agent-loop-ext
Crash-resilient external agent loop with state persistence and CI/CD integration
Infer measurable completion criteria for an agent-loop task from project docs, code, and AIWG standards when the user has not supplied --completion explicitly
$ npx -y skills add jmagly/aiwg --skill infer-completion-criteria --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/infer-completion-criteriaContext preview
The summary Claude sees to decide when to auto-load this skill.
Infer measurable completion criteria for an agent-loop task from project docs, code, and AIWG standards when the user has not supplied --completion explicitly
namespace: aiwg name: infer-completion-criteria aliases: [agent-loop-infer-completion, al-infer-completion, ralph-infer-completion] platforms: [all] description: Infer measurable completion criteria for an agent-loop task from project docs, code, and AIWG standards when the user has not supplied --completion explicitly commandHint: argumentHint: '"<task description>" [--task-type code|test|docs|refactor] [--non-interactive]' allowedTools: "Read, Glob, Grep, Bash" model: haiku category: automation modelRole: efficiency modelTier: economy
When a user starts an `agent-loop` task without supplying `--completion`, this skill derives a measurable, verifiable completion criterion from project state. The output must satisfy the `vague-discretion` rule: a concrete shell command or file-inspection check that returns pass/fail unambiguously.
Iteration is only as good as its gate. A loop with a vague gate ("until it's done") runs forever or exits prematurely. This skill is what turns "agent-loop this" into "agent-loop this until `<measurable thing>`."
The canonical name for the iterative-loop addon is **agent-loop**. `ralph` is the legacy name for the executor skill, retained as an alias; `al` is a short form. The detection/routing skill is `agent-loop` (which delegates to this skill when criteria are missing); the executor is `ralph` (canonical name forthcoming). Everywhere this skill says "agent-loop" you can read "ralph" as the legacy equivalent.
This skill is invoked by:
This skill does **not** run when `--completion` is explicit. The user's word is authoritative.
The skill is a deterministic walk through five evidence layers, plus one synthesis step. Each layer contributes candidate criteria; the synthesis picks the strongest measurable one and explains the chain of evidence.
Parse the user's task description for an intent verb. Map to a default criterion class:
| Verb / phrase | Criterion class | |---|---| | "fix tests", "make tests pass", "test failure" | Test suite passes (exit 0) | | "add tests", "increase coverage", "test coverage" | Coverage threshold met | | "fix types", "type errors", "migrate to typescript" | Type checker exits 0 | | "fix lint", "clean up warnings", "style" | Linter exits 0 | | "build", "make it compile" | Build command exits 0 | | "refactor", "extract", "rename" | Tests still pass AND build still passes (regression gate) | | "implement <X>", "add feature <X>" | Tests for the new code exist and pass | | "document", "add docs", "JSDoc" | Coverage check on docstrings/JSDoc presence | | "fix bug", "resolve issue #N" | Specific test for that bug passes AND existing suite still green | | "migrate", "upgrade" | Build + test + lint all green (no regression) |
If the verb is ambiguous, the skill falls back to "regression gate" (build + test + lint all green) as the safest default.
Read the project's context files. AIWG-managed projects often declare commands directly:
# Run tests npm test # Type check npx tsc --noEmit # Lint markdown npm exec markdownlint-cli2 "**/*.md"
Extract these as the canonical commands for their respective domains. The Development section of `CLAUDE.md` is the highest-trust source here — it's what the project's maintainers run.
Also scan for explicit completion-criterion conventions. Some projects state "a commit is not finished until CI passes" — that signals the CI command (or equivalent local invocation) is the gate.
Inspect the project's manifest files to discover scripts and tools:
| Manifest | Where to look | |---|---| | `package.json` | `scripts.test`, `scripts.lint`, `scripts.build`, `scripts.coverage`, `scripts.typecheck` | | `Cargo.toml` | implies `cargo test`, `cargo build`, `cargo clippy` | | `pyproject.toml` | `[tool.pytest]`, `[tool.ruff]`, `[tool.mypy]`, `scripts.*` | | `go.mod` | implies `go test ./...`, `go vet ./...`, `go build ./...` | | `Gemfile` | implies `bundle exec rspec`, `bundle exec rubocop` | | `pom.xml` / `build.gradle` | `mvn test`, `mvn verify`, `gradle test` | | `.tool-versions` / `mise.toml` | language version pins inform which tool is canonical |
When multiple scripts exist (e.g. `test`, `test:unit`, `test:integration`), prefer the script the project's own docs reference. If the docs don't reference any, prefer the most specific match to the task verb (e.g. for "fix integration test" → `test:integration`).
CI files encode the team's actual definition of "passes":
| CI system | Scan | |---|---| | GitHub Actions | `.github/workflows/*.yml` — extract `run:` steps from non-deploy jobs | | Gitea Actions | `.gitea/workflows/*.yml` — same | | GitLab CI | `.gitlab-ci.yml` — extract `script:` from test/lint jobs | | CircleCI | `.circleci/config.yml` | | Jenkins | `Jenkinsfile` |
The first non-trivial verification step in the primary workflow is the team's canonical "done" gate. If CI runs `npm test && npm run lint && npm run typecheck` in order, the inferred criterion is "all three exit 0."
If the project has a `.aiwg/` directory, scan for relevant context:
Reusable project context and specialist workflows for the AI tools you already use. Plan software, coordinate specialist reviews, prepare campaigns, investigate incidents, organize research, curate media, and maintain operational knowledge.
Repo: jmagly/aiwg
Crash-resilient external agent loop with state persistence and CI/CD integration
Detect requests for iterative autonomous agent loops and route to the appropriate loop executor
Automatically execute tests when code-generating agents modify source files, enforcing the execute-before-return pattern
Enable agent loops to learn from similar past tasks and share patterns across loops
Query and manage the executable feedback debug memory
Execute tests on generated code and iterate until passing