validation-diagnostics
Skill110 starsChanged 3 months agoMITView on GitHub
Validation and diagnostic skill for HASTE. Compare planned vs implemented work, generate diagnostic reports, and feed misses back into skill refinement. Use when: 'validate implementation', 'compare to spec', 'diagnostic report', 'drift analysis', 'coverage check', 'implementation review'.
In this repository10 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .github/copilot-instructions.mdCopilot instructions
- api-designSkill
- copilot-cli-modesSkill
- debug-test-failuresSkill
- dependency-updateSkill
- deterministic-scriptsSkill
- imagery-provider-adaptationSkill
- release-notesSkill
- security-analysisSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
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 107
When validation reveals a miss:
- Distinctive: line 108
Document the miss in the diagnostic report
≈ “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
Validation & Diagnostics
Overview
Structured process for comparing planned vs implemented work, generating diagnostic reports, and identifying gaps. Used by validation agents to provide concrete, evidence-based assessments.
Key Concepts
The Trust Problem
Agents will claim work is complete when it isn't. Validation must be:
- Observable — Based on test output, not agent claims
- Deterministic — Same input produces same verdict
- Evidence-based — Every finding references concrete code or test results
- Structured — Consistent format for easy human review
Patterns & Techniques
Planned vs Implemented Comparison
Step 1: Extract planned items From the spec, issue, or plan, extract a checklist of:
- Acceptance criteria
- Required endpoints/functions
- Expected model fields
- Required test coverage
- UI components specified
Step 2: Verify each item For each planned item, check:
- Does the code exist? (file search, grep)
- Does it match the specification? (read and compare)
- Is it tested? (find corresponding test)
- Does the test pass? (run and capture output)
Step 3: Generate drift report
## Drift Analysis: [Feature]
| Planned Item | Status | Evidence |
|-------------|--------|----------|
| [spec item] | ✅ Implemented | [file:line] |
| [spec item] | ⚠️ Partial | [what's missing] |
| [spec item] | ❌ Not found | [searched in...] |
| [unplanned] | ⚡ Scope creep | [file:line] |Diagnostic Report Template
## Diagnostic Report: [Component/Feature]
### Summary
[1-2 sentence verdict]
### Test Results[Actual test output — copy/paste, not paraphrased]
### Code Quality
| Metric | Result |
|--------|--------|
| Type hints present | ✅ / ❌ |
| Pydantic models used | ✅ / ❌ |
| Config class used (no hardcoded secrets) | ✅ / ❌ |
| Error handling present | ✅ / ❌ |
| Logger used (not print) | ✅ / ❌ |
### Findings
| # | Severity | Finding | Location | Recommendation |
|---|----------|---------|----------|----------------|
| 1 | [High/Med/Low] | [what] | [file:line] | [fix] |
### Coverage Gaps
[Code paths without tests]
### Verdict
✅ PASS | ⚠️ CONDITIONAL PASS | ❌ FAIL
[Explanation with evidence]HASTE-Specific Validation Checks
| Component | Must Verify |
| New API endpoint | Auth level, Pydantic validation, error codes, CORS |
| New processor | Config injection, logger usage, error handling |
| New data model | Pydantic BaseModel, field types, validation rules |
| New data layer | Abstract interface compliance, connection handling |
| New UI component | FluentUI usage, no alt frameworks, responsive |
| Geospatial code | CRS preservation, COG compliance, GDAL/rasterio usage |
Feedback Loop
When validation reveals a miss:
- Document the miss in the diagnostic report
- Identify the pattern (was it a convention violation? missing test? spec ambiguity?)
- Propose a skill update or instruction addition to prevent recurrence
- Flag to the Orchestrator for tracking
Decision Framework
| Validation Result | Action |
| All checks pass, tests green | ✅ Approve |
| Minor issues, tests pass | ⚠️ Conditional — list issues for human review |
| Tests fail | ❌ Block — must fix before proceeding |
| Scope creep detected | ⚠️ Flag — human decides if extra work is acceptable |
| Spec ambiguity found | ⚠️ Flag — needs clarification before validation |
Common Pitfalls
- Accepting "tests passed" without seeing output — Always run and capture
- Validating only happy path — Check error handling and edge cases
- Skipping convention checks — HASTE has specific patterns that must be followed
- Not checking for scope creep — Extra changes can introduce regressions
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.

