Claude Code plugin marketplace: autonomous implementation skills (implement-issues, plan-slices, pr-and-merge, commit-and-push, PI/Codex delegation, dispatcher and requirements-checker agents)
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Karsten Samaschke 5c4c48cebc feat(routing): GPT-5.6 tiers on the Codex lane too — explicit variant ids — 0.10.0
Corrects 0.9.0's 'not available on Codex' conclusion, which came from an
incomplete probe: the alias gpt-5.6 and the guessed gpt-5.6-codex are
rejected under ChatGPT-account auth, but the explicit variant ids
gpt-5.6-luna/-terra/-sol all work on codex-cli 0.144.1 (all three
smoke-tested live). Both external lanes now carry identical tiering:
luna fast, terra ordinary, sol demanding/agentic/review. use-codex
documents the alias-vs-variant-id gotcha; gpt-5.5 and spark demoted to
error fallbacks; dispatcher table, implement-issues routing,
pr-and-merge reviewer example, herdr reference example, and
prepare-work's cheap-lane audit all updated.
2026-07-10 07:56:28 +02:00
.claude chore(git-ops): record forgejo token_file; repo renamed to implementation-skills-claude 2026-07-05 09:24:17 +02:00
.claude-plugin feat(fable-skills): new plugin v0.2.0 — Fable-tier discipline skills for Opus-class models 2026-07-05 12:21:56 +02:00
plugins feat(routing): GPT-5.6 tiers on the Codex lane too — explicit variant ids — 0.10.0 2026-07-10 07:56:28 +02:00
tests perf(implement-issues): token-lean loop behavior — 0.7.0 2026-07-06 18:29:58 +02:00
.gitignore feat(implement-issues): add active-run heartbeat (#4) 2026-07-06 04:01:47 +00:00
README.md feat(fable-mode): explicit 'fable mode' umbrella activation — 0.3.0 2026-07-07 15:05:47 +02:00

Claude Implementation Skills

This repository is a Claude Code plugin marketplace that publishes two plugins:

  • implementation-skills — an autonomous implementation toolchain for planning slices, implementing backlog work, delegating across execution lanes, and driving changes through commit, PR, review, and merge workflows.
  • fable-skills — operating-discipline skills that teach lighter Claude models (Opus, Sonnet, Haiku) to work the way the Fable tier does on coding and research tasks when Fable is not available.

Plugin contents

Skills

Skill Summary
implement-issues Works actionable backlog items through triage, TDD implementation, requirements checks, review, commit, PR, merge, and any configured deploy gate; optional parallel execution runs concurrent implementation with a serial merge queue.
plan-slices Plans vertical product slices and can file staged slices as tracker issues using the shared work-item contract.
prepare-work Interactively shapes a raw ask (feature, bug, epic) with the user — research-first questions, decision-completeness audit, contract-shaped breakdown, file or stage per scope.
pi-delegate Runs pipeline-integrated delegation to PI-Agent for adversarial review or isolated-worktree implementation attempts; optional Herdr PTY-pane backend for long-running runs.
use-pi Offloads context-heavy ad hoc work to one or more PI-Agent subagents; optional Herdr PTY-pane backend for long-running or detach-surviving runs.
use-codex Offloads context-heavy ad hoc work to one or more OpenAI Codex CLI subagents; optional Herdr PTY-pane backend for long-running runs.
commit-and-push Reviews the current diff, excludes secrets and junk, writes a conventional commit, and pushes via the project host.
pr-and-merge Creates or reuses a PR, runs adversarial review/fix loops, merges, and refreshes the local base branch.

Agents

Agent Summary
dispatcher Chooses the engine, model, effort, and timeout for delegated work without executing the task.
requirements-checker Audits implemented work against acceptance criteria and reports evidence-backed COVERED/PARTIAL/MISSING verdicts.

fable-skills plugin

Fable-tier operating discipline for Opus-class models on long-running, challenging coding and research work. Design premise (validated with Opus introspection and behavioral evals): Opus already performs at criterion on any task that fits one fresh context window — the failures worth encoding are horizon failures (compaction losses, goal and constraint drift, sunk-cost thrash, verification decay), and those are self-masking, so every skill triggers on countable events (third failed fix, any compaction, any edit after the last green run), never on self-diagnosis.

Skill Summary
fable-mindset Judgment core the others reference: one-way/two-way door gates before irreversible actions, first-frame skepticism, deliberate altitude (minimal correct over impressive), honest outcomes (never manufacture green), reports for the reader who was absent.
fable-long-horizon Endurance: externalized notes with verbatim ask + definition-of-done + constraints + epistemic ledger, compaction survival (summaries flatten epistemic status), checkpoint cadence with stuck-detector, retreat paths, three-strike sunk-cost rule, endgame protocol that drives behavior not just green tests.
fable-coding Building: risk-first decomposition, walking skeleton as the anti-over-building gate, vertical slices, mechanical/behavioral commit separation on refactors, plus the any-scale non-negotiables (reproduce-first, mechanism fixes, scoped diffs, native idiom, urgency never skips verification).
fable-debugging Hard bugs: deterministic reproduction first, differential diagnosis over serial guessing, ruled-out ledger, probe/fix separation, multi-causal trap (persisting symptom ≠ false hypothesis), nonlocal-cause checklist (aliasing, caching, ordering, hidden persistence), fix at the invariant.
fable-research Investigation: falsifiable framing, complete enumeration, decoy-guard and computed-value traps, one deliberate disconfirming search per claim, claim ledger with confidence, draft-thesis-early over collect-forever, verdict-first cited synthesis.
fable-orchestration Delegation: context window as the scarcest resource, sweep-detection as the delegation trigger, non-overlapping work-order scopes, subagent output as claims (graduated verification), no edits to files an in-flight reader has in scope, find→verify→synthesize.
fable-mode Umbrella activation: an explicit "fable mode" utterance loads and arms all six skills for the session — one-line acknowledgement, immediate long-horizon scaffolding, all tripwires armed, survives compaction, exits only on explicit request.

Eval record (2026-07-05, tests/fable-skills.acceptance.md, tests/setup-fable-eval-sandbox.sh): baseline Opus passed all short-horizon sandboxes including a hardened pressure/misdirection coding task and an aliased-cache debugging task, and answered externally-described long-horizon judgment probes correctly — establishing that its gap is in-the-moment recognition, not knowledge. Baseline Haiku failed honest-disclosure criteria that the skills then fixed. Skill content for the long-horizon dimension is grounded in structured Opus introspection (failures confirmed real, countable-trigger design, shared epistemic-ledger primitive) plus no-regression and ceremony-cost checks on short tasks.

Shared conventions

  • The shared work-item contract lives at plugins/implementation-skills/skills/implement-issues/references/work-item-contract.md.
  • Per-repository configuration is stored in .claude/implement-issues.md and .claude/git-ops.md in the consuming repository.
  • Commit messages, PR text, and automation output follow a zero-AI-attribution policy.
  • Implementation workflows are TDD-first: tests or executable acceptance gates lead the change, and review follows the implemented evidence.
  • Skills cross-reference sibling skills within this plugin using relative paths, so the plugin can be installed as a portable bundle.

Installation

Add the marketplace:

/plugin marketplace add https://git.samaschke.de/ksamaschke/claude-implementation-skills.git

Install the plugins:

/plugin install implementation-skills@samaschke-skills
/plugin install fable-skills@samaschke-skills

Development

tests/ holds the TDD acceptance specs for the git operations skills (commit-and-push, pr-and-merge), prepare-work, and the fable-skills plugin (fable-skills.acceptance.md plus the setup-fable-eval-sandbox.sh fixture builder). The packaged skills were adversarially reviewed and sandbox-tested before release.