# Specialization > Use immediately when no installed skill covers a reusable domain, or when feedback, a mistake, or a better process reveals that an existing skill is incomplete or wrong. Record the first observation, create or amend a private candidate, and dogfood it on the triggering case now. Follow provenance to improve the canonical skill instead of forking it. Recurrence strengthens evidence but never delays a proven correction. - Skill: `ghostlygawd/specialization` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add ghostlygawd/specialization` - Raw SKILL.md: https://api.skillmd.com/api/skills/ghostlygawd/specialization/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: GhostlyGawd (https://skillmd.com/u/ghostlygawd) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ghostlygawd/specialization --- # Specialization Turn the first useful observation into a tested candidate. Do not wait for a second session to learn; only permanent canonical changes wait for proof and review. Read `references/evolution-loop.md` when classifying evidence or deciding whether a dogfood replay is strong enough. ## 1. Recall and classify Before minting a new domain, recall related evidence: ```sh python3 skills/specialization/needs.py match --domain "kafka consumer groups" ``` Classify the observation exactly once: - `gap`: no installed skill covers a reusable capability. - `correction`: existing guidance produced or encouraged a wrong result. - `improvement`: existing guidance worked, but feedback revealed a better process. Project-only facts route to that project's instructions. A code defect routes to `auto-healer`. A reusable procedure routes here even on its first occurrence. ## 2. Record and create immediately For a new capability: ```sh python3 skills/specialization/needs.py add \ --learning-kind gap --domain "kafka consumer groups" \ --category infra --tags area:streaming,class:rebalance \ --shape "had to derive why a consumer rejoin caused a full rebalance" ``` For an existing skill, follow its provenance to the canonical source and seed an amendment candidate from that source. Corrections and improvements require all three provenance arguments and a readable source skill; `add` rejects a generic sibling draft when any is missing or the target does not match source frontmatter. The source must resolve to a literal `SKILL.md`; a `gap` cannot carry owner inputs: ```sh python3 skills/specialization/needs.py add \ --learning-kind correction --domain "codex hook output" \ --target-skill hook-authoring \ --target-provenance "GhostlyGawd/recursive-harness@" \ --source-skill skills/hook-authoring/SKILL.md \ --shape "the documented output shape failed against the current host schema" ``` `add` appends compact evidence to the provider-neutral private ledger and creates or updates `candidates//` immediately. Never store transcripts or full prompts. Multiple observations in one provider session add shapes but count as one recurrence. If a generic gap later resolves to an existing owner, `add` archives the generic draft and rebases from that owner. It rejects a domain already bound to a different target skill, including a targetless gap observation; continue through the known owner or resolve the collision instead of clearing provenance. ## 3. Author and dogfood now Open the candidate path printed by `add`. Replace its draft marker with the smallest evidence-shaped procedure. Use `harness-authoring` for duplication, provenance, source-of-truth, and budget checks. Replay the triggering case with the candidate explicitly in context. Compare the actual before and after behavior, then record the result: ```sh python3 skills/specialization/needs.py candidate dogfood \ --case "consumer rebalance diagnosis" \ --before "reasoned from scratch and missed the coordinator transition" \ --after "candidate identified the transition and selected the verified fix" \ --outcome worked --generalizes yes \ --verification "replay fixture rebalance-v1 passed" ``` Log failed and partial replays too. Revise the same candidate; do not mint a sibling. A new `gap` needs two materially distinct worked cases for the current revision, including one marked `generalizes=yes`. A `correction` or `improvement` needs one worked replay with concrete verification. After authoring and successful dogfood, remove the draft marker and validate: ```sh python3 skills/specialization/needs.py candidate validate ``` Validation makes the candidate promotion-ready after the first observation when the proof is strong enough. Recurrence >= 3 only surfaces an unvalidated candidate for urgent review; count alone is never proof. ## 4. Promote through the canonical owner `promote-check` shows proof-ready and recurring-unvalidated candidates: ```sh python3 skills/specialization/needs.py promote-check ``` Do not install, push, or open a cross-repository change without approval. After approval: 1. Strengthen the provenance owner when one exists; do not create an overlapping skill. 2. Add the triggering case as a regression fixture. 3. Regenerate provider packages from the canonical source and run shared fixtures. 4. Land through the repository's guarded branch and review workflow. 5. Close the loop with `needs.py promoted --skill `. ## Boundaries - A missing binary is acquired and verified; record a specialization only when the reusable acquisition procedure itself was the knowledge gap. - `routing-learnings` chooses the artifact; `harness-authoring` governs its form; this skill owns first-observation capture, candidate evolution, and dogfood. - `auto-healer` records code defects and attempted fixes. When the root is wrong or missing reusable guidance, route that learning back here. - Never edit an installed provider cache as canonical source. Follow provenance, update Recursive Harness, then regenerate and reinstall the adapter. provenance: 2026-06-27, session 9f6014a0, original expert-accretion loop; revised 2026-07-18 after the owner required immediate first-observation creation plus dogfooded improvements to existing skills instead of recurrence-gated learning.