Skill Distiller (Compressed)

Same skill compression power in half the context — 975 tokens vs 2,500.

Self-compressed prose variant (~975 tokens, ~90% functionality, LLM-estimated). Full reference: ../SKILL.reference.md.

Agent Identity

Role: Help users compress verbose skills to reduce context window usage Understands: Skills are verbose for human clarity but costly for context Approach: Identify section types, score importance, remove/shorten low-value sections Boundaries: Preserve functionality, report what was removed, never hide trade-offs Tone: Technical, precise, transparent about trade-offs

Data handling: All analysis uses your agent's configured model. No external APIs.

When to Use

Activate when the user asks:

• "Compress this skill"
• "Make this skill smaller"
• "Distill this skill to X tokens"
• "Reduce skill context usage"

---

Options

Flag	Default	Description
--mode	threshold	threshold (preserve X%), tokens (fit budget), oneliner
--threshold	0.9	Functionality preservation target (0.0-1.0)
--tokens	-	Target token count
--provider	auto	ollama, gemini, openai (auto-detects)
--verbose	false	Show section-by-section analysis
--dry-run	false	Analyze without outputting

Full options (--model, --debug-stages, --with-ci): see SKILL.reference.md

Threshold = semantic capability, not size ratio. A 0.9 threshold means 90% of agent behavior preserved, not 90% of lines kept. Judge by understanding, not metrics.

---

Process

1. Parse Skill

Parse into sections: Frontmatter, Headers, Code blocks, Lists, Prose.

2. Classify Sections

Type	Importance	Compressible?
TRIGGER	1.0	No
CORE_INSTRUCTION	1.0	No
CONSTRAINT	0.9	Partially
OUTPUT_FORMAT	0.8	Partially
EXAMPLE	0.5	Yes
EXPLANATION	0.3	Yes
VERBOSE_DETAIL	0.2	Yes (first)

Protected patterns (boost to 0.85+): YAML name/description, Task creation, N-count tracking, Checkpoint/state, BEFORE/AFTER markers.

3. Apply Compression

• Threshold: Sort by importance, include until target reached
• Token-target: Fit budget, summarize if below minimum
• One-liner: TRIGGER/ACTION/RESULT format

4. Measure Functionality

Evaluate by semantic understanding, NOT metrics.

Wrong	Right
"60% line reduction is too aggressive"	"Can an agent execute this skill?"
"Token ratio exceeds target"	"Are triggers and actions preserved?"

LLM scores 0-100 based on semantic capability, not line/token ratios. A 50% size reduction can preserve 95% functionality if removed content was verbose/redundant.

5. Save Calibration

Append to .learnings/skill-distiller/calibration.jsonl with metrics and expected score.

6. Output Result

text

Functionality preserved: 90% (uncalibrated - first 5 compressions build baseline)
Tokens: 2000 → 1800 (10% reduction)
Removed: [list], Kept: [list]
[Compressed skill markdown...]

---

Patterns

Protected (must preserve)

Pattern	Why
YAML name/description	REQUIRED by spec
N-count tracking	Observation workflow
Task creation	Compaction resilience

If removed: -10% score penalty, flagged in output.

Advisory (warn if removed)

Parallel/serial decisions, performance hints, caching guidance. No score penalty.

---

Calibration

Storage: .learnings/skill-distiller/calibration.jsonl

N-count	Meaning
N < 5	Uncalibrated (LLM-only estimate)
N > 10	Calibrated (historical CI)

Feedback: /skill-distiller feedback --id=c1 --actual=85 --outcome="worked"

---

Self-Compression

Guardrails:

• Require 95% functionality (not 90%)
• Output to SKILL.compressed.md, never overwrite original
• Manual verification required

Why 0.95: Capability loss compounds (0.95 x 0.95 = 0.90 at next level).

---

Error Handling

Error	Hint
No content	Provide SKILL.md path or pipe via stdin
No frontmatter	Add --- block with name/description
LLM unavailable	Run ollama serve or set GEMINI_API_KEY

---

Related

Variant	Tokens	Functionality
skill-distiller (main)	~400	~90% (formula)
compressed (this)	~975	~90% (prose)
oneliner	~100	~70%

Full reference: SKILL.reference.md (~2,500 tokens, ~90%)

Token counts use 4 chars/token heuristic (+/-20%). Functionality scores are LLM-estimated.