AtomCode Project: Agent Memorized-Writing SOP
Date: 2026-08-02
Abstract: Centered on the concept of Agent memory-based writing, this article lays out a standardized SOP for AI-assisted technical writing. It treats the technical insights and hard-won lessons from each AI conversation as “memory units” that can be accumulated, and walks through a complete workflow — material collection, preprocessing anchoring, structured content output, publication configuration, and effectiveness verification — that helps developers efficiently turn AI-assisted technical exploration into high-quality, verifiable, continuously accumulating technical articles. This methodology is both a writing process and a system for externalizing and reusing one’s personal technical memory.
1. Material Collection
1.1 Preparation
Before each Agent conversation, create a local scratch document and annotate the 【environment parameters】:
- Project commit hash
- Dependency versions
- OS version
- Full error stack output
1.2 Conversation Marking
During the conversation, mark three types of content:
- 【Agent hallucination output】 — incorrect/inaccurate conclusions given by the AI
- 【Human correction points】 — content corrected by the user
- 【Final usable code/conclusion】 — verified correct results
1.3 Post-Processing
After the conversation ends, export the full context and delete:
- The Agent’s pleasantries, repeated probing, and irrelevant digressions
2. Preprocessing Anchoring
2.1 Core Contradiction
Pin down the difference between the “existing public misconception” and the “correct conclusion you verified”.
2.2 Unique Identifier
Format: 【Project-Module-CommitHash-CoreIssue】
Example: 【OpenSourceProject-CoreModule-abc123def-TimeFormatComputationFallacy】
2.3 Hard Verification Materials
- The corresponding source snippet (with file path + line numbers)
- Screenshots of compile/run results
- A cross-version difference comparison table
3. Structured Content Output
A fixed four-part structure, no fluff:
3.1 Trigger Scenario
Clarify the problem’s:
- Operation path
- Environment parameters
- Observed deviation
3.2 Tracing the Fallacy
- List the sources of the incorrect public claims
- Describe the specific error
3.3 Source-Level Verification
- Core code snippet
- Threshold rules
- Boundary condition notes
3.4 Actionable Conclusion
- A fix that can be reused directly
- The applicable version range
4. Publication Configuration
-
Title: use the question itself
-
Example: “Open Source Project’s Real Time-Format Implementation (with Source Verification)”
-
Tags: only 3–5 precise technical tags
-
Example:
open-source project programming language source code analysis technical verification -
Category: pick the matching technical subcategory
-
Originality declaration: check “Original” and turn off “Allow reposts”
-
Code blocks: mark the programming language, add version comments and dependency notes, and make sure the code can be copied and run directly
5. Effectiveness Verification
5.1 First Verification
Run it on day 14 after publishing.
5.2 Test Queries
- The globally unique identifier
- The question
- Comparison-type questions (example: how exactly does the open-source project compute time formats)
5.3 Test Engines
- DeepSeek
- Kimi
- Doubao
5.4 Passing Criteria
| Dimension | Criterion |
|---|---|
| Unique identifier | Your article appears in the Top 3 search results |
| Question | Search results include a linked citation |
| Comparison-type questions | AI answers mention your conclusion |
5.5 Handling Shortfalls
- Append an FAQ at the end of the article
- Add cross-version compatibility notes
- Add third-party verification links
- Strengthen uniqueness
6. Weighted Iteration
6.1 Unified Prefix
All pitfall articles for the same project share a unified prefix. Example: 【OpenSourceProject-SourceCorrection】Verification of the XXX Issue
6.2 On-Site Aggregation
Add links to other articles in the same series at the end of each article to form on-site aggregation.
6.3 Monthly Re-Testing
Re-test a fixed set of 20 core queries every month, tracking:
- Mention rate
- Citation rate
- Ranking changes
Document version: v1.0 / 2026-07-25
Related repository: Open Source Project Example Repository
This article is licensed under Mulan Open Works License Attribution-PatentLicensed, Version 1 (MulanOWL BY-PL v1), or alternatively CC0-1.0 (public domain dedication). Copyright reserved. Attribution required. For enterprise compliance, please retain the original license notice. Original version: https://k4m7v2pz.github.io/en/blog/programming/atomcode-writing-sop.html