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Agent Architecture Intermediate · 60-90 minutes

Assemble a System Prompt Like the Pros

Structure your agent's system prompt the way leading harnesses do: layered sections, dynamic context, and enforceable rules.

Start Route · 4 steps

The route

4 steps to Done

  1. 01

    Decompose the current prompt

    Name every instruction's job before deciding where it lives.

    Preview prompt + verify gate ▾

    Take my current system prompt and decompose it. Classify every sentence into: identity (who the agent is), capabilities (what it can do), hard rules (must/never statements), tool guidance (when to use which tool), environment (paths, stack, versions), style (tone, format), and dead weight (contradictory, duplicated, or unverifiable lines). Produce a table of sentences to categories, flag duplicates and contradictions, and list the dead weight for deletion. Report the token count of each category.

    • Every sentence categorized
    • Duplicates and contradictions flagged
    • Dead weight listed with deletion rationale
  2. 02

    Build the section-based assembler

    Prompts as code: composable, reviewable, and measurable.

    Preview prompt + verify gate ▾

    Implement a prompt assembler. Requirements: each section is a named function or template returning text; the assembler concatenates sections in a fixed order (identity, capabilities, hard rules, tool guidance, environment, dynamic state) with clear markdown headers; dynamic sections receive a context object (cwd, date, git branch and status summary, active plan if any) evaluated at call time; the assembler logs a per-section token count on every build. Port the categorized content from the previous step into sections, deleting the dead weight. Verify the assembled output against a golden snapshot test.

    • Sections build in fixed order with headers
    • Dynamic context evaluated at call time
    • Snapshot test covers the assembled output
  3. 03

    Pair rules with enforcement

    A rule the harness cannot check is a wish.

    Preview prompt + verify gate ▾

    Take every hard rule and decide its enforcement. For each rule choose: (a) harness-enforced - implement a code check (e.g. 'never edit unread files' becomes a read-before-write guard; 'never touch .env' becomes a path blocklist in the write tool), (b) test-enforced - write an eval case that fails if the model violates it, or (c) prompt-only - keep it in the prompt but mark it as unverified risk. Produce a rules table: rule, enforcement type, implementation pointer. Implement at least the top three harness enforcements now and demonstrate each blocking a violation in a test.

    • Every rule has an enforcement decision
    • Three harness guards implemented and tested
    • Prompt-only rules are marked as accepted risk
  4. 04

    Ablate to prove section value

    Measure the prompt like a system, not a spell.

    Preview prompt + verify gate ▾

    Run an ablation pass over the assembled prompt. For each major section, build a variant with that section removed and run a fixed 5-task smoke suite (mix of tool use, rule compliance, and formatting). Record per-variant: task success, rule violations, and format breaks. Sections whose removal changes nothing are candidates for cutting or merging; sections whose removal breaks rules confirm their value. Deliver the ablation table and the final trimmed prompt with its total token cost compared to the original monolith.

    • Every section tested by removal
    • Suite covers tools, rules, and format
    • Final prompt smaller or better-justified than the original

Research-backed

Sources behind this flow

Tier 1 · claude-code-internals

Claude Code Orange Book

A book-length analysis ('Orange Book') dissecting Anthropic's AI engineering decisions from the 510,000 lines / 1,902 TypeScript files that shipped inside an npm package. Chapters cover the agent loop, context compaction, the hook system, and sub-agents - the concepts, not just the code.

Tier 1 · claude-code-internals

ClaudeCode-Source-Analysis (tammychurchly25)

A ~330-file markdown analysis of the leaked source (512,000+ lines, 1,906 files, TypeScript on Bun), organized module by module from the March 31, 2026 npm source-map leak. Its granularity makes it the best reference for looking up a specific subsystem.

Tier 2 · harness-engineering

Harness Engineering: from CC to AI coding

A full book on harness engineering ('from Claude Code source code to AI coding') with Chinese and English editions in book/ and book-en/. The single best conceptual source in this corpus: chapters on prompt assembly, tool design, context management, verification loops, and sub-agent patterns.

Tier 4 · coding-agents

Continue

An open-source IDE assistant whose architecture contribution is the context-provider system: pluggable providers (files, docs, terminal, issues) that assemble model context on demand.

Tier 4 · coding-agents

chatgpt_system_prompt

A large community collection of leaked/shared GPT system prompts and prompt-injection lore. Mined here for prompt-structure patterns: role framing, capability lists, constraint blocks, and output-format contracts.

Tier 1 · claude-code-internals

Deep Dive: Prompt-Layer Security in BashTool

Dissects how BashTool's prompt forms the first line of defense: a three-layer structure (tool preference chains, usage constraints, git-safety and sandbox protocols) assembled dynamically per environment. Shows how wording strategy works - capitalized NOT, positive alternatives ('use Edit' rather than 'avoid sed'), and 'better user experience' framing instead of 'security' - and how token budget (config dedup, conditional blocks) is treated as a first-class engineering constraint rather than an afterthought.