent to invoke /peer-review via the Skill tool. Describe the request in natural language:
The prompt must also state explicitly that the subagent's final assistant message must contain the verbatim findings text /peer-review produced.
Merge the two interpretations for each feedback item:
| Agreement | Action |
|-----------|--------|
| Both agree on intent and correctness | High confidence. Use the shared interpretation. |
| Intent agrees, correctness differs | Flag the correctness concern with both perspectives. |
| Intent disagrees | Flag as ambiguous. Present both readings and note which has stronger evidence. |
For each feedback item, output the original feedback followed by the interpretation:
### Item <N>: <short label>
**Original:** <feedback text, truncated if long>
**File:** <path:line if applicable>
**Intent:** <reconciled interpretation of what the author wants>
**Correctness:** <sound | concern: <explanation>>
**Confidence:** <high | medium | low>
**Ambiguity:** <none | <description of unclear aspects>>
<If interpreters disagreed, show both perspectives>
After all items, add a summary:
## Interpretation Summary
- Total items: <N>
- High confidence: <N>
- Correctness concerns: <N>
- Ambiguous intent: <N>
Then use the TaskList tool and proceed to any remaining task.
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take tobihagemann/interpret-feedback from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.