EasyPlatform prompt-compress

[IMPORTANT] Use

TaskCreate

to break ALL work into small tasks BEFORE starting.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

AI Mistake Prevention — Failure modes to avoid on every task:

• Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal.
• Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing.
• Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain.
• Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path.
• When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site.
• Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code.
• Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks.
• Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis.
• Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly.
• Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Quick Summary

Goal: Two-phase optimization of any markdown prompt file: (1) Caveman Compression — aggressively strip stop words and grammatical scaffolding while preserving semantic meaning; (2) Prompt Enhancement — apply AI attention anchoring so AI actually reads and follows all instructions.

Workflow:

1. Read — Read the target file completely
2. Compress — Apply caveman compression pass (Phase 1)
3. Enhance — Apply prompt enhancement transforms (Phase 2)
4. Verify — No content loss, correct structure, rule density ≥ pre-optimization

Key Rules:

• Compress FIRST, enhance SECOND — compression removes noise; enhancement then structures signal
• Never remove meaningful rules, constraints, code examples, or

  file:line

  evidence
• Post-optimization rule density (MUST ATTENTION/NEVER/ALWAYS per 100 lines) must be ≥ pre-optimization
• Caveman compression applies to prose only — never compress code blocks, YAML, or structured tables
• Prompt quality > token count, but verbose prompts degrade quality — optimize clarity-per-token

Output Quality — Token efficiency without sacrificing quality.

1. No inventories/counts — AI can

   grep | wc -l

   . Counts go stale instantly
2. No directory trees — AI can

   glob

   /

   ls

   . Use 1-line path conventions
3. No TOCs — AI reads linearly. TOC wastes tokens
4. No examples that repeat what rules say — one example only if non-obvious
5. Lead with answer, not reasoning. Skip filler words and preamble
6. Sacrifice grammar for concision in reports
7. Unresolved questions at end, if any

Target File

Compress and enhance this file: <target>$ARGUMENTS</target>

If no file specified, ask via

AskUserQuestion

Phase 1: Caveman Compression

Aggressively remove stop words and grammatical scaffolding while preserving meaning. Think like a caveman — use only content words that carry semantic weight.

What to Remove

What to Keep (Always)

at least

approximately

more than

15

many

appears to be

seems

might

what sounded like

from

with

without

stuck to

every Tuesday

weekly

always

never

Dr.

Mr.

Senator

not

no

never

without

Preposition Decision Rule

• Keep when defining relationship:

  made from wood

  (keep

  from

  ),

  stuck to wall

  (keep

  to

  )
• Remove when purely grammatical:

  system for processing data

  →

  system processing data

• Keep

  in/on/at

  for location/position:

  file in /src

  (keep) vs

  written in prose

  (remove)

Compression Examples

from

Compression Scope

Apply to:

• Prose paragraphs and explanatory text
• Bullet point descriptions
• Rule statements (keep imperative verbs)
• Section intros and transitions

Do NOT compress:

• Code blocks (any language)
• YAML frontmatter
• Structured tables (column values may be fragmented — keep as-is)

• file:line

  references and paths

• <!-- SYNC -->

  tags and their content
• Frontmatter fields

Phase 2: Prompt Enhancement

Applies after compression. Source: Anthropic prompt engineering guide, Stanford "lost-in-the-middle" research, 2025-2026 LLM context optimization studies.

Context Engineering Principles — Research-backed principles for prompt quality. Source: Anthropic prompt engineering guide, Stanford "lost-in-the-middle" research, 2025-2026 LLM context optimization studies.

1. Primacy-Recency Effect — LLM performance drops 15-47% for middle-context information (Stanford). AI attention peaks at first/last 10% of text. Action: Place the 3 most critical rules in both the first 5 lines AND the last 5 lines of every prompt. Queries at end improve quality by up to 30% (Anthropic).
2. High-Signal Density — Anthropic: "Identify the smallest collection of high-signal tokens that maximize the probability of the desired outcome." Action: Every line should change AI behavior. If removing a line doesn't change output → cut it. Target ≥8 rules (MUST ATTENTION/NEVER/ALWAYS) per 100 lines.
3. Context Rot — LLM performance degrades as context length grows — even when all content is relevant. Compression (5-20x) maintains or improves accuracy while saving 70-94% tokens. Action: Compress aggressively. Shorter, denser prompts outperform longer, diluted ones.
4. Structured > Prose — Tables, bullets, XML/markdown parse faster than paragraphs. Constrained formats reduce error rates vs free-text. Action: Convert narrative to tables/bullets. Use markdown headers for semantic sections.
5. RCCF Framework — Modern LLMs (2025+) already know how to reason. What they need: Role (personality), Context (grounding), Constraints (guardrails), Format (structure). Constraints and format matter more than verbose instructions.
6. Checkbox Avoidance —

   [ ]

   syntax triggers mechanical compliance — AI ticks boxes without reasoning. Bullet rules force reading and evaluation. Action: Replace

   - [ ] Check X

   with

   - MUST ATTENTION verify X

   .
7. Example Economy — 3-5 examples optimal for few-shot; diminishing returns after. Action: 1 best example per pattern. Use BAD→GOOD pairs (2-3 lines each) for anti-patterns.
8. Deferred Tool Loading — Claude Code delays loading tool definitions when they exceed 10% of context window. Action: Keep injected docs well under 10% of context budget. Docs exceeding ~3,000 lines are too large for injection — split or compress.
9. Rule Density Verification — Post-optimization rule count (MUST ATTENTION/NEVER/ALWAYS) must be ≥ pre-optimization count. Compression should preserve or increase density, never decrease it. Action: Count before and after every optimization pass.

Prompt Enhancement Transforms (Base) — Transforms 1-3 are identical across

prompt-enhance

/

prompt-compress

/

prompt-expand

. Transform 4 is per-skill (conciseness pass for enhance/compress; structural clarity pass for expand) and stays local to each skill.

Transform 1: Inline Summaries for READ References

Problem: AI sees

MUST ATTENTION READ file.md

and skips it. Solution: Add a 2-3 line summary of key rules BEFORE the read instruction.

Before:

MUST ATTENTION READ .claude/protocols/evidence.md

After:

> **Evidence-Based Reasoning** — Speculation is FORBIDDEN. Every claim requires `file:line` proof.
> Confidence: >95% recommend freely, 80-94% with caveats, <80% DO NOT recommend.

MUST ATTENTION READ .claude/protocols/evidence.md for full details.

Scope rules:

• .claude/

  protocol files → always add an inline summary (stable, belongs to framework)

• docs/project-reference/

  files → NO inline summary (varies per project, auto-injected by hooks). Add:

  (content auto-injected by hook — check for [Injected: ...] header before reading)

Transform 2: Top Summary Section

Required structure (first 20 lines after frontmatter):

> **[IMPORTANT]** TaskCreate instruction...

> **Protocol Name** — [inline summary]. MUST ATTENTION READ `path` for details.

## Quick Summary

**Goal:** [One sentence — what this skill achieves]

**Workflow:**

1. **[Step]** — [description]

**Key Rules:**

- [Most critical constraint]

Transform 3: Bottom Closing Reminders

Add at the very end of the file:

---

## Closing Reminders

- **IMPORTANT MUST ATTENTION** [echo rule #1 from the top section]
- **IMPORTANT MUST ATTENTION** [echo rule #2]
- **IMPORTANT MUST ATTENTION** [echo rule #3]
- **IMPORTANT MUST ATTENTION** add a final review task to verify work quality

Pick 3-5 rules AI most commonly violates. Bottom section re-anchors attention after the long middle.

Shared Protocol Duplication Policy — Inline protocol content in skills (wrapped in

<!-- SYNC:tag -->

) is INTENTIONAL duplication. Do NOT extract, deduplicate, or replace with file references. AI compliance drops significantly when protocols are behind file-read indirection. To update: edit

.claude/skills/shared/sync-inline-versions.md

first, then grep

SYNC:protocol-name

and update all occurrences.

Transform 4: Token Optimization (Conciseness Pass)

After caveman compression, apply structural cleanup:

Cut:

• Filler phrases — "It is important to note that", "Please make sure to" → state the rule directly
• Redundant explanations — if heading says it, body need not re-explain
• Duplicate content — merge sections saying same thing (except intentional top/bottom anchoring)
• Overly verbose examples — trim to minimum lines demonstrating pattern

Keep:

• Code examples with actual file paths/patterns
• Decision tables and lookup references
• Anti-pattern before/after pairs
• All

  file:line

  evidence and concrete paths
• Top/bottom anchoring (intentional duplication)

Process

Step 1: Read and Analyze

1. Read target file completely
2. Record: current line count, rule density (MUST ATTENTION/NEVER/ALWAYS count)
3. List all READ references → classify as

   .claude/

   (needs inline summary) or

   docs/

   (skip)
4. Identify: missing Quick Summary, missing Closing Reminders, prose-heavy sections

Step 2: Caveman Compression Pass

1. Identify all prose paragraphs and bullet descriptions
2. Apply compression rules — remove stop words, keep semantic content
3. Skip code blocks, YAML, tables, SYNC tags, file paths
4. Verify meaning preserved after each paragraph

Step 3: Create Inline Summaries

For each

.claude/

1. Read the referenced file
2. Extract 2-3 key rules
3. Write blockquote inline summary
4. Keep MUST ATTENTION READ instruction on next line

Step 4: Add/Fix Top Section

• If Quick Summary missing → create from file content
• If present but weak → strengthen with Goal, Workflow, Key Rules
• Protocol summaries appear before Quick Summary

Step 5: Add/Fix Bottom Section

• If Closing Reminders missing → add standard section
• Pick rules AI most commonly skips
• Remove old "IMPORTANT Task Planning Notes" if superseded

Step 6: Verify

.claude/

docs/

Closing Reminders

• IMPORTANT MUST ATTENTION apply caveman compression FIRST before any structural enhancement — never skip Phase 1
• IMPORTANT MUST ATTENTION never compress code blocks, YAML frontmatter, structured tables, or SYNC tags
• IMPORTANT MUST ATTENTION verify rule density post-compression ≥ pre-compression — compression must not dilute signal
• IMPORTANT MUST ATTENTION apply primacy-recency anchoring — 3 critical rules in first 5 AND last 5 lines of every enhanced file
• IMPORTANT MUST ATTENTION add inline summaries only for

  .claude/

  protocol files, never for

  docs/

  project files
• IMPORTANT MUST ATTENTION cite

  file:line

  evidence for every claim (confidence >80% to act). NEVER speculate without proof.
• IMPORTANT MUST ATTENTION READ

  CLAUDE.md

  before starting <!-- SYNC:critical-thinking-mindset:reminder -->
• MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact. <!-- /SYNC:critical-thinking-mindset:reminder --> <!-- SYNC:ai-mistake-prevention:reminder -->
• MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction. <!-- /SYNC:ai-mistake-prevention:reminder -->