Skill Distillation

Step 5 of the universal request loop (see AGENTS.md and meta-skills). Every user request ends with a distillation pass before delivery. Skip it and the library decays; the next session rediscovers the same lessons.

ORIENT -> PLAN -> EXECUTE -> VALIDATE -> DISTILL -> DELIVER

When to run

Run automatically when any of these happen:

1. A task required > 3 iterations.
2. The user corrected you ("that's wrong", "you missed X", "it should be Y").
3. You discovered a workaround not in any existing skill.
4. A sub-agent hit a failure not covered by an existing skill.
5. You solved something that would break again next session without docs.

Also run during heartbeats (every 2-3 days): review recent memory/ files for undistilled lessons.

Loop

INTERACTION -> TRIGGER CHECK -> EXTRACT -> CLASSIFY -> WRITE -> VERIFY

1. Trigger check

Ask:

• Did I learn something not in any skill file?
• Did the user correct my approach?
• Did I waste time on something a future agent would also waste time on?
• Was there a procedure that worked but isn't documented?

Any yes -> extract.

2. Extract

3. Classify

New knowledge
├── Fits existing skill        -> UPDATE that skill
│   ├── New rule               -> Key Rules
│   ├── New failure mode       -> Failure Modes
│   ├── New procedure          -> Key Workflows
│   └── Correction             -> fix in place
├── Cross-cutting pattern      -> UPDATE meta-skills
├── Entirely new capability    -> CREATE new skill (MSF Phase 1-3)
└── One-off fact               -> MEMORY.md, not skills

Rule: if the knowledge helps solve a different future problem, it belongs in a skill. If it's specific to this one situation, it belongs in memory.

4. Write

• Skill updates: edit the specific section; bump the Iteration Log.
• New skills: skills/<name>/SKILL.md from the meta-skilling template.
• Procedure changes: update the orchestrator or relevant workflow skill.
• Always: date and context ("Learned from SO-101 session 2026-04-08").

5. Verify

• Re-read the file. Does it stand on its own?
• Could a fresh agent follow it?
• Is the lesson a procedure, not a fact?

What good distillation looks like

Bad: "The table had dual RigidBodyAPI which made it explode." This is a fact about one table; useless next session.

Good: "Before using any USD asset in physics, run the Asset Stability Check: load in isolation -> simulate 2 s -> if unstable, scan child meshes for rogue RigidBodyAPI." Reusable procedure.

Bad: "SO-101 needs hybrid IK + joint-space." Specific to one robot.

Good: "Arms with < 6 DOF cannot do lateral transport with pure differential IK. Use hybrid: IK for precision positioning, joint-space for large moves." Generalizes to any small-DOF arm.

The Generalization Rule

Always ask: "What's the general principle behind this specific fix?"

Integration with Orchestrator

The orchestrator's workflow should include distillation as a final step:

Phase 1: Verify Foundations
Phase 2: Incremental Integration
Phase 3: Polish & Deliver
Phase 4: Distill ← NEW (automatic, after delivery)
  - What went wrong during this task?
  - What workarounds were discovered?
  - What feedback did the user give?
  - Which skills need updating?
  - Should a new skill be created?

Anti-patterns

• Waiting to be asked. If you learned it, write it now.
• Capturing facts instead of procedures. "X broke" is not a skill; "check X before running" is.
• Over-generalizing from a single data point unless the user confirmed the pattern.
• Under-generalizing: writing a per-asset fix when the pattern applies to all USD assets.
• Skipping verification. An unreadable update is worse than none.
• Duplicating across skills. One canonical location per procedure; cross-reference from others.
• Embedding code in skills. See below.

Code in Skills — Reference, Don't Embed

Skills should reference canonical sources rather than embed code directly. Embedded code becomes stale when the upstream library or API changes — silently giving future agents wrong patterns to follow.

Rule

For any code pattern in a skill:

• If it comes from Isaac Sim examples or docs: link to the doc page and/or local example file path. Describe the pattern in prose; let the agent read the actual file.
• If it's a custom utility not in any upstream source: put it in a versioned script file in the workspace (e.g., isaac-sim/utils/grasp_utils.py), reference it by path, and note the version it was written for.
• If it's a tiny one-liner or config snippet (e.g., a settings dict): embedding is acceptable, but add a comment with the Isaac Sim version it was validated against.

Why

• Isaac Sim APIs change between versions. A skill with embedded code can silently guide agents down wrong paths.
• Local example files ($ISAAC_SIM_DIR/source/standalone_examples/...) always reflect the installed/built version — they are the most accurate reference available.
• Docs at docs.isaacsim.omniverse.nvidia.com are version-tagged — link to latest unless pinning to a specific release is intentional.

What to Write Instead of Code

• The pattern name (e.g., "damped least-squares IK")
• Where to find the canonical implementation (doc URL + local file path)
• Key parameters and how to tune them (table form is fine)
• What can go wrong and how to detect it
• The conceptual flow in prose: what the code does step by step, without being the code itself

Library-Health Check (Run During Distillation)

Distillation isn't only "add new knowledge." Each pass should also detect when an existing skill has grown stale, bloated, or duplicate. Catch these at write time or they accrete until a full consolidation pass is needed.

Bloat detection

Before saving a skill update, eyeball the target file:

Sidecar Offload (when SKILL.md is too long)

Rule: one level deep only. scripts/foo.py and examples.md are valid; scripts/utils/foo.py is not (Anthropic spec: one-level refs).

Duplication check

Before adding to a skill, search the library for the same concept:

grep -rE "<your-new-concept>" --include=SKILL.md skills/

If another skill already documents it:

• Add to the existing skill, not your current one. Single source of truth.
• Cross-link from your current skill: "See <other-skill> for X."
• If the existing skill's coverage is wrong/outdated → fix it there, don't fork.

Staleness check

Each distillation, ask of the skill you're touching:

• Is the framework version reference still current? (Isaac Sim 5.x → 6.0+, PhysX → Newton)
• Are the hardcoded paths really paths, or should they be $ENV_VARS?
• Are there time-sensitive phrases ("before August 2025", "as of 2026-04-04")? Replace with version-anchored or "sample-run" framing.
• Does any "lessons learned" section reference a project that no longer exists?

Cadence

Draft Skills — High Priority Iteration

When a skill is created during a task (i.e., no existing skill covered the feature), it is by definition unproven. Treat it differently:

Frontmatter Flag

Add to the new skill's YAML frontmatter:

status: draft
priority: high
created_from: <task description>

Behavior While Draft Skill Is Active

• Tell the user immediately: "I'm working with an untested skill for [feature] — I'll check in after each step rather than at the end."
• Shorten iteration cycles: share intermediate results after each meaningful step, not just at delivery
• Ask early, not late: if anything about the goal is ambiguous, surface it before implementation, not after
• Validate direction before depth: confirm the approach is right before building it out fully
• Checkpoint messages should include: what was just completed, what comes next, and any open questions

Promotion to Stable

After a draft skill has been used successfully on at least one task with user confirmation:

1. Remove status: draft and priority: high from frontmatter
2. Add validated: <date> and a note on what the validation task was
3. Run a full distillation pass to incorporate any corrections from the task
4. Update the orchestrator's feature→skill map if one exists

Anti-patterns for draft skills

• Running a draft skill to completion without checking in. Direction may be wrong.
• Skipping the user notice. They need to know confidence is lower.
• Promoting to stable after one pass with no user feedback.