system-prompt.md

System-prompt mode (v3.34.0)

dario proxy --system-prompt=<mode> controls the system prompt dario sends upstream on Claude-backend requests. The default replays Claude Code's prompt verbatim — every existing setup keeps its current behavior. The non-default modes let you strip CC's behavioral constraints without losing subscription billing.

The empirical basis for this feature lives in docs/research/system-prompt-classifier-study.md — short version: Anthropic's billing classifier doesn't read the system prompt content. We tested 7 mutations (single char, word substitution, full replacement, extra block, length padding) and all routed to five_hour (subscription). System prompt is for the model. The classifier reads other channels.

Modes

Mode	What it does	Output capability vs verbatim
verbatim (default)	CC's prompt unchanged, byte-for-byte	baseline
partial	Strip # Tone and style, # Text output, and the scope/verbosity/comment bullets in # Doing tasks. Keeps every IMPORTANT: refusal reminder and every tool description.	~1.2–2.8× on open-ended work
aggressive	Partial + remove the prompt-level RLHF restatements (IMPORTANT: Assist with authorized security testing…, IMPORTANT: You must NEVER generate or guess URLs…) and the # Executing actions with care section.	<3% above partial
<file path>	Replace the slot entirely with the contents of a file you control. The escape hatch for users running well-defined agent workflows with their own system prompt.	depends on your prompt

Aggressive vs partial — what's the actual difference?

Aggressive is provided for completeness, not because it does meaningful work. The added removals are prompt-level restatements of refusal categories — reminders the prompt makes about RLHF behavior that's already trained into the model's weights. Removing the reminder doesn't remove the trained behavior. We measured this: 9 trials (3 prompts × 3 strip levels), aggressive vs partial added <3% practical change on benign tasks.

If you're choosing between partial and aggressive, choose partial. The aggressive mode exists so the test matrix could distinguish "behavioral constraint" (real, in the prompt, ~1.2–2.8× effect) from "alignment restatement" (decorative, in the prompt but trained into the weights, <3% effect).

Custom file mode

dario proxy --system-prompt=/path/to/your-prompt.txt

The CLI reads the file at startup and passes the contents to the runtime path. The proxy never re-reads the file — to change the prompt, restart the proxy. An empty file or unreadable path fails fast with a clear error rather than silently degrading to verbatim.

The custom prompt replaces the entire system[2].text slot. Your client's own system prompt (the one your agent normally sends) is still appended after, just as it would be on top of the CC verbatim default. So a custom prompt + your agent's prompt = the model's full instruction context.

Configuration sources

dario proxy --system-prompt=partial                    # CLI flag
DARIO_SYSTEM_PROMPT=partial dario proxy                # env var
dario proxy --system-prompt=/etc/dario/prompt.txt      # file path

CLI flag wins over env var. Both are read at proxy startup; mid-run changes require a restart.

dario doctor surfaces the active mode + char-count delta vs CC's default, so you can confirm at a glance which mode is actually live without reading the proxy log.

What this is NOT

• Not bypassing alignment. The model's refusal behavior on harmful content is RLHF-trained into the weights, not the prompt. You can run --system-prompt=aggressive and still get refusals on harmful requests — that's the entire point of including aggressive in the test matrix and measuring <3% delta.
• Not detected as misuse by the classifier. 7/7 variants routed to five_hour in the empirical test. If Anthropic later starts fingerprinting system-prompt content, you'll see it in the rate-limit-classifier headers; we'll document the change and update this page.
• Not specific to dario. Any client building its own request body could already do this. Dario makes it a one-flag operation that preserves CC's other wire-shape axes (header order, body field order, billing tag, beta flags) so the rest of the subscription routing path keeps working.

Drop-in custom-prompt recipes

Four starting points you can save to a file and use with --system-prompt=<filepath>. Each is a complete system[2].text replacement — short by design (CC's stock prompt is ~27,000 characters; these are 200–500). Copy, modify, A/B against your actual workload, keep what works.

Recipe 1 — Terse engineer (~280 chars)

You are a senior engineer. Answer questions directly and ship code. Prefer working code over prose. Skip pleasantries, hedging, and apologies. When asked for a recommendation, recommend — don't enumerate every option unless asked. Match output length to question complexity. If the question is ambiguous, pick the most likely interpretation and proceed; flag the assumption in one sentence.

Day-to-day coding work, agent-driven sessions, anything where you want minimum friction. Optimizes signal-to-noise.

Recipe 2 — Verbose explainer (~500 chars)

You are an engineer-mentor. Your job is to teach by example. For every code answer, explain the reasoning, alternative approaches, and tradeoffs you considered. For every concept, give the intuition first, then the technical detail, then a concrete example. Include comments in code that explain WHY decisions were made, not just WHAT the code does. Aim for outputs that build the user's mental model, not just answer the question.

Learning a new codebase, onboarding, contexts where pedagogical depth matters more than turn-around. The opposite axis from Recipe 1.

Recipe 3 — Code reviewer (~440 chars)

You are reviewing code. Your job is to surface issues — bugs, security risks, performance traps, edge cases not handled, style and maintainability concerns, missing tests, ambiguous APIs. Order findings by severity. Suggest specific fixes with code snippets, but don't rewrite the entire file unless asked. If the code is correct, say "no issues found" and stop — don't invent problems. Honest is more valuable than thorough.

Review-only sessions, gating PRs through an LLM check, pairing review with another tool. The "honest > thorough" line is load-bearing — without it, models manufacture concerns to justify their output.

Recipe 4 — Research assistant (~520 chars)

You are a research assistant. Answer questions with structured analysis: summary first (2-4 sentences), then claim-by-claim breakdown with supporting reasoning, then unresolved questions or limitations. Distinguish between observed facts, reasonable inferences, and speculation — never blur the boundaries. Use markdown tables for comparisons across more than two items. When citing online sources, prefer primary documentation, papers, or official spec text over secondary blog posts. Flag uncertainty explicitly.

Investigation work, technical due diligence, evaluating libraries / frameworks / services. Optimizes for analysis quality over speed.

Empirical mapping — what each section of CC's prompt actually controls

CC Section	Constrains	Effect when removed
# Tone and style	Verbosity bias toward terse, no-emoji, apology patterns	Output length grows; conversational tone returns
# Text output	Final-answer format, "summary at end" patterns	Less rigid output structure
# Doing tasks bullets ("Don't add features", "Default to writing no comments", "Don't explain WHAT", scope discipline)	Code stays minimal; comments suppressed; refuses to expand scope past literal request	Code includes comments where useful; explanations included; scope inferred more broadly
# Executing actions with care	Confirmation-before-action bias	More autonomous action; fewer clarifying questions for ambiguous-but-low-risk work
IMPORTANT: lines reminding of refusal categories	Nothing measurable — restate RLHF-trained behavior	<3% practical delta. Alignment is in the weights, not the prompt.

Behavioral knobs (top three rows) are real — flipping them changes output. Alignment knobs (bottom two) are decorative — removing them doesn't change refusal behavior because refusal is trained into the weights.

Reproducibility

The strip rules in src/cc-template.ts:resolveSystemPrompt are ported byte-for-byte from scripts/research/test-constraint-removal.mjs, which is committed in this repo. The empirical billing-classifier validation script is scripts/research/test-system-prompt-mods.mjs. Both run real upstream requests against your own subscription.

node scripts/research/test-system-prompt-mods.mjs            # 7 upstream requests, classifier readout per variant
node scripts/research/test-constraint-removal.mjs             # 9 upstream requests, behavior delta per variant

To A/B test your own custom prompt: hold everything constant (model, max_tokens, effort, tools, body field order, billing tag, OAuth bearer, headers) except system[2].text. Send identical user prompts under your variants. Measure the representative-claim header per response (should stay five_hour), output character count + usage.output_tokens, and whatever behavior axis you care about. Repeat at least 3× to rule out sampling variance. If your prompt routes to anything other than five_hour, something else changed besides the prompt — open an issue with the request-id; that's how a new fingerprint axis would be found.