knowledge-gap
Skill128 starsChanged 6 months agoMITView on GitHub
Post-task knowledge gap review and proposals for improving skills/rules
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
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 8
Run this after completing a task to identify issues in information retrieval and propose candidates for improving skills/rules.
- Distinctive: line 17
When you felt unsure during implementation
- Distinctive: line 18
When you had to repeatedly refer to existing code
- Distinctive: line 105
After execution, the AI reviews the most recent task and outputs the identified issues and proposals in the format above.
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 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
Knowledge Gap Review
Run this after completing a task to identify issues in information retrieval and propose candidates for improving skills/rules.
Purpose
A skill for AI-assisted development that helps verbalize project-specific tacit knowledge and continuously improve the knowledge base.
When to Use
- During self-review after completing a task
- When you felt unsure during implementation
- When you had to repeatedly refer to existing code
Review Process
Step 1: Reflection Perspectives
Review the most recent task from the following perspectives:
- Items that took time to research
- Information that required three or more tool calls to find
- Implicit patterns understood by referencing multiple parts of existing code
- Areas where you had to proceed by guessing because documentation was not found
- Items where decisions were difficult
- Cases with multiple possible implementation approaches
- Situations where you checked existing patterns for error codes or naming conventions
- Parts where you were not confident whether your choice was correct
- Information missing from existing skills/rules
- Cases where you referenced a skill but the required information was missing
- Mistakes or rework that could have been avoided if a rule had existed
Step 2: Classify the Issues
Classify the identified issues based on the following criteria:
| Difficulty | Criteria |
| High | Took more than 10 minutes / required multiple files |
| Medium | Took 5–10 minutes / referenced 1–2 existing files |
| Low | Less than 5 minutes / easy to find but worth recording |
Step 3: Propose Where to Maintain Them
Propose the appropriate destination based on the nature of each issue:
| Destination | Suitable Cases |
.claude/skills/ | Knowledge referenced during specific tasks (invoked by command) |
.claude/rules/ | Rules that should always be applied (auto-loaded) |
CLAUDE.md | Project-wide principles and architecture |
| Update existing file | Can be handled by extending an existing skill/rule |
Output Format
Output the results using the following format:
## Knowledge Gap Review Results
### Identified Issues
| Item | Difficulty | Recommended Location | Summary |
| ------------ | ------------ | ------------------------------ | ------------------- |
| (Issue name) | High/Med/Low | .claude/rules/go/repository.md | (Brief description) |
### Details
#### 1. (Issue name)
**Current Problem**:
(What made it difficult)
**Required Information**:
- (Specific information that was needed 1)
- (Specific information that was needed 2)
**Recommended Action**:
(File name and a summary of what should be added)
---
### Next Steps
- [ ] Create a GitHub issue (label: `ai-knowledge-gap`)
- [ ] Create or update skill/rule filesExample
/knowledge-gapAfter execution, the AI reviews the most recent task and outputs the identified issues and proposals in the format above.
Related
- Existing skills:
.claude/skills/ - Existing rules:
.claude/rules/ - CLAUDE.md:
CLAUDE.md - GitHub label:
ai-knowledge-gap(for tracking issues)
In this repository62 other files it gives its agents.
- AGENTS.mdAGENTS.md
- internal/AGENTS.mdAGENTS.md
- pkg/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- internal/CLAUDE.mdCLAUDE.md
- pkg/CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- .cursor/rules/agent-files.mdcCursor rule
- .cursor/rules/apps/bun.mdcCursor rule
- .cursor/rules/apps/eth-contracts.mdcCursor rule
- .cursor/rules/apps/xrp-server.mdcCursor rule
- .cursor/rules/database/atlas-hcl.mdcCursor rule
- .cursor/rules/database/sqlc.mdcCursor rule
- .cursor/rules/documentation-language.mdcCursor rule
- .cursor/rules/general.mdcCursor rule
- .cursor/rules/github-actions.mdcCursor rule
- .cursor/rules/go/conventions.mdcCursor rule
- .cursor/rules/go/di.mdcCursor rule
- .cursor/rules/go/go-path.mdcCursor rule
- .cursor/rules/go/go-test.mdcCursor rule
- .cursor/rules/internal/application-layer.mdcCursor rule
- .cursor/rules/internal/btc-shared.mdcCursor rule
- .cursor/rules/internal/clean-architecture.mdcCursor rule
- .cursor/rules/internal/cli-structure.mdcCursor rule
- .cursor/rules/internal/domain-layer.mdcCursor rule
- .cursor/rules/internal/dto.mdcCursor rule
- .cursor/rules/internal/infrastructure-layer.mdcCursor rule
- .cursor/rules/internal/interface-adapters-layer.mdcCursor rule
- .cursor/rules/internal/internal-interface.mdcCursor rule
- .cursor/rules/internal/mockery.mdcCursor rule
- .cursor/rules/internal/repository.mdcCursor rule
- .cursor/rules/internal/usecase.mdcCursor rule
- .cursor/rules/proto.mdcCursor rule
- .cursor/rules/README.mdCursor rule
- .cursor/rules/security.mdcCursor rule
- .cursor/rules/shell-script.mdcCursor rule
- .cursor/rules/ssot.mdcCursor rule
- .cursor/rules/task-context-loading.mdcCursor rule
- .cursor/rules/typescript.mdcCursor rule
- .cursor/rules/yaml.mdcCursor rule
- llms.txtllms.txt
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- label-context-mappingSkill
- makefile-updateSkill
- mockerySkill
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- openspec-exploreSkill
- openspec-proposeSkill
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- The first 60 shown.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
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.

