Borrowing it
Nothing to install: this file belongs to glassBead-tc/widescreen-research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/glassBead-tc/widescreen-research/main/.claude/commands/hooks/post-edit-operation.mdgit clone --depth 1 https://github.com/glassBead-tc/widescreen-researchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/glassbead-tc/widescreen-research/post-edit-operation)<a href="https://agentmods.dev/commands/glassbead-tc/widescreen-research/post-edit-operation"><img src="https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/post-edit-operation.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00607 |
| Opus 5 | $0.00000 | $0.00303 |
| Sonnet 5 | $0.00000 | $0.00121 |
| Haiku 4.5 | $0.00000 | $0.00061 |
Grade A, and why
post-edit-operation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post-Edit Operation Hook
Description
This hook validates that MCP server operations follow the State Tracker Pattern - servers should only track state, never provide intelligence.
Trigger
Runs after editing operation files in MCP servers that implement state tracking patterns.
Instructions
1. Run Validation
# For projects with state tracker validation
npm run validate:state-tracker
2. Review Output
If violations are found:
❌ Errors (Must Fix)
- Content generation (generateIdeas, createSolution, etc.)
- Content analysis (analyzeQuality, evaluatePrompt, etc.)
- Intelligence provision (infer, deduce, conclude)
⚠️ Warnings (Review Carefully)
- Decision making based on content
- Pattern matching on prompt content
- Potential content transformation
3. Fix Violations
BAD: Server tries to be intelligent
// ❌ Generating content
const ideas = this.generateIdeas(prompt);
// ❌ Analyzing content
const quality = this.evaluateQuality(prompt);
// ❌ Making decisions
if (prompt.includes('complex')) {
return this.useAdvancedMode();
}
GOOD: Server tracks state only
// ✅ Store what LLM provides
const idea = { content: prompt };
// ✅ Use LLM's evaluation
const quality = parameters.quality;
// ✅ Use LLM's decision
const mode = parameters.mode;
4. Commit Changes
Once validation passes:
git add src/tools/operations/...
git commit -m "description"
The git pre-commit hook will also run validation automatically.
Checklist
Before committing your changes:
- Validation passes (
npm run validate:state-tracker) - Server only validates parameters
- Server only stores state
- Server only returns progress metadata
- No content generation
- No content analysis
- No intelligent decisions
- LLM provides all intelligence via prompt/parameters
Quick Reference
What the Server Can Do
✅ Validate parameter types and requirements ✅ Store what the LLM sends (unmodified) ✅ Count/aggregate stored items ✅ Calculate progress percentages ✅ Group items by LLM-provided categories ✅ Find items by LLM-provided scores/ratings ✅ Format metadata for display
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 113 lines · 0 tokens per session scan A bfa92ca3d54c
post-edit-operation is a command published in the GitHub repository glassBead-tc/widescreen-research (6 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 607 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.