Prompt Enhance

Quick Summary

Goal: Two-phase optimization — (1) Caveman Compression strips stop words + grammatical scaffolding while preserving semantic meaning; (2) Prompt Enhancement applies AI attention anchoring so AI reads and follows all instructions — producing a prompt/skill that states its objective and ultimate outcome (one consolidated Goal) in both top summary and bottom reminders so AI optimizes for the right result.

Summary:

• Two phases, in order: caveman-compress prose FIRST, then attention-anchor structure — NEVER skip or reorder.
• Enhance derives BOTH a Goal (the outcome to optimize for) AND a Summary (key things + steps to notice) for the target, and places both in its Quick Summary.
• Anti-forget rule (task/purpose targets): when the target performs a task or has a purpose, the Summary AND Closing Reminders MUST carry the goal + purpose + ALL important main steps/tasks (compact enumeration) — why: AI forgets steps buried in the long middle of the prompt; the top Summary and bottom Reminders are the two high-attention anchors that survive context rot.
• Protect content: NEVER compress code/YAML/tables/SYNC tags, NEVER delete rules or file:line evidence; post rule-density MUST be ≥ pre.
• Route on --op (default enhance): compress = token-strip only, expand = reconstruct compressed text.

Workflow:

1. Detect — Classify target: skill file, sub-agent file (.claude/agents/*.md), protocol file, or general doc
2. Read — Read target file completely
3. Goal + Summary — Derive the target's one-sentence Goal (what it achieves + the ultimate outcome it must cause) AND its Summary (2-4 bullets of the key important things + the steps AI must notice) from the target's task, constraints, and success criteria
4. Compress — Apply caveman compression (Phase 1)
5. Enhance — Apply AI attention anchoring transforms (Phase 2)
6. Verify — No content loss, rule density ≥ pre-optimization, Goal anchored top and bottom

Key Rules:

• Operation flag (see Operation Mode): --op=enhance (default) = compress + anchor + skill-principles; --op=compress = token-strip only; --op=expand = reconstruct compressed text into fluent form (inverse Phase 1 + structural Transform 4)
• NEVER skip Phase 1 (compress) before Phase 2 (enhance) — compression removes noise, enhancement structures signal
• NEVER remove meaningful rules, constraints, code examples, or file:line evidence
• MUST ATTENTION derive the target's Goal and add it to both ## Quick Summary and ## Closing Reminders
• MUST ATTENTION derive the target's Summary (key important things + steps AI must notice) and place it in ## Quick Summary immediately after the Goal — a condensing digest at a different altitude than Workflow/Key Rules, NEVER a verbatim re-listing of them
• MUST ATTENTION when the target performs a task or has a purpose, the Summary AND ## Closing Reminders MUST enumerate the goal + purpose + ALL important main steps/tasks as a compact list — why: long task descriptions in the middle of the prompt get forgotten; the top Summary and bottom Reminders re-anchor every step so none is skipped (compact enumeration ≠ the verbose Workflow prose, so the altitude stays distinct)
• MUST ATTENTION skill AND sub-agent (.claude/agents/*.md) targets require the SAME Goal + Summary + Closing-Reminders structure (see When Target is a Sub-Agent File) — anchored top and bottom; NEVER alter SYNC blocks when enhancing an agent
• 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

Target File

Compress and enhance this file: $ARGUMENTS

No file? Ask via AskUserQuestion. Text passed (not file path)? Apply caveman compression directly and output result.

Operation Mode (--op=)

Route on --op (default enhance). Transforms 1-3 (inline summaries, top summary, closing reminders — the shared SYNC base block below) are identical across all ops; only Phase 1 and Transform 4 differ:

• enhance / compress → run Phase 1: Caveman Compression + Transform 4: Conciseness below. enhance additionally derives the Goal and (for skill files) applies the Universal Skill-Building Principles; compress skips both for a pure token-reduction pass.
• expand → run the Language Expansion branch below INSTEAD of Caveman Compression, and the Structural Clarity Transform 4 instead of conciseness.
• No --op provided → enhance.

--op=expand — Language Expansion branch

Reconstruct fluent, grammatically correct English from caveman-compressed text while preserving ALL semantic content (inverse of Phase 1). Run INSTEAD of Caveman Compression.

Restore (add back): articles (a/an/the); connectives matching the logical relationship (because/however/in order to); auxiliary verbs (is/are/was/has); clarifying prepositions; pronouns referencing prior nouns; subordinate clauses merging choppy sentences. Preserve exactly (never paraphrase/omit): all nouns + main verbs + adjectives, numbers/quantifiers, uncertainty qualifiers, negations (not/no/never/without), technical/domain terms, file:line paths, names/titles, time/frequency words.

Connective selection (match relationship, never arbitrary): cause→effect because/since/as a result; contrast however/although/despite; addition additionally/furthermore; sequence first/then/finally; purpose in order to/so that; condition if/when/unless; clarification specifically/that is.

Per sentence: identify core S-V-O (non-negotiable) → restore articles/auxiliaries/connectives/prepositions → merge related shorts → target 10-25 words. Skip code blocks, YAML, tables, SYNC tags, paths.

Transform 4 (expand) — Structural Clarity pass: convert prose rule-lists → bullets, enumerated conditions → decision tables, before/after examples → two-column tables. Keep as prose: explanatory context (why a rule exists), workflow narratives, anti-pattern rationale.

Verify (expand): no semantic loss (all facts/numbers/paths present), rule density post ≥ pre, no telegraphic 2-5 word prose sentences remain, code blocks untouched.

Phase 0: Detect Target Type

Before any other step, classify target:

When Target is a Skill File

Target .claude/skills/**/*.md (any SKILL.md)? Apply Universal Skill-Building Principles AFTER caveman compression, BEFORE writing enhanced output.

Risk-profile gate (blocking): Enhancement preserves the target skill's job, input/output, mutation authority, delegation boundary, and terminal states. Classify the target as content, analysis, conversion, implementation, orchestration, or security/authority before applying the checklist. Fresh agent review, specialist routing, inline sub-agent protocols, and recursive loops are mandatory only for implementation, orchestration, or security/authority targets (or when the target already owns an equivalent gate). For content, analysis, and conversion skills, record those rows as N/A — not required by the target contract; never add review machinery merely because this enhancer can add it. A change that widens mutation or delegation authority must be surfaced as a contract change, not silently introduced.

Skill Enhancement Checklist

After caveman compression, evaluate skill against each principle, add missing structure:

Anti-Forget Anchoring (task/purpose targets)

Any target that performs a task or has a purpose (skill, sub-agent, task-prompt) hides its main steps in the long middle — exactly the zone AI attention drops 15-47% (Stanford "lost-in-the-middle"). The fix is to mirror those steps into the two high-attention anchors:

• MUST ATTENTION enumerate EVERY important main step/task — completeness beats brevity here; a step omitted from both anchors is a step AI will skip — why: the Summary and Reminders are the only parts guaranteed to be read on a long prompt.
• The compact enumeration is a DIFFERENT altitude than the verbose ## Workflow/body — short phrases, not full prose — so it complements (never replaces) the detailed steps below.
• Surface conditional routing too (modes, --flags, gates) so the AI doesn't forget a whole branch — why: a forgotten mode silently runs the wrong path.

When Target is a Sub-Agent File

Target .claude/agents/*.md (a custom sub-agent definition — the shape a creator skill like custom-agent emits)? Apply the Sub-Agent Required Structure AFTER caveman compression, BEFORE writing enhanced output. Same Goal + Summary + Closing-Reminders contract as a skill file — anchored top and bottom so the isolated, zero-history sub-agent optimizes for the right outcome — mapped onto the agent body (## Role → ## Workflow → ## Key Rules → ## Output).

Sub-Agent Required Structure

• MUST ATTENTION add the missing **Summary:** and the Closing-Reminders Goal echo; lightly tighten Role/Workflow prose only — why: the structure must match a skill so creator skills emit one consistent shape.
• NEVER alter <!-- SYNC:... --> blocks or their :reminder variants — they are canonical-sync content; edit the canonical source (.claude/skills/shared/sync-inline-versions.md) instead — why: a divergent SYNC copy fails the verify-sync-divergence oracle.
• NEVER delete the agent body sections (## Role, ## Workflow, ## Key Rules, ## Output) — preserve them; only restructure the summary/closing anchors.

Phase 1: Caveman Compression

Applies to --op=compress|enhance. For --op=expand, run the Language Expansion branch (above) instead.

Aggressively remove stop words + grammatical scaffolding preserving meaning. Use only content words carrying semantic weight.

What to Remove

What to Keep (Always)

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

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

Transform 4: Token Optimization (Conciseness Pass)

Applies to --op=compress|enhance. For --op=expand, use the Structural Clarity pass (see expand branch above).

Prompt quality FIRST. Verbose prompts degrade quality — AI attention dilutes across unnecessary tokens. Optimize clarity-per-token: maximum signal, minimum noise.

What to cut:

• Filler phrases — "It is important to note that", "Please make sure to", "You should always" → just state the rule
• Redundant explanations — heading says it, body doesn't re-explain. Tables > paragraphs for structured data
• Duplicate content — merge sections saying same thing differently (except intentional top/bottom anchoring)
• Overly verbose examples — trim to minimum lines demonstrating pattern. Replace paragraph explanations with // comment in code
• Prose paragraphs for rules — convert to bullet lists or tables (AI parses structured formats faster)

What to KEEP:

• Code examples with actual file paths/patterns (AI copies these directly)
• Decision tables and lookup references
• Anti-pattern examples (before/after pairs)
• All file:line evidence and concrete paths
• Top/bottom anchoring (intentional duplication)

Evaluation metrics per doc:

• Density score — useful rules per 100 lines (higher = better)
• Savings estimate — % tokens saveable without losing information
• Risk — what breaks if cut too aggressively (e.g., AI misses a pattern)

Process

Step 0: Detect and Classify

1. Identify target type (skill file / protocol / general doc / raw text)
2. Skill file (.claude/skills/**/*.md) → apply Universal Skill-Building Principles after Phase 1

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. Derive the one-sentence Goal (what it achieves + ultimate outcome it must cause) from target task/outcomes/guardrails; cite source lines or mark inferred with confidence
5. Derive the Summary (2-4 bullets of the key important things + the steps AI must notice) — the read-this-if-nothing-else digest at a different altitude than Workflow/Key Rules; cite source lines or mark inferred with confidence — why: the Summary condenses what matters most, it does not re-list every step/rule
   • If the target performs a task or has a purpose, the Summary MUST also enumerate ALL important main steps/tasks (compact ordered list) + any modes/flags/gates — why: steps buried in the long middle get forgotten; the Summary anchor re-surfaces every one
6. Identify: missing Quick Summary, missing Goal, missing Summary, missing main-step enumeration (task targets), missing Closing Reminders, prose-heavy sections

Step 2: Caveman Compression Pass

1. Identify all prose paragraphs and bullet descriptions
2. Apply Phase 1 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/ protocol reference:

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

• Missing Quick Summary → create from file content
• Present but weak → strengthen with Goal, Workflow, Key Rules
• Ensure **Goal:** states what the skill achieves AND the ultimate outcome it must cause — a single consolidated line (never split the objective and outcome into two separate lines)
• Ensure **Summary:** is present in Quick Summary immediately after the Goal — create if missing, strengthen if weak; it condenses the key important things + the steps AI must notice at a different altitude than Workflow/Key Rules (NEVER a verbatim re-listing of them) — why: the Goal gives the outcome, the Summary gives the read-this-if-nothing-else digest
• For task/purpose targets, ensure the Summary enumerates ALL important main steps/tasks (compact ordered list) + modes/flags/gates — why: completeness on steps is the anti-forget guarantee
• Protocol summaries appear before Quick Summary

Step 5: Add/Fix Bottom Section

• Missing Closing Reminders → add standard section
• Pick rules AI most commonly skips (evidence-based, task creation, pattern search)
• Echo the same Goal near the start of Closing Reminders: **IMPORTANT MUST ATTENTION Goal:** ...
• For task/purpose targets, add a MUST ATTENTION line re-listing ALL important main steps/tasks in order — why: the bottom anchor re-surfaces every step after the long middle, matching the top Summary
• Remove old "IMPORTANT Task Planning Notes" if superseded by Closing Reminders

Step 6: Verify

[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting.

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

Universal Skill-Building Principles — 10 principles for building AI skills that work across any project type. Source: extracted from changes-review, plan-review, code-review skill rewrites.

Meta-principle: Teach AI to reason, not to recite. Skill's job: structure WHEN and HOW AI applies its existing knowledge — not enumerate every possible concern.

1. Detect Before Act — Every skill starts with a classification phase. Detect artifact type (plan type, code category, change nature) before applying any logic. Detection drives: sub-agent selection, which dimensions to emphasize, mandatory vs. optional checks. Anti-pattern: same checklist applied regardless of input type.

2. Derive, Don't Enumerate — Teach AI HOW to reason about a domain, not WHAT items to tick. Replace "check X, Y, Z" with "understand role → read conventions → derive concerns from first principles → execute with evidence." Fixed checklist = ceiling. Thinking framework = floor. Test: Can this skill run on a Python/Go project without modification? If not → it's enumerating, not teaching.

3. Evidence Gates — Every claim, finding, recommendation requires file:line proof or traced call chain. Confidence thresholds: >80% act freely, 60-80% verify first, <60% DO NOT recommend. "Insufficient evidence" is valid output. Speculation is forbidden output.

4. Fresh Eyes Protocol — For implementation, orchestration, and security/authority targets, Round 1 is in the main session and Round 2+ uses a fresh sub-agent (zero memory of Round 1); the main agent reads the report but NEVER filters or overrides findings. Max 3 rounds, then escalate to the user. For content, analysis, and conversion targets, apply only when the target contract explicitly requires an independent review; otherwise record N/A and preserve the target's simpler terminal state. Why: main agent rationalizes its own mistakes. Zero-memory sub-agent catches what main agent dismissed.

5. Specialize by Type — When the risk profile requires delegation, route to specialized sub-agents based on detected artifact type:

   Artifact type	Sub-agent
   Source code / diffs	code-reviewer
   Security-sensitive changes	security-auditor
   Performance-critical changes	performance-optimizer
   Plans / docs / specs	general-purpose

6. Embed Protocols Verbatim, Never Reference — When a target actually emits a sub-agent prompt, shared protocols MUST be copied inline into that prompt — never referenced by file path or tag name. Do not create a sub-agent prompt merely to satisfy this principle. Maintain canonical source; embed the needed body at each real call site.

7. Search-Based Discovery — Never hardcode project-specific paths, formats, or identifiers. Teach skill to discover them:

   • "Search for coding-standards, style-guide, contributing" not "read docs/X/code-review-rules.md"
   • "Find the project's test format near changed files" not "look for TC-{FEATURE}-{NNN} in docs/specs/" This is what makes a skill work across any project without modification.

8. Dimensions > Checklists — Structure review/analysis as named thinking dimensions, each with a Think: prompt that forces first-principles reasoning: (1) state dimension's role, (2) derive what could go wrong if weak, (3) apply to artifact with evidence. Produces targeted, evidence-backed findings — not generic "add more detail" suggestions. Serial attention: When applying a dimension-based framework, NEVER scan all dimensions simultaneously. One focused pass per dimension. AI misses violations when attention is split across concurrent concerns. Pattern: identify applicable dimensions → sequential focused passes → aggregate. Threshold invariant: 3+ similar patterns in any dimension pass = MANDATORY extraction. 2+ violations of same kind = structural/architectural finding, not individual instance.

9. Recursive Quality Loop — For targets whose contract includes review/fix convergence, use Fix → Re-review → Fix → Re-review; each round uses a NEW fresh sub-agent and stops at 3 rounds with escalation. For other targets, do not invent a loop: preserve their declared terminal state and record this principle as N/A.

10. Anti-Rationalization Anchors — Explicitly name and embed the evasion patterns AI uses to skip steps in the skill's closing reminders:

    Evasion	Rebuttal
    "Too simple for this"	Wrong assumptions waste more time. Apply anyway.
    "Already searched"	Show file:line evidence. No proof = no search.
    "Just do it"	Still need task tracking. Skip depth, never skip tracking.

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.
10. Affirmative Directives — Models comply with affirmative directives more reliably than prohibitions; a bare "don't X" leaves the correct action unspecified, so the model substitutes an arbitrary alternative. Action: State the action to take, not only the action to avoid. Keep NEVER/forbidden guardrails for hard invariants — but pair each with the right path ("Do X" not just "Don't do Y").
11. Rationale-Carrying Instructions — A rule shipped with its reason generalizes to edge cases the rule never enumerated and survives compression; a bare imperative gets misapplied or silently dropped. Action: Append a terse — why: … clause to every non-obvious rule. The reason names the failure prevented or outcome wanted — never restates the rule.

Prompt Enhancement Transforms (Base) — Transforms 1-3 are identical across all /prompt-enhance ops (--op=compress|expand|enhance). Transform 4 is per-op (conciseness pass for compress/enhance; structural clarity pass for expand) and stays local to each op branch.

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 (project-specific). Add: (Hooks may point to this file, but every host must open it directly using docs-index routing when the context is needed)

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 AND the ultimate outcome it must cause]

**Summary:** [2-4 bullets/sentences — the key important things + the steps AI must notice; the read-this-if-nothing-else digest, distinct altitude from the enumerated Workflow/Key Rules below]

**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 Goal:** [same goal as Quick Summary]
**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.

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

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing OR flagging one as a defect. Before changing or reporting a constant, limit, flag, cutoff, wording, or pattern, read nearby context and history, the CALLER's ordering, and 2+ sibling call sites of the same convention. A doc stating WHAT without WHY is missing rationale, not proof of a missing guard. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Assert the outcome your system owns, not the intermediate state your infrastructure owns. When verifying async work, assert the final business state — never the delivery/retry bookkeeping held in shared infrastructure that any co-running process can write. Such a check passes when run alone and flakes the moment anything else shares that infrastructure. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

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.