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Specialization

Skill GhostlyGawd/recursive-harness/plugins/recursive-specialization/skills/specialization

Portable, evidence-driven agent development harness for Codex, Claude Code, and generic Agent Skills. Active beta v0.1.2.

Install
npx -y skills add GhostlyGawd/recursive-harness --skill specialization

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One thing to look at

  • 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use immediately when no installed skill covers a reusable domain, or when feedback, a mistake, or a better process shows that an existing skill is incomplete or wrong. Record the first observation, create or amend a private candidate, and dogfood it now. Follow provenance to the canonical skill instead of forking it. Recurrence strengthens evidence but never delays a proven correction.

SKILL.md

4.0 KB, as published. Nobody here has run it

Specialization for Codex

Turn the first useful observation into a tested local candidate. The lifecycle hook supplies the absolute CLI path plus Codex session and turn IDs. Use that path; do not assume the current repository contains Recursive Harness.

Read references/evolution-loop.md before validating a candidate.

Classify once

  • gap: no available skill covers a reusable capability.
  • correction: an existing skill produced or encouraged a wrong result.
  • improvement: an existing skill worked, but feedback exposed a better process.

Project-only facts belong in project instructions. Code defects belong in the project's defect loop. Wrong or missing reusable guidance belongs here.

Record the first observation

Recall related evidence first:

<cli-command-from-hook> match --domain "domain"

Then record. Pass the --provider codex, --session, and --turn values supplied by the hook:

<cli-command-from-hook> add --learning-kind gap \
  --domain "domain" --shape "specific missing reasoning" \
  --provider codex --session "<session>" --turn "<turn>"

For an existing skill, follow the provenance in its SKILL.md and add --target-skill, --target-provenance, and --source-skill pointing to the canonical source. All three are mandatory for a correction or improvement so a missing provenance owner cannot silently become a sibling skill; the target must also match the source frontmatter name, and the source must resolve to a literal SKILL.md. A gap cannot carry owner inputs. Never edit the installed plugin cache as the owner.

add writes compact evidence to a provider-neutral private ledger and creates or updates the candidate immediately. It never needs prior-chat access and must not store transcripts or full prompts. A generic gap later matched to an existing owner is archived and rebased from that source. A domain already bound to another target, or later reported as a targetless gap, is rejected rather than clearing or silently combining owners. Continue through the known provenance owner.

Author and dogfood now

Open the printed candidate. Replace its draft marker with the smallest procedure that addresses the evidence. Replay the triggering case with the candidate loaded explicitly, compare before and after, and record the honest result:

<cli-command-from-hook> candidate dogfood <nid> \
  --case "triggering case" --before "observed baseline" \
  --after "candidate result" --outcome worked \
  --generalizes yes --verification "named check or fixture passed" \
  --provider codex --session "<session>"

Record partial and failed attempts too, then revise the same candidate. A new gap needs two materially distinct worked cases for the current revision, including one marked generalizes=yes. Corrections and improvements need one worked replay with concrete verification. Remove the draft marker only after the candidate is authored, then run:

<cli-command-from-hook> candidate validate <nid>

Promotion boundary

Validation makes a first-observation candidate promotion-ready when proof is strong. Recurrence only surfaces repeatedly unvalidated work; count is not proof.

Keep candidates local until the user approves a canonical change. After approval, strengthen the provenance owner, add the triggering regression case, regenerate provider packages, and use the owner's guarded review workflow. Mark it promoted only after the canonical change is accepted.

provenance: 2026-07-18, first Codex adapter for Recursive Harness Specialization; owner required immediate creation and dogfood for both missing capabilities and provenance-owned skill improvements.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.