version: "1.0.1" name: interpret-feedback description: "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\"."

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

Run both interpretation branches independently. Launch them with spawn_agent / wait_agent using inherited model defaults. Both branch prompts must direct them to treat the shared working tree and its git index as read-only and to interpret by reading and reasoning.

Internal Interpretation

Spawn a sub-agent with the feedback items and all available context. Instruct it to:

1. Read all referenced code and surrounding context
2. 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

1. Return structured results per item

Run $peer-review Skill

Spawn a Codex sub-agent and instruct it to read and follow $peer-review from the installed skill directory, with the feedback items and all available context. Describe the request in natural language:

• Material — the listed third-party feedback items and their surrounding context.
• Task — for each item, determine what the author most likely wants changed and why, whether the suggestion is technically sound, and where the phrasing is ambiguous enough to support multiple valid readings.
• Skepticism guidance — do not take feedback at face value. Check whether the author's stated concern matches the code reality. Look for cases where the reviewer misread the code, confused two similar constructs, or applied a general rule that does not fit this specific context.
• Output format — for each feedback item, return:

1. Intent — what the author most likely wants changed and why (one to two sentences)
2. Correctness — whether the suggestion is technically sound. If not, explain what the reviewer likely misunderstood, with evidence from the code
3. Ambiguity — if the intent supports multiple valid readings, list each reading and which has stronger evidence
4. Confidence — high (clear intent, sound suggestion), medium (likely intent but some uncertainty), or low (genuinely ambiguous or likely incorrect)

The branch prompt must also state explicitly that the sub-agent's final message must contain the verbatim findings text $peer-review produced.

Step 3: Reconciliation

Merge the two interpretations for each feedback item:

Step 4: Output Enriched Items

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 call update_plan to mark this step completed and continue with the next step of the active workflow.

Rules

• If either interpretation agent is unavailable or returns malformed output, proceed with results from the remaining agent.