tobihagemann/interpret-feedback

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

Use the Agent tool to launch both agents below in a single assistant message so they run concurrently. Each Agent call uses model: "opus" and does not set run_in_background. That is two Agent tool calls total.

Internal Interpretation

Spawn a subagent 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
3. 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:

• 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 prompt must also state explicitly that the subagent's final assistant message must contain the verbatim findings text /peer-review produced.

Step 3: Reconciliation

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. |

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 use the TaskList tool and proceed to any remaining task.

Rules

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