evaluate-feedback
Skill1 starsChanged 6 months agoMITView on GitHub
Use when receiving a list of requirements, bug reports, or feedback to classify, prioritize, and surface contradictions before acting.
In this repository10 other files it gives its agents.
- api-designSkill
- brainstormSkill
- code-reviewSkill
- commitSkill
- go-apiSkill
- multi-agentSkill
- planSkill
- tddSkill
- ts-apiSkill
- workflowSkill
The file
We split this skill into the rules it gives an agent
7 rules. About 0 are common, 7 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈7
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 18
Read all feedback. For each item, assign:
- Distinctive: line 66
After the list, suggest how to group items into commits:
- Distinctive: line 79
Do not implement anything until the user confirms.
- Distinctive: line 85
Do not start implementing before the user confirms the priority list
- Distinctive: line 86
Do not silently pick an interpretation for unclear items
- Distinctive: line 87
Do not combine high-risk and low-risk changes into the same suggested commit
- Distinctive: line 88
Do not skip the batching suggestion — it helps keep commits small
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Evaluate Feedback
Evaluate incoming requirements, feedback, or bug reports. Prioritize before acting on anything.
Recommended model: Sonnet
Rules
1. Classify each item
Read all feedback. For each item, assign:
| Attribute | Options |
| Type | bug / feature / performance / dx / chore / security |
| Impact | High / Medium / Low |
| Effort | High / Medium / Low |
| Clarity | Clear / Needs clarification |
2. Flag contradictions and unclear specs
Before scoring, identify:
- Items that contradict each other
- Items where the requirement is ambiguous
- Items that are out of scope for the current milestone
For unclear items, ask a clarifying question with preset options:
Q: "[unclear item]" — what is the intended behavior?
1. [interpretation A]
2. [interpretation B]
3. [interpretation C]
4. Other — describe belowResolve ambiguities before prioritizing.
3. Output a prioritized list
Format:
Priority | # | Type | Impact | Effort | Item
---------|---|------------|--------|--------|-----
P1 | 1 | security | High | Low | JWT expiry not validated
P1 | 2 | bug | High | Medium | Race condition in createUser
P2 | 3 | feature | Medium | Medium | Add pagination to /users
P3 | 4 | chore | Low | Low | Rename internal variablePriority rules:
- P1: security bugs, data-loss bugs, High impact + Low/Medium effort
- P2: features and improvements with clear specs, Medium impact
- P3: Low impact, chores, nice-to-haves
4. Suggest batching strategy
After the list, suggest how to group items into commits:
Suggested batching:
- Commit 1: Items 1 + 2 (both security/correctness fixes, same area)
- Commit 2: Item 3 (standalone feature)
- Commit 3: Item 4 (chore, separate)
5. Hard stop before implementation
End with:
Confirm this priority order or make adjustments, then choose an item to start with.
Do not implement anything until the user confirms.
What NOT to do
- Do not start implementing before the user confirms the priority list
- Do not silently pick an interpretation for unclear items
- Do not combine high-risk and low-risk changes into the same suggested commit
- Do not skip the batching suggestion — it helps keep commits small
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

