Semantic Compress

Semantic Compress

Make a document written for an LLM reader smaller while preserving what it does. The essence of an LLM-directed document is behavioural, not textual - the behaviour it induces in the reading model across the tasks it handles. Compression splits content into two kinds and treats each correctly:

• Core knowledge - anything in the model's training (named concepts, standard definitions, common-domain background). Replace the explanation with a pointer: the bare concept name or a short cue that activates the right knowledge. A pointer costs a few tokens and reliably switches on the model's existing understanding; a full explanation is wasted tokens, and deleting the concept entirely gambles that it is already active in the model's reasoning - often it is not, and disambiguation suffers.
• Project / bespoke knowledge - anything the model cannot know from training: specific facts, local decisions, constraints, and non-standard twists on a known concept. Keep these explicit and verbatim. The model has no other source for them.

Compression is therefore point at core, spell out bespoke. Pointing is not deletion and it is not full explanation; it is the minimum that both activates the right core knowledge and preserves every bespoke detail.

Mode Selection

This skill operates in one of two modes, selected deterministically:

Default to distill when:

• The input is a skill, system prompt, or instruction document
• Behaviour preservation matters
• The user asks for "smaller but same behaviour"

Local is permitted only when all hold:

• The input is a short span (< 500 chars)
• There is an obvious single core->pointer swap
• No downstream behaviour depends on it

When in doubt, distill: a local edit cannot, by construction, preserve a global behavioural property, so anything with a behavioural surface goes through the A/B gate.

Hard Rule: Behavioural Evidence Required

A compression is never accepted on inspection - only on behavioural evidence from an A/B run.

This skill must refuse to output a compressed document that has not passed an A/B equivalence run against the original. Introspection about behaviour ("this should work the same") is structurally unreliable - the model guesses optimistically. Execution over the transfer set is the only arbiter.

This rule binds distill mode (the rule's home: whole-document compression always carries behavioural risk). Local mode is the deliberate, narrow exception - a span small enough (< 500 chars, single obvious swap, no downstream behaviour) that the behavioural risk is negligible by construction. The moment a local edit touches a behavioural surface, it is no longer local: it is a distill, and the gate applies.

Local Mode

The v1 span-level operation: find a span that explains a concept the model already holds, replace it with a pointer, keep every bespoke detail verbatim. These steps are also the inner micro-operation distill mode regenerates with (references/distill-loop.md, Part 2, step 1).

Step 0: Audience gate

This skill applies only when the LLM is the audience for the explanation. If the text explains a concept to a human (onboarding notes, a message to teammates, docs for new hires), the explanation is not redundant for its real audience - leave it. Compress only the spans the model itself is meant to read and act on.

Nested / wrapped instructions. If the input wraps an instruction the model is meant to process (e.g. "preprocess this instruction before executing it: '...'", or a quoted prompt to compress), the wrapper is a meta-directive to you - act on it, do not emit it. Compress the quoted payload by the rules below and return only that. The payload's audience is the model, so the audience gate is satisfied for the payload regardless of the wrapper.

Step 1: Split each span into core vs bespoke

Read the input. For each span, classify:

• Core - a named concept or standard definition the model already holds (Chesterton's Fence, the Agile Manifesto, idempotency, SOLID, optimistic locking in its standard form).
• Bespoke - information with no training source: a specific number, a local choice, a constraint, an exception, or a non-standard redefinition of an otherwise-known term.

A single sentence often contains both. Split at that seam.

Step 2: Core -> pointer

Replace a core-knowledge explanation with the smallest cue that activates it - usually the concept's name, optionally one disambiguating word:

• "the principle that you shouldn't remove something until you understand why it's there" -> "Chesterton's Fence"
• "observe-orient-decide-act faster than the competitor" -> "OODA"

Always emit the pointer, with one exception: if a surviving bespoke span already names the concept literally, the pointer is redundant - omit it then, and only then. Never drop the pointer on the grounds that the frame is merely "implied" - that judgement is self-certifying and is exactly the escape that collapses this skill back into deleting what the model knows. When in doubt, keep the pointer; it costs almost nothing.

Step 3: Bespoke -> explicit, verbatim

Keep every bespoke span unchanged: specific facts, decisions, constraints, and especially non-standard twists. If a known term is redefined locally ("optimistic locking - but we hash the whole record, not a version counter"), the twist is bespoke: keep it in full. Dropping it because the term looks familiar is the most dangerous failure this skill can make.

Step 4: Self-sufficiency check

Could the model act correctly on the output alone? If a pointer is too thin to disambiguate, widen it by one cue word - never back to a full explanation. If a bespoke detail was lost, restore it verbatim. The all-core degenerate case (input that is purely known concepts with nothing bespoke and no instruction) compresses to its pointers; if there is also no instruction to act on, the output is just those pointers - not empty text and not a meta-note.

Step 5: Output

Clean text: pointers for core, explicit bespoke. No meta-commentary, no "[compressed]" markers, no "see X" links - the pointer is the reference, inline and natural.

Examples

Core -> pointer, bespoke kept

Input: "When evaluating whether to remove a legacy feature, first understand why it was created - you shouldn't destroy something without understanding its purpose, as it may serve a need you're unaware of. Our legacy auth module was built for EU data-residency compliance that still applies to German users." Output: "Chesterton's Fence on removing the legacy auth module: built for EU data-residency compliance, still applies to German users."

All-core -> pointers (degenerate case)

Input: "Remember to follow the Agile Manifesto and the SOLID principles in this project." Output: "Follow Agile and SOLID." Nothing bespoke and no instruction beyond the frame, so it compresses to the bare pointers - not to empty text.

Non-standard twist is bespoke

Input: "We use optimistic locking - by which we specifically mean the client sends a hash of the entire record snapshot, and the server rejects on any field mismatch, not just a version-counter bump." Output: "Optimistic locking, but our variant: client sends a hash of the whole record snapshot; server rejects on any field mismatch, not a version-counter bump."

Nothing core

Input: "The staging cluster runs 3 nodes in eu-west-2 with a 4GB heap cap per node; the nightly backup starts at 02:00 UTC to the cold-storage bucket." Output: (unchanged - all bespoke)

Human audience -> do not compress

Input: "Reminder for new hires: 'idempotent' means you can safely retry an operation. Skim the API onboarding doc before Friday." Output: (unchanged - the definition is for new hires, not the model)

Anti-patterns

• Deleting the concept instead of pointing at it. Pure elision gambles the concept is already active; a pointer guarantees activation at near-zero cost. "The frame is implied" is not a licence to delete - emit the pointer unless a bespoke span literally names the concept.
• Dropping the bespoke part. "Chesterton's Fence on the legacy module" without the EU-compliance reason is useless - the reason is the whole payload.
• Dropping a non-standard twist as if it were the standard concept. If a known term is locally redefined, the redefinition is bespoke. Keep it.
• Re-explaining core knowledge. A pointer suffices; a paragraph is waste.
• Compressing human-directed text. The audience gate comes first.
• Meta-commentary. No "[pointer]", no "// core concept". The output reads as natural, shorter text.

Distill Mode

The headline operation: produce the smallest document that behaves the same as the original, with acceptance gated on A/B behavioural evidence (the Hard Rule above), never on inspection. Distill mode composes two reference docs:

• references/transfer-set-design.md - derives and confirms the transfer set (the operational definition of the document's essence).
• references/distill-loop.md - the engine: teacher baseline capture, candidate regeneration, and the iterate-to-minimal controller.

The loop composes ab-equivalence (skills/ab-equivalence/references/ab-equivalence.md) for the behavioural test. This skill owns compression (transfer set, candidate regeneration, the loop, the report); ab-equivalence owns the behavioural comparison (running the runner over the transfer set, judging equivalence per case).

The distill loop (seven steps)

1. Define the transfer set. Derive behavioural test cases from the document across the four taxonomy types (happy / edge / adversarial / composition). The user confirms or augments them before any baseline run - the transfer set is the operational definition of essence, so the user signs off. The skill flags thin coverage. See references/transfer-set-design.md.
2. Capture the teacher. Run the original document over each case and record the behaviour it induces (the disciplines enforced, the outputs produced). This baseline is the equivalence target, captured once and cached across rounds.
3. Compress = regenerate, not edit. Produce a smaller candidate against the behavioural spec: the local core->pointer move (Local Mode above), de-duplication, and dropping prose the baseline proves was never acted on. Regeneration - rewriting to function - is what lets it get genuinely smaller; trimming alone cannot.
4. A/B validate. Run the candidate over the same transfer set; the equivalence judge compares candidate-vs-teacher behaviour per case under strict no-regression. This is the only acceptance gate.
5. On any regression, the divergence names what was load-bearing. The judge's behaviour delta names the lost behaviour; add back the minimum that restores it, then re-validate.
6. Converge on the smallest candidate that still passes. Stop at diminishing returns (a round that can only shrink further by regressing) or a budget ceiling (default 5 rounds).
7. Output the minimal equivalent document plus an A/B distillation report (references/distillation-report-template.md).

Strict no-regression gate. The candidate passes iff, on every transfer-set case, it induces every behaviour the original induced. Any dropped behaviour is a fail (essence lost) and triggers add-back. Incidental improvements are acceptable but never required and never the goal - the goal is faithful, smaller reproduction. The gate is behavioural equivalence, not textual similarity.

Distill mode example

Input: "Distill this CLAUDE.md to the smallest version that behaves the same" Output: The minimal equivalent document plus an A/B distillation report (<document-name>-distillation-report.md) recording the size delta, the transfer set and its coverage, per-case equivalence verdicts, what was dropped vs load-bearing, and the distribution-shift caveat.

Distribution-Shift Guard

A distillation is only valid over the transfer set it was tested against - the same overfitting risk model distillation faces outside its transfer distribution. The transfer set is the operational definition of essence for this run; behaviour outside its coverage is unproven.

Mitigations

1. Push for diverse coverage. The transfer set should span all four case types (happy / edge / adversarial / composition). A narrow transfer set (all happy-path) permits aggressive compression that breaks on edge or adversarial inputs - the breadth of the set bounds the safety of the compression.
2. Stay conservative. Keep anything not proven behaviourally inert by the A/B. If a section was never exercised by any case, it is not proven inert - it might be load-bearing for untested inputs. The conservative default is to keep it.
3. Name the coverage. The report explicitly states which sections were exercised and which were not. Silence about untested behaviour is forbidden.

What the guard prevents

• Compressing away a section that only matters for adversarial inputs when the transfer set was all happy-path.
• Claiming "same behaviour" when behaviour outside the transfer set was never checked.
• The user believing the compression is universally valid when it is only valid for the tested cases.

Coverage threshold

If fewer than 70% of the identified behaviour-inducing sections are covered by at least one case, warn the user before proceeding and offer to auto-generate additional cases for the uncovered sections. A distillation gated on a thin set makes a weak equivalence claim - say so loudly rather than proceed silently.

Conservative-default logic

For each section in the original:
  if exercised_by_at_least_one_case(section):
    if A/B says inert:        can drop
    if A/B says load-bearing: must keep (pointed or de-duped, never dropped)
  else:
    section is NOT PROVEN INERT
    keep by default (conservative)
    report as "kept (uncovered, conservative default)"

Report integration

The A/B distillation report (references/distillation-report-template.md) makes the coverage legible:

• Transfer-set coverage percentage in the header.
• Thin-coverage warnings listed in the Transfer Set section.
• Uncovered-but-kept sections labelled "conservative default" under What Proved Load-Bearing.
• The distribution-shift caveat stated verbatim: behaviour outside the transfer set is not guaranteed equivalent.

Transform #2: Directive-clarity

Compression (Local + Distill above) is Transform #1 of this skill's optimizer family: it makes a document smaller while preserving behaviour. Directive-clarity is Transform #2: it makes a document lighter to act on while preserving behaviour, by rewriting instructions that force the reading model to unpack an action before it can act into concrete directives that name the action. Both transforms share one validator - ab-equivalence's harness (skills/ab-equivalence/references/ab-equivalence.md) - and differ only in the gate they apply to its result. The frame that generates this transform, and the caveat that keeps it honest (every claimed gain is a hypothesis the harness must measure, never an assertion), is references/cognitive-ergonomics.md.

When it fires

• Explicitly invoked: --directive-clarity <document> targets a document directly.
• Auto-suggested: when distill mode detects high negation density or vague-pointer patterns in the document it is compressing, it surfaces directive-clarity as the recommended next stage. This is the natural entry into the staged compose order below.
• Never auto-applied. The transform proposes rewrites; the A/B run validates them; the user approves the result. No rewrite reaches the output document without passing the gate and the user signing off - directive-clarity changes the words the model acts on, so the human confirms the change.

The loop

1. Detect patterns. Scan the document for the four latent-action shapes - bare negation, fact-not-action, vague pointer, ordering/policy rule - per references/directive-clarity-patterns.md. Detection over-includes on purpose: it optimises for recall, flagging every candidate shape.
2. Classify each candidate convert-for-free vs battle-scar. Route every detected pattern through references/battle-scar-classifier.md. A battle-scar - a prohibition earned from a specific past failure, where the wording is the load-bearing content - is flagged for preservation and never rewritten. The classifier is the precision half: it holds back scars to a sub-10% false-positive target, defaulting to preserve on uncertainty. Only the convert-for-free set proceeds.
3. Rewrite. Turn each released candidate into a concrete directive per references/directive-clarity-rewrites.md, under the two acceptance checks (names-the-concrete-action, semantic-equivalence). Keep every qualifier that scopes a prohibition; keep the fact behind a fact-not-action rewrite; never fabricate a referent for a vague pointer - flag it for human confirmation and leave the original in place.
4. A/B validate. Run the candidate against the teacher over the transfer set via ab-equivalence's harness (skills/ab-equivalence/references/ab-equivalence.md). The harness emits, per case, an equivalence verdict (equivalent / candidate-regressed / candidate-diverged) and an efficiency signal (original_directness, candidate_directness, interpretation_notes).
5. Present for approval. Surface the per-rewrite diffs, the A/B verdicts, the preserved battle-scars, and any vague pointers held for confirmation. The user approves before the rewritten document is emitted.

The gate

Directive-clarity's acceptance is stricter than compression's. Compression gates on strict no-regression alone (sameness). Directive-clarity gates on no-regression and a measured directness gain:

• No-regression: zero cases return candidate-regressed (summary.pass == true). A rewrite that reads cleaner but permits a behaviour the original forbade is a regression and fails here.
• Directness gain: candidate_directness > original_directness on the rewritten cases, with no candidate-regressed. This is the efficiency signal the A/B harness already records on every run (skills/ab-equivalence/references/ab-equivalence.md) - directive-clarity reads that signal and gates on it; it does not redesign or re-instrument the harness. A rewrite that loses nothing but also measures no directness improvement has not earned its place: keep the original.

On a regression, the divergence names the rewrite that lost behaviour. Revert that specific rewrite to its pre-rewrite form, keep the passing rewrites, and re-validate - the same granular add-back the distill loop uses.

How directive-clarity composes with compress

When both transforms run on one document, they run as Option C: staged with a checkpoint - compress first, freeze its gains, then directive-clarity on top, with any regressing rewrite reverting to the frozen state without losing the compression.

Why staged, not the alternatives:

• Not combined/interleaved (Option B). The two transforms apply different gates to the A/B result: compress accepts on no-regression alone, directive-clarity demands no-regression and a directness gain. A single interleaved A/B cycle cannot apply two gates to one result, and when a combined pass regresses there is no way to attribute the loss to a compression pointer-swap or a directive rewrite. Attribution loss makes add-back guess. Ruled out.
• Not loosely independent (Option A). Running two unlinked passes leaves directive-clarity's teacher baseline underspecified. If its A/B teacher were the original document, the measured directness gain would conflate compression's effect with directive-clarity's, and nothing would structurally stop a directive-clarity regression - measured against the original - from pulling behaviour back past the compression gains. Clean attribution and a protected floor both require an explicit checkpoint.
• Staged with checkpoint (Option C, chosen). The compressed document becomes an explicit checkpoint that is both directive-clarity's new teacher baseline and its revert floor. Directness is then measured against compressed behaviour (clean attribution: the gain is directive-clarity's alone), and no rewrite can regress below the compressed state because the teacher is the compressed state. This is exactly the behaviour the transform must guarantee: compress gains kept, only the regressing rewrite reverts.

Run order and checkpoint mechanism (precise):

1. Compress to convergence. Run distill mode to its minimal equivalent document D_compressed, accepted by compression's strict no-regression gate against the original teacher.
2. Checkpoint. Freeze D_compressed as the directive-clarity stage's input. Capture a fresh teacher baseline by running the A/B harness's runner over D_compressed once per transfer-set case - these become the cached teacher transcripts for the next stage. (D_compressed is behaviourally equivalent to the original by construction, but its directness differs, so the gain must be measured against D_compressed's directness, not the original's.) The original teacher transcripts from step 1 are not reused here.
3. Directive-clarity over D_compressed. Detect -> classify -> rewrite (loop steps 1-3) to produce candidate D_direct.
4. A/B validate D_direct against the D_compressed checkpoint teacher. Apply the directive-clarity gate (no-regression AND directness gain).
5. Granular revert on regression. A rewrite that regresses against the checkpoint reverts to its D_compressed form; passing rewrites stay. Because the teacher is D_compressed, the checkpoint is the floor - no rewrite can drop behaviour below the compressed state. Re-validate and converge.

Compress first (not directive-clarity first) because compression can point-away or de-duplicate prose that directive-clarity would otherwise spend a rewrite and an A/B cycle on; compressing first shrinks the surface directive-clarity must scan and avoids rewriting spans that compression removes.