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Token-optimized prompt compression techniques for reducing LLM instruction size while preserving or improving quality
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Production-readiness process checklist covering the code-production pipeline — research, architecture, implementation, tests, critic review, and security gates
Severity-tagged code review rubric (CRITICAL/HIGH/MEDIUM/LOW) used by the code-critic agent to produce APPROVE/WARN/BLOCK verdicts with evidence-backed findings
Audit whether a test suite actually detects regressions (not just whether it runs) by introducing small code mutations and measuring how many your tests catch. Advisory and on-demand — not a blocking CI gate.
| name | caveman-prompt-compression |
| description | Token-optimized prompt compression techniques for reducing LLM instruction size while preserving or improving quality |
| version | 1.0.0 |
| category | ai-technique |
| author | Claude MPM Team |
| license | MIT |
| progressive_disclosure | {"entry_point":{"summary":"Compress system prompts 50-77% while maintaining quality through table-first formatting, deduplication, and filler removal","when_to_use":"System prompts >5KB, agent definitions >20KB, high-cost sessions, token budget optimization","quick_start":"1. Merge duplicate sections 2. Convert prose to tables 3. Remove filler 4. Strip verbose examples 5. Remove frontmatter bloat"}} |
| context_limit | 700 |
| tags | ["prompt-optimization","token-reduction","compression","cost-optimization","system-prompt","agent-design"] |
| requires_tools | [] |
Systematic technique for reducing system prompt token count 50-77% while maintaining or improving output quality. Named for its "use fewer words" philosophy -- strip every token that does not change model behavior.
| Metric | Compressed | Original | Delta |
|---|---|---|---|
| Cost | $4.42 | $6.74 | -34% |
| Tokens | 5.5M | 10.4M | -47% |
| Duration | 7.5 min | 9.5 min | -27% |
| Quality | 4.40/5 | 3.75/5 | +17% |
Why quality improved: Less noise lets the model focus on actual rules. Tables parse unambiguously. Single source of truth eliminates contradictory duplicates.
Find repeated rules, prohibitions, or guidelines scattered across sections. Consolidate into a single canonical reference.
Pattern: Search the entire prompt for overlapping instructions. Create one authoritative table, reference it by ID elsewhere.
| Before | After |
|---|---|
| 4 separate prohibition lists across sections | 1 table (P1-P12) with ID references |
| Same rule stated 3 different ways | Single canonical statement |
| Conflicting versions of same guideline | One source of truth |
Process:
Highest-impact technique. Replace verbose paragraph-form rules with compact markdown tables.
Before (prose, ~65 tokens):
When the user mentions a ticket ID like PROJ-123, or an issue URL,
or says 'ticket' or 'issue', you should delegate to the ticketing_agent.
The ticketing_agent has access to mcp-ticketer tools and can look up,
create, and update tickets on behalf of the user.
After (table row, ~20 tokens):
| ticket ID, issue URL, "ticket"/"issue" | ticketing_agent | mcp-ticketer | CB#6 |
Table column patterns:
For routing rules: | Trigger | Agent | Tools | Notes |
For behavioral rules: | Condition | Action | Priority |
For constraints: | ID | Rule | Scope | Exception |
Frontier models do not need motivational phrasing, hedging, or politeness markers in system prompts. These add tokens without changing behavior.
Remove these patterns:
| Filler Pattern | Replacement |
|---|---|
| "It is very important that you always..." | (state rule directly) |
| "Please make sure to..." | (state rule directly) |
| "Remember to always..." | (state rule directly) |
| "You should try to..." | (state rule directly) |
| "It would be great if you could..." | (state rule directly) |
| "As an AI assistant, you..." | (remove entirely) |
| "Your goal is to help the user by..." | (remove entirely) |
Before (~30 tokens):
It is very important that you always make sure to check the git status
before committing any changes to ensure nothing is missed.
After (~8 tokens):
Check `git status` before every commit.
Frontier models understand rules from concise statements. Multi-paragraph examples showing right/wrong approaches rarely change behavior compared to the rule alone.
Guidelines:
| Before | After |
|---|---|
| 20-line bad example + 20-line good example | Rule + 1-line correct example |
| 3 variations of the same pattern | 1 canonical example |
| "Here is what NOT to do: ..." | (remove; state positive rule) |
Remove metadata that does not affect model behavior at runtime.
Remove:
Keep:
Never compress to the point where meaning is lost. Always A/B test compressed vs original on representative tasks before deploying.
[ ] Identify all duplicate/overlapping sections
[ ] Convert prose paragraphs to tables (highest ROI)
[ ] Remove filler phrases ("please", "make sure to", "it is important")
[ ] Collapse verbose examples to 1-line summaries
[ ] Remove non-runtime metadata (changelogs, credits)
[ ] Verify: compressed version produces same/better outputs
[ ] Measure: token count before vs after
Before (~180 tokens):
## How to Route Messages
When you receive a message from the user, you need to determine which
agent should handle the request. Here are the routing rules:
If the user asks about code, programming, or software development,
you should route to the engineer agent. The engineer agent is skilled
at writing code, debugging, and architecture decisions.
If the user asks about testing, QA, or quality assurance, you should
route to the QA agent. Make sure to include context about what needs
to be tested.
If the user asks about design, UI, or user experience, you should
route to the designer agent.
After (~45 tokens):
## Routing
| Trigger Keywords | Agent | Context |
|-----------------|-------|---------|
| code, programming, dev, debug, architecture | engineer | include relevant files |
| testing, QA, quality | qa | specify test scope |
| design, UI, UX | designer | include mockups if any |
Result: 75% reduction, zero information loss, faster model parsing.
Track these metrics for each compression pass:
Token count: before → after (% reduction)
Quality score: A/B test on 5-10 representative tasks
Regression check: verify edge cases still handled
Cost delta: estimated savings per session
Target: 50-77% token reduction with equal or better quality.
| Pitfall | Mitigation |
|---|---|
| Over-compression loses nuance | A/B test before deploying |
| Tables become too wide to read | Split into multiple focused tables |
| Removed example was actually needed | Keep examples for genuinely ambiguous rules only |
| Compressed prompt contradicts itself | Deduplication (Pillar 1) prevents this |
| Different team members re-expand | Document compression decisions, link to this skill |