
Bitter Pill Engineering
FreeStreamline AI instruction sets for better performance.
Free · Opens the source repo
What Bitter Pill Engineering does
Bitter Pill Engineering is a specialized tool designed to audit AI instruction sets, focusing on the issue of over-prompting. As AI models evolve, instruction sets can become bloated with redundant or contradictory rules that degrade performance. This skill addresses that problem by applying a systematic evaluation process known as the Five Questions to each rule within the instruction set. The skill classifies rules into categories such as CUT, RESOLVE, MERGE, EVALUATE, SHARPEN, MOVE, and KEEP, providing users with an actionable report on how to optimize their instruction sets.
The core principle behind Bitter Pill Engineering is that less scaffolding leads to better output. By questioning whether a smarter model could render a rule unnecessary, users can identify and eliminate extraneous instructions that do not contribute to improved performance. This process not only helps in cleaning up the instruction sets but also estimates potential token savings, making it a cost-effective solution for users who are conscious of resource utilization.
Bitter Pill Engineering includes two primary workflows: Audit and QuickCheck. The Audit workflow is designed for comprehensive evaluations of entire systems, while QuickCheck allows users to assess specific files quickly. This flexibility makes it suitable for various scenarios, whether users are looking to perform a full audit of their AI setup or simply want to check a single file for redundancy.
This skill is particularly valuable for developers and designers working with AI models who need to maintain efficient and effective instruction sets. By utilizing Bitter Pill Engineering, users can ensure that their AI systems are not only functional but also optimized for performance, leading to better outcomes in their projects.
When to use it
Use this skill when you need to audit AI instruction sets for over-prompting or to simplify and optimize your setup.
When not to use it
This skill is not suitable for addressing logical flaws in ideas; for that, consider using a RedTeam approach.
What you can build with it
Full System Audit
Run a comprehensive audit of your entire AI instruction set to identify redundant or unnecessary rules.
Quick File Check
Quickly assess a single file for over-prompting and receive immediate feedback on rule efficiency.
Post-Cleanup Validation
After trimming your rules, use the skill to validate that no redundant instructions remain.
How to install Bitter Pill Engineering
View source1. Install with the skills CLI
npx skills add danielmiessler/lifeos/BitterPillEngineering --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by danielmiesslerCustomization
Before executing, check for user customizations at:
~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/BitterPillEngineering/
If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.
Voice Notification
When executing a workflow, do BOTH:
-
Send voice notification:
curl -s -X POST http://localhost:31337/notify \ -H "Content-Type: application/json" \ -d '{"message": "Running the WORKFLOWNAME workflow in the BitterPillEngineering skill to ACTION"}' \ > /dev/null 2>&1 & -
Output text notification:
Running the **WorkflowName** workflow in the **BitterPillEngineering** skill to ACTION...
BitterPillEngineering
What It Does
Audits any AI instruction set for over-prompting. It runs every rule through Five Questions — does Claude already do this, does it contradict another rule, is it redundant, was it a one-off fix, is it vague — then classifies each as CUT, RESOLVE, MERGE, EVALUATE, SHARPEN, MOVE, or KEEP, with an estimate of the tokens you'd save. Two workflows: Audit (full system) and QuickCheck (single file).
The Problem
Instruction sets accumulate. Every time the model does something wrong, someone adds a rule, and over months the file fills with instructions that restate default behavior, contradict each other, or fixed one bad output that never recurred. The cost is hidden: every unnecessary rule competes for attention and degrades the rules that actually matter, so a bloated setup produces worse output than a lean one. The hard part is telling load-bearing rules from dead weight — which is what this audit does, rule by rule.
How It Works
Built on the principle that less scaffolding = better output. The core test for every rule: "Would a smarter model make this unnecessary?" If yes, it's scaffolding, not architecture, and it's a candidate to cut. The Five Questions and the classification table below drive the verdict for each rule.
Workflow Routing
| Workflow | Trigger | File |
|---|---|---|
| Audit | "audit setup", "full audit", "check all rules" | Workflows/Audit.md |
| QuickCheck | "quick check", "check this file", "check these rules" | Workflows/QuickCheck.md |
Examples
Example 1: Full system audit
User: "Run BPE on my setup"
→ Invokes Audit workflow
→ Reads all force-loaded files from settings.json
→ Evaluates each rule against the Five Questions
→ Returns categorized report with estimated token savings
Example 2: Check a single file
User: "Quick check this CLAUDE.md"
→ Invokes QuickCheck workflow
→ Reads the target file
→ Returns concise keep/cut/sharpen verdict
Example 3: Post-cleanup validation
User: "I trimmed my rules, check if anything's still redundant"
→ Invokes Audit workflow
→ Compares remaining rules against Claude defaults
→ Flags any surviving dead weight
Gotchas
- Claude's built-in system prompt changes across versions — what was "default behavior" 3 months ago may not be now. When in doubt, test rather than assume.
- Rules that seem redundant with defaults may have been added because Claude was inconsistent about following the default. Check failure history before cutting.
- "One-off fix" rules sometimes prevent recurring failures. Check if the failure pattern is truly gone before removing.
- The
loadAtStartuplist in settings.json andpostCompactRestore.fullFilesmust stay in sync — if you remove a file from one, check the other. - Deterministic drift detection exists:
bun ~/.claude/LIFEOS/TOOLS/SkillDriftLint.ts --dir skills/ [--strict] [--top N](ported from @rpriven, public issue #1523). Advisory-only V1/V2 pattern scan — use it to FIND candidates mechanically, then judge each against the four keep-classes with this skill's questions. Drift grows back after every cut; the linter is the continuous check, this skill is the judgment.
The Five Questions
For every rule, instruction, or preference found, evaluate:
- Default behavior? Does Claude already do this without being told?
- Contradiction? Does this conflict with another rule in the same or different file?
- Redundancy? Is this already covered by a different rule or file?
- One-off fix? Was this added to fix one specific bad output rather than improve outputs generally?
- Vague? Would Claude interpret this differently every time? (e.g., "be more natural", numeric personality scales)
The HOW-vs-WHAT Test (sixth question, first-class)
Beyond the Five Questions, audit every rule for procedural over-prompting: does it dictate execution methodology or reasoning choreography ("first analyze X, then consider Y, then decide Z") instead of articulating the ideal state (WHAT done looks like) plus the tools? If it scripts the model's HOW rather than naming the WHAT, it is scaffolding — flag it CUT.
Exception — four keep-classes are legitimate HOW, never cut them: safety-gate (confirmation/destructive-op guard/approval), verified-gotcha (a documented non-obvious failure), tool-contract (exact CLI/API/path recipe), output-format-contract (required deliverable shape). Deterministic tools (*.ts) are exempt. This is the positive form of the core test: a rule that survives "would a smarter model make this unnecessary?" is either a keep-class or genuine architecture. Full doctrine: LIFEOS/RULES/Philosophy.md § Ideal-State Prompting.
Classification
| Category | Action |
|---|---|
| Restates default behavior | CUT — the model already does this |
| Contradicts another rule | RESOLVE — pick one, cut the other |
| Duplicates another rule | MERGE — one location, one statement |
| One-off fix for past mistake | EVALUATE — still relevant or already learned? |
| Vague / unquantifiable | SHARPEN — add specific DO/DON'T examples, or cut |
| Loaded but rarely actionable | MOVE to on-demand — load via the CLAUDE.md routing table when needed |
| Specific, actionable, non-default | KEEP — this is what good instructions look like |
Anti-Fragile vs Fragile
Keep (anti-fragile): Verification harnesses, ISC, data pipelines, specific DO/DON'T examples, tool preferences, routing rules.
Cut (fragile): CoT orchestrators, format parsers, retry cascades, numeric personality scales, abstract value statements, process descriptions that aren't followed.
Output Format
## BitterPillEngineering Audit
**Scope:** [what was audited]
**Files read:** [count]
**Rules evaluated:** [count]
### CUT (restating defaults)
- [rule] — [reason]
### RESOLVE (contradictions)
- [rule A] vs [rule B] — [which to keep and why]
### MERGE (redundancies)
- [locations] — [merge into where]
### EVALUATE (one-off fixes)
- [rule] — [still needed? verdict]
### SHARPEN or CUT (vague)
- [rule] — [sharpen how, or cut why]
### MOVE to on-demand
- [content] — [how often it's actually needed]
### KEEP (carrying weight)
- [rule] — [why it matters]
**Estimated savings:** [lines] lines, ~[tokens] tokens
Execution Log
After completing any workflow, append a single JSONL entry:
echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"BitterPillEngineering","workflow":"WORKFLOW_USED","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/LIFEOS/MEMORY/SKILLS/execution.jsonl
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