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interpret-feedback

Interpret third-party feedback by running parallel internal and peer interpretations to surface intent, correctness concerns, and ambiguities

Install / Use

npx skills add tobihagemann/turbo --skill interpret-feedback

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Supported Platforms

OpenAI Codex

Our assessment of interpret-feedback

interpret-feedback scores 85/100 on our quality scale, 2797th of 4,616 Development & Engineering skills we index.

Its SKILL.md is 4.9 KB long, well organised into 10 sections with 2 code examples: a solid amount of guidance for an agent.

It has 405 GitHub stars, a meaningful sign that others use it.

Substance
26/30
Structure
18/20
Description
15/15
Adoption
11/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 12 days ago, so interpret-feedback is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-10-05. Automated pattern scan on 2026-10-05. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

interpret-feedback compared with similar skills

All 4 of these similar skills score higher than interpret-feedback; compare them before choosing.

SkillScoreStarsUpdatedFormat
interpret-feedback (this skill)by tobihagemann8540512d agoSKILL.md
ai-job-searchby MadsLorentzen10045.0ktodayCLAUDE.md
claude-howtoby luongnv8910041.8k5d agoCLAUDE.md
algorithmic-artby anthropics100177.9k13d agoSKILL.md
pptxby anthropics100177.9k13d agoSKILL.md

Frequently asked questions

How do I install interpret-feedback?
Run npx skills add tobihagemann/turbo --skill interpret-feedback. The install tabs above show the steps for each supported agent.
Which AI agents does interpret-feedback work with?
It is written for OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
Is interpret-feedback safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is interpret-feedback still maintained?
The repository was last updated 12 days ago, so interpret-feedback is actively maintained.

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

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:

  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.

Related Skills

View on GitHub
GitHub Stars405
CategoryDevelopment
Updated12d ago
Forks31

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions