xiaolai/nlpm

Dernière activité source enregistrée: 30 septembre 2026 à 17:52

Catalogue SkillsMP mis à jour: 29 octobre 2026 à 18:30

skills collectés: 34

Étoiles GitHub: 151
Forks GitHub: 41

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# nlpm [![Validated by NLPM](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/xiaolai/nlpm/main/nlpm-badge.json)](https://github.com/xiaolai/nlpm/blob/main/nlpm-badge.json) Natural-Language Programming Manager — score, check, fix, and test NL artifacts across **Claude Code, Codex CLI, and Antigravity**. Tier-aware scoring with per-tool overlays. Part of the [xiaolai plugin marketplace](https://github.com/xiaolai/claude-plugin-marketplace). NLPM includes multi-tool validation that checks **manifest-vs-disk consistency** — the bug class where a SKILL.md exists on disk but is silently missing from `plugin.json` (and therefore invisible after `claude plugin install`). Verified across 8+ tools including Anthropic's official `plugin-validator` and the Linux Foundation's `skills-ref`. See [`analysis/ecosystem-gap.md`](analysis/ecosystem-gap.md) for the research. ## What it does NLPM treats natural language artifacts as **programs that can be linted**. Just as ESLint scores JavaScript and ruff scores Python, NLPM scores the markdown files that drive AI behavior: skills, agents, commands, rules, hooks, prompts, CLAUDE.md, and memory files. Eight commands, each doing one thing: | Command | What it does | |---------|-------------| | `/nlpm:ls` | Discover and inventory all NL artifacts in a repo | | `/nlpm:score` | Score artifact quality (100-point scale) | | `/nlpm:check` | Cross-component consistency checks | | `/nlpm:fix` | Auto-fix fixable issues | | `/nlpm:trend` | Track quality score trends over time | | `/nlpm:test` | Run NL artifact tests against spec files (TDD) | | `/nlpm:init` | Initialize NLPM for a project | | `/nlpm:security-scan` | Scan plugins for security risks in executable artifacts | Slash commands ship as a Claude Code plugin. The scoring rubric covers three ecosystems (Claude Code, Codex CLI, Antigravity) via tier-aware overlays — see [`analysis/multi-tool-design-2026-05.md`](analysis/multi-tool-design-2026-05.md). The standalone Python 3.11+ validator (`bin/nlpm-check`) has no Claude Code dependency and runs in pre-commit hooks or CI on any tool's artifacts. ### Beyond linting: the learning loop NLPM also runs as a self-evolving GitHub Actions pipeline that audits real plugin repos, contributes fix PRs, harvests teaching examples from clean ones, and feeds learnings back into its own rule catalog: - **Exemplar pipeline** (v0.8.17+): repos that audit clean at score ≥ 90 produce a teaching artifact under `auditor/exemplars/` — 62 published so far, covering 31 of the 50 Rules with real-world positive references. See [the gallery](auditor/exemplars/README.md). - **Rule-citation auto-PR** (v0.8.18+): `auditor-cite-exemplars.yml` runs weekly and opens a human-gated PR adding `> Real-world example: [<repo>]` links to `skills/nlpm/rules/SKILL.md`, so each rule documents both the bad case (in the rule body) and the good case (in a real repo). - **Two-stage drift detector** (v0.8.15–v0.8.16): `auditor/scripts/validate-rule-ids.py` re-validates every audit's `rule_id` against the rubric (type drift) and the rule's title keywords (semantic drift). The 2026-05-13 sweep found 990 mislabeled `rule_id`s across 128 historical audits; the validator is now wired as a soft-warn telemetry step in every new audit so future drift is caught immediately. - **Drift-filtered rule health**: `auditor/scripts/rule-health.py` reports `validated_hits` per rule (raw hits minus drift hits) and `exemplars_count` per rule, so the "needs attention" view is calibrated against actual rule violations rather than scorer noise. ## Installation Two install paths — both reach the same code. Pick one: **Via Anthropic's official community marketplace** (curated; updates lag the maintainer's marketplace by up to ~24h): ```bash claude plugin marketplace add anthropics/claude-plugins-community claude plugin install nlpm@claude-community --scope project # or --scope user ``` **Via the xiaolai marketplace** (latest version lands here first): ```bash claude plugin marketplace add xiaolai/claude-plugin-marketplace # Project scope (recommended) claude plugin install nlpm@xiaolai --scope project # Global (all projects) claude plugin install nlpm@xiaolai --scope user ``` > **Install fails with "Plugin not found in marketplace 'xiaolai'"?** Your local marketplace clone is stale. Run `claude plugin marketplace update xiaolai` and retry — `plugin install` does not auto-refresh. (The community marketplace doesn't have this caveat.) ## Quick Start In Claude Code: ``` /nlpm:ls # see what NL artifacts you have /nlpm:score # score them all /nlpm:score agents/ # score just agents /nlpm:score --changed # score only git-changed files /nlpm:check # check cross-component consistency /nlpm:fix # auto-fix what's fixable /nlpm:trend # track score history over time /nlpm:test # run NL-TDD specs ``` From CI or a pre-commit hook (no Claude Code required): ```bash curl -fsSL -o /usr/local/bin/nlpm-check \ https://raw.githubusercontent.com/xiaolai/nlpm/main/bin/nlpm-check chmod +x /usr/local/bin/nlpm-check nlpm-check . # exit 1 on high-confidence findings ``` ## For plugin/skill authors — standalone validator If you author a plugin and want NLPM in your **pre-commit hook, CI, or pre-publish gate**, use the standalone binary at [`bin/nlpm-check`](bin/nlpm-check). It's a single Python 3.11+ file with no external dependencies. It runs the deterministic subset of `/nlpm:check` — including the manifest-vs-disk consistency check that no other validator (Anthropic's official `plugin-validator`, Linux Foundation's `skills-ref`, third-party tools) currently covers. ```bash # One-line install curl -fsSL -o /usr/local/bin/nlpm-check \ https://raw.githubusercontent.com/xiaolai/nlpm/main/bin/nlpm-check chmod +x /usr/local/bin/nlpm-check # Run in your plugin repo nlpm-check . ``` Templates ship in [`templates/`](templates/): - `pre-commit-nlpm.sh` — drop-in git pre-commit hook - `workflows/nlpm-check.yml` — drop-in GitHub Actions workflow See [`docs/for-authors.md`](docs/for-authors.md) for the full author guide. See [`analysis/ecosystem-gap.md`](analysis/ecosystem-gap.md) for the research on why this check exists and which other validators do (and don't) cover it. ## Scoring System Scores start at 100 and go down. Every issue has a fixed penalty. The score is deterministic: same artifact, same penalties, same number. | Score | Band | Meaning | |-------|------|---------| | 90-100 | Excellent | Production-ready | | 80-89 | Good | Minor gaps | | 70-79 | Adequate | Meets threshold, should improve | | 60-69 | Weak | Below threshold | | <60 | Rewrite | Fundamental problems | Default pass threshold: 70. Configure in `.claude/nlpm.local.md`. See `skills/nlpm/scoring/SKILL.md` for the full penalty tables (the Codex, Antigravity, memory-file and workflow-program tables are in `skills/nlpm/scoring/references/`, indexed from SKILL.md). See `skills/nlpm/rules/SKILL.md` for the 50 Rules of Natural Language Programming. ## What it scores 20+ artifact types across one universal floor and three per-tool overlays. The scorer auto-classifies each artifact by its path (see `agents/scorer.md` step 3 for the tier classifier) and applies the matching rules. | Tier | Artifacts | |------|-----------| | Universal (Tier 1, open spec at agentskills.io) | `SKILL.md`, `AGENTS.md` (canonical universal memory file, per nlpm decision) | | Claude Code (Tier 2-Claude) | `commands/`, `shared partials`, `agents/`, `skills/`, `hooks/hooks.json`, `.claude-plugin/plugin.json`, `.claude-plugin/marketplace.json`, `.mcp.json`, `CLAUDE.md`, `.claude/rules/`, `.claude/settings.json`, `.lsp.json`, `monitors/monitors.json`, `~/.claude/projects/*/memory/*.md` | | Codex CLI (Tier 2-Codex) | `.agents/skills/<n>/SKILL.md`, `.codex-plugin/plugin.json`, `.agents/plugins/marketplace.json`, `.codex/config.toml` (TOML — `[mcp_servers.*]`, `[hooks.*]`, `[agents]` global settings), `.codex/agents/*.toml` (subagent definitions), `.codex/hooks.json`, `agents/openai.yaml` sidecar, root `AGENTS.md` (hierarchical) | | Antigravity (Tier 2-Antigravity, advisory) | `.gemini/skills/`, `.agent/skills/`, `.gemini/commands/<n>.toml`, `.gemini/settings.json` (with embedded `mcpServers` + `hooks`), `gemini-extension.json`, `GEMINI.md` | ## NL-TDD Write test specs BEFORE writing artifacts: ``` 1. Write spec: .nlpm-test/my-agent.spec.md 2. /nlpm:test -> RED (artifact doesn't exist) 3. Write artifact: agents/my-agent.md 4. /nlpm:test -> check trigger accuracy, output format, score 5. /nlpm:score -> verify quality score 6. Iterate -> fix until GREEN ``` See `skills/nlpm/testing/SKILL.md` for the full spec format. ## Configuration Create `.claude/nlpm.local.md` (or run `/nlpm:init`): ```yaml --- strictness: standard score_threshold: 70 rule_overrides: R09: { min_examples: 2 } # restore the stricter two-example bar R05: { threshold: 600 } # allow skills up to 600 lines R23: { budget: 800 } # increase rules budget --- ``` | Level | Threshold | Effect | |-------|-----------|--------| | Relaxed | 60 | Only flag seriously broken artifacts | | Standard | 70 | Flag artifacts that need improvement | | Strict | 80 | Flag anything below good quality | ## Continuous Enforcement NLPM ships a `PostToolUse` hook that fires when you write or edit files. A shell script (`scripts/check-artifact.sh`) classifies the file -- if it's an NL artifact, Claude reminds you to run `/nlpm:score`. Non-NL files produce no output. This is advisory -- it does not block writes. For blocking enforcement, use a `PreToolUse` hook (see tdd-guardian for an example). ## Architecture ``` commands/ User-facing commands (8 + 3 shared partials) ls.md Discover artifacts -> dispatches scanner score.md Score quality -> dispatches scorer + vague-scanner in parallel check.md Cross-component checks -> dispatches checker fix.md Auto-fix issues -> dispatches scorer trend.md Track score history -> dispatches scorer + vague-scanner test.md Run NL-TDD specs -> dispatches tester init.md Configure project security-scan.md Scan plugins for security risks -> dispatches security-scanner shared/ discover.md Artifact path patterns (not user-invocable) classify.md Type classification rules (not user-invocable) append-history.md Persist scoring snapshot to .claude/nlpm-history.json with scope marker (not user-invocable) agents/ Dispatched by commands (6 agents) scanner.md haiku -- fast artifact discovery scorer.md sonnet -- 100-point quality scoring checker.md sonnet -- cross-component consistency vague-scanner.md haiku -- mechanical vague-word counting tester.md sonnet -- evaluates artifacts against test specs security-scanner.md sonnet -- security risk detection in executable artifacts skills/nlpm/ Knowledge base (17 skills) Core (loaded by agents): conventions/ Universal NL floor: SKILL.md open spec, AGENTS.md, vague-quantifier list, naming conventions-claude/ Claude Code overlay: .claude/* paths, plugin.json, hook events, CLAUDE.md, tool catalog conventions-codex/ Codex CLI overlay: .codex/config.toml, .codex-plugin/plugin.json, .agents/skills/, openai.yaml sidecar conventions-antigravity/ Antigravity overlay: .gemini/* + .agent/*, GEMINI.md (advisory) patterns/ NL programming best practices + anti-patterns scoring/ Penalty tables with rule number cross-references rules/ The 50 Rules of Natural Language Programming (R01-R50) vocabulary/ Canonical noun/verb registry for R51 drift detection (opt-in) testing/ NL-TDD spec format, test patterns security/ Security pattern database for executable artifact scanning Writing Reference (loaded on demand): writing-skills/ How to write SKILL.md files writing-agents/ How to write agent definitions writing-rules/ How to write .claude/rules/ files writing-prompts/ Universal prompt engineering guide writing-hooks/ How to write Claude Code hooks writing-plugins/ How to design and build plugins orchestration/ Multi-agent workflow patterns hooks/ hooks.json PostToolUse advisory (command type + check-artifact.sh) scripts/ check-artifact.sh NL artifact classifier for the PostToolUse hook .nlpm-test/ Self-test specs (dogfooding NL-TDD) bin/ Standalone author surface (v0.8.0+) nlpm-check Pure-Python validator for pre-commit / CI / pre-publish nlpm-badge shields.io endpoint generator + optional attestation sidecar tests/ Python unittest suite (81 tests total) test_nlpm_check.py bin/nlpm-check test_nlpm_badge.py bin/nlpm-badge test_validate_rule_ids.py auditor/scripts/validate-rule-ids.py test_exemplar_helpers.py batch-process.py + rule-health.py exemplar paths test_exemplar_gallery_and_citations.py build-exemplar-gallery.py + propose-rule-citations.py templates/ Drop-in author templates pre-commit-nlpm.sh git pre-commit hook workflows/nlpm-check.yml GitHub Actions workflow docs/ for-authors.md Full guide for plugin/skill authors architecture.md Per-component inventory (commands, agents, skills, hooks) auditor.md Auditor pipeline reference (workflows, data, gates, scripts) site.md nlpm.com site reference (pages, build, deploy workflows) analysis/ ecosystem-gap.md Why this validator exists (stable ref) scope-expansion-2026-05.md Author-surface plan 2026-05-11-why-obvious-bugs-persist.md Original research snapshot auditor/ Self-evolution pipeline (GitHub Actions + data) audits/ Per-repo audit reports and findings sidecars exemplars/ Teaching artifacts from clean audits + auto-generated gallery (v0.8.17+) case-studies/ Narrative articles from post-merge re-audits disclosures-pending/ Security disclosures queued for manual filing feedback/ Rolling rule-health summary findings.jsonl Append-only audit findings (joined by fingerprint) disagreements.jsonl self_false_positive + maintainer_rejected + pr_comments_snapshot logs/events.jsonl Lifecycle events + outcome signals + drift telemetry registry/ Repo tracking database scripts/ 25+ pipeline helpers — see "Auditor — Self-Evolution Pipeline" prompts/ Shared rubric and exemplar-writer prompts reports/ Daily pipeline reports ``` ## Tips - **Score early, score often.** Run `/nlpm:score` after writing any new artifact. - **Use `--changed` for speed.** `score --changed` only scores git-modified files. - **Use `/nlpm:trend` before releases.** Catches regressions that individual scoring misses. - **Do not chase 100.** 85+ is excellent. The last 5-10 points are diminishing returns. - **R01 is the most common penalty.** "appropriate", "relevant", "as needed" each cost -2. Replace with measurable criteria. - **Auto-fix handles the mechanical stuff.** Focus your energy on descriptions, examples, and scope notes. - **Pre-commit + slash commands together.** Run `nlpm-check` in your pre-commit hook for the deterministic checks; let `/nlpm:score` handle the judgment calls inside Claude Code. ## Troubleshooting **"Score seems too low"** -- Check which penalties hit. Scoring is deterministic. Vague quantifiers stack up fast. **"Writing skill didn't load"** -- Use keywords from the skill's description: "write an agent definition", "create a new agent". **"Check found orphans that aren't really orphans"** -- Writing skills are on-demand (loaded by Claude, not referenced by agents). This is expected. **"Trend shows no history"** -- Run `/nlpm:score` first to create the baseline snapshot. ## Case Studies 25+ case studies in [`case-studies/`](case-studies/) from the auditor pipeline. A few representative ones: - [The frontmatter tax: 19 silent registration failures in a 33,000-star plugin collection](case-studies/2026-04-24-wshobson-agents.md) — `wshobson/agents`, 100 artifacts sampled of 509, 5 PRs batched and agentically merged in 13 seconds. (Companion [learnings debrief](case-studies/2026-04-18-wshobson-agents-learnings.md).) - [Four bytes of quoting, approved by two OpenAI engineers](case-studies/2026-04-07-openai-codex-plugin-cc.md) — `openai/codex-plugin-cc`, 93/100 Gold tier, two shell-injection fixes merged by OpenAI contributors in 39 hours. - [Auditing kubesphere/kubesphere](case-studies/2026-05-07-kubesphere-kubesphere.md) — 16k-star repo, 18 findings including duplicate sections and broken YAML, surfaced by manifest-vs-disk and cross-component checks the other validators don't run. - [When the Linter Met Its Match](case-studies/2026-04-06-how-we-helped-gsd.md) — `gsd-build/get-shit-done`, 80 files scored, 5 PRs accepted, plus the false-positive that improved NLPM itself. ## Effectiveness As of 2026-05-19 the auditor pipeline has filed 278 PRs across 44 distinct accepting repos, with a 71% acceptance rate (98 merged + 20 applied-separately, 49 rejected, 111 still open). The following data points are the highest-signal: - **`google-gemini/gemini-skills`** and **`googleworkspace/cli`** — both Google orgs that originally CLA-blocked the pipeline — ended up accepting work: 2 merged and 4 applied-separately respectively, once the CLA gate was satisfied. - **`openai/codex-plugin-cc`** has 2 merges — first-party OpenAI org acceptance. - **`kubesphere/kubesphere`** (24k+ stars) accepted 5 PRs — the highest-profile downstream. - 8 repos (`zubair-trabzada/geo-seo-claude`, `wshobson/agents`, `sickn33/antigravity-awesome-skills`, `kubesphere/kubesphere`, `jeremylongshore/claude-code-plugins-plus-skills`, `Jeffallan/claude-skills`, `hesreallyhim/awesome-claude-code`, `caliber-ai-org/ai-setup`) each hit the per-repo PR cap of 5 — more PRs could ship if the cap were raised. - 2 repos have crossed into **rule-adoption** (maintainer credited NLPM in CHANGELOG or systemically backfilled siblings): [`jeremylongshore/claude-code-plugins-plus-skills`](https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/main/CHANGELOG.md) and [`sickn33/antigravity-awesome-skills`](https://github.com/sickn33/antigravity-awesome-skills/blob/main/CHANGELOG.md). ## Auditor — Self-Evolution Pipeline The `auditor/` directory contains a GitHub Actions pipeline that systematically discovers, audits, and contributes to Claude Code repos across GitHub. Two branches run in parallel: bugs become contribute PRs, clean repos become teaching exemplars. Both branches feed back into NLPM's rules. ``` discover (weekly) → audit ├─ has bugs ─→ contribute PRs ─→ track merges ─→ write case study │ ↓ │ feedback/log.json │ ↓ └─ clean (≥90) ─→ write exemplar ─→ gallery ──→ rule-citation PR (weekly, human-gated) ↓ update NLPM rules → audit better ``` 15 workflows in [`.github/workflows/auditor-*.yml`](.github/workflows/): discover, batch-processor, audit, contribute, track, case-study, **exemplar** (v0.8.17+), **cite-exemplars** (v0.8.18+), classify, daily-report, suppressions, refine-rules, docs-diff, rule-review, integration-test. Human-in-the-loop via issue labels at the audit, contribute, exemplar, and rule-refinement decision points. See [auditor/README.md](auditor/README.md) for the full pipeline documentation and [auditor/SCHEMAS.md](auditor/SCHEMAS.md) for the data contracts. ## Prerequisites - **Slash commands (`/nlpm:*`)**: none. Pure markdown — no Python, no Node.js. - **Standalone `bin/nlpm-check`**: Python 3.11+ (stdlib only; no pip install). - **Auditor workflows**: `CLAUDE_CODE_OAUTH_TOKEN`, `PAT_TOKEN`, and `OPENAI_API_KEY` GitHub repo secrets. ## License ISC ## Native validation and behavioral evidence Run Claude's current `plugin validate --strict` alongside NLPM, then native `plugin eval --no-publish` for behavior with and without the plugin. Manifest consistency, rubric scores and observed behavior are separate evidence. A 100-point score is not a runtime guarantee. Revisit ecosystem comparisons when the host validator changes; the linked gap analysis records its research date, not perpetual exclusivity.

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How to write Claude Code agents: example blocks, model choice, least-privilege tools.

Langue du texte source : anglais

2026-09-30

Affichage de 34 skills collectés sur 34.