post-release

name	post-release
description	Run automated post-release workflow (eval baselines, dashboard, docs) for AILANG releases. Runs the tier-based benchmark suite (core+stretch+frontier by default) for standard + agent evals with validation and progress reporting. Use when user says "post-release tasks for vX.X.X" or "update dashboard". Fully autonomous with pre-flight checks.

AILANG Post-Release Tasks

Run post-release tasks for an AILANG release: evaluation baselines, dashboard updates, and documentation.

Current State

• Current version: !'cat std/VERSION'
• Latest tag: !'git describe --tags --abbrev=0 2>/dev/null || echo "no tags"'
• GitHub release: !'gh release list --limit 3 2>/dev/null | head -3 || echo "gh not available"'
• Existing baselines: !'ls eval_results/baselines/ 2>/dev/null | tail -5 || echo "none"'
• Dashboard version: !'jq -r .version docs/static/benchmarks/latest.json 2>/dev/null || echo "not found"'
• Active changelog: !'ls changelogs/ | grep current 2>/dev/null'

Use the data above first. Only re-run these commands manually if the injected context is empty or you need to refresh after making changes.

Quick Start

Most common usage:

# User says: "Run post-release tasks for v0.3.14"
# This skill will:
# 1. Run eval baseline (extended_suite: 7 production models + lang-harness sweep) - ALWAYS USE --full FOR RELEASES
# 2. Update website dashboard (JSON with history preservation)
# 3. Update axiom scorecard KPI (if features affect axiom compliance)
# 4. Extract metrics and UPDATE CHANGELOG.md automatically
# 5. Move design docs from planned/ to implemented/
# 6. Run docs-sync to verify website accuracy (version constants, PLANNED banners, examples)
# 7. Commit all changes to git

🚨 CRITICAL: For releases, ALWAYS use --full flag by default

• Dev models (without --full) are only for quick testing/validation, NOT releases
• Users expect full benchmark results when they say "post-release" or "update dashboard"
• Never start with dev models and then try to add production models later

When to Use This Skill

Invoke this skill when:

• User says "post-release tasks", "update dashboard", "run benchmarks"
• After successful release (once GitHub release is published)
• User asks about eval baselines or benchmark results
• User wants to update documentation after a release

Available Scripts

scripts/run_eval_baseline.sh <version> [--full] [--cross-harness]

Run evaluation baseline for a release version.

🚨 CRITICAL: ALWAYS use --full for releases!

Usage:

# ✅ RECOMMENDED release baseline (standard + agent + 4-language Explorer sweep) — ~$23
.claude/skills/post-release/scripts/run_eval_baseline.sh 0.15.0 --full --lang-harness

# Standard + agent only (no 4-lang Explorer sweep) — ~$16
.claude/skills/post-release/scripts/run_eval_baseline.sh v0.15.0 --full

# Major release: includes cross-harness comparison (gpt5-5 + opencode-gpt5-5 etc.) — ~$47
.claude/skills/post-release/scripts/run_eval_baseline.sh 0.15.0 --full --cross-harness

# ❌ Dev only — 3 cheap models, AILANG lang only (quick testing/validation) — ~$3.50
.claude/skills/post-release/scripts/run_eval_baseline.sh 0.15.0

Output:

Running eval baseline for 0.3.14...
Mode: FULL (extended_suite: 7 production models)
Expected cost: ~$16 (FULL) or ~$23 (FULL + lang-harness) or ~$47 (FULL + cross-harness)
Expected time: ~30-60 minutes

[Running benchmarks...]

✓ Baseline complete
  Results: eval_results/baselines/0.3.14
  Files: 726 result files

What it does:

• Step 1: Standard eval (0-shot + self-repair)
  • Uses extended_suite (--full, 10 models): gpt5-5, gpt5-4-mini, claude-opus-4-8 (Jun 2026 flagship), claude-sonnet-4-6, gemini-3-1-pro, gemini-3-flash, or-glm-5-1, or-minimax-m3, or-deepseek-v4-flash, or-deepseek-v4-pro (modern OS refreshed 2026-06-04)
  • Or dev_models (default): gpt5-4-mini, claude-haiku-4-5, gemini-3-flash
  • Both AILANG and Python; all benchmarks in selected tier(s)
  • Cloud-vs-OS note: in standard mode the best OS model (glm-5.1, 90% de-flaked) MATCHES the best cloud model (gemini-3-1-pro, 90%) at ~½ the cost; minimax-m3 (87%, $0.30/1M) ties opus/gpt5-5 (87%, $5/1M) at ~1/16 the cost.
• Step 2: Agent eval — AILANG-only (redesigned 2026-07-11). Agent mode measures the agent-loop uplift on WEAK models, so the subjects are the free on-device GPU models; a small lab sample gives reference signal. It does not run the expensive multi-turn cloud fleet (near-ceiling cloud models add little agent signal at high $).
  • agent_suite (7 cloud weak+reference models, run in parallel): gpt5-6-luna (codex — OpenAI weak/fast), claude-haiku-4-5 (claude — Anthropic weak), claude-sonnet-4-6 (claude — longitudinal anchor), opencode-or-deepseek-v4-pro (OS agent champion), opencode-or-deepseek-v4-flash (OS best-value), opencode-or-glm-5-1 + opencode-or-glm-5-2 (settle whether 5.2 is actually good).
  • On-device GPU models are NOT in this suite — they are covered continuously by the daily rig rotation (dev.ailang.os-rotation-filler → eval_results/rotation/os-rolling, --agent --bank-by-version). For the on-device-vs-cloud agent table, aggregate the rotation's per-version GPU data with this suite's results.
  • motoko- removed 2026-06-04*: the AILANG-native motoko/bun harness hangs on the rig (0 completions, orphans subprocesses). Pending agent-harness-instability diagnosis; re-add when reliable.
  • Tier system (v0.14.0+, frontier added v0.29.0): smoke (23), core (19), stretch (21), frontier (16), vision (9) — counts as of the 2026-07-11 v0.29.2 re-tier (8 stretch→frontier promotions from the ELO/frontier-model-fail audit; demotions deferred to the post-agent audit)
  • 🚫 Never run smoke for cloud models. Smoke is the cheap/fast sanity tier for the local OS-model iteration loop (the nightly rig, Ollama, de-flaking). Cloud/API models (Anthropic, OpenAI, Google, OpenRouter) go straight to core,stretch,frontier — smoke would just spend API budget re-confirming saturated benchmarks every model already passes, with zero added signal. The only time smoke joins a cloud run is an explicit --tier smoke,core,stretch,frontier full audit.
  • Default scope: core,stretch,frontier — Core is the headline metric, Stretch is harder mixed results, Frontier is the top-end discriminator (release baselines are its only routine data source)
  • Expected: core 70%+ for AILANG; vision intentionally low
  • Feeds the ailang-vs-python comparison story in the Model Leaderboard page
• Step 3 (--lang-harness): Language × Harness sweep — cheapest models × 4 languages
  • lang_harness_suite: claude-haiku-4-5, gemini-3-flash, gpt5-4-mini, opencode-haiku
  • All 4 languages: ailang, python, javascript, go
  • Tier: core only (19 benchmarks) — stretch/frontier are skipped here even if a wider --tier was set globally
  • Note: 4 core benchmarks are AILANG/Python-only (contract_bst_validate, contract_roman_numeral, effect_composition, effect_tracking_io_fs) and auto-skip on JS/Go runs
  • Feeds the Agent Harness Explorer language spread and cross-harness comparison data
  • Cost: ~$7 extra
• --cross-harness: Replaces Step 2 with harness_suite (6 models, paired across harnesses)
  • claude-sonnet-4-6 + opencode-sonnet-4-6, gemini-3-flash + opencode-gemini-3-flash, gpt5-5 + opencode-gpt5-5
  • Cost: ~$31 extra vs base FULL (3x)
• Saves combined results to eval_results/baselines/vX.X.X/
• Accepts version with or without 'v' prefix

⚠️ Note: gpt5-5-pro is in models.yml but not in any default suite — agent mode is blocked (codex rejects with ChatGPT account, opencode returns 0 tool calls). Don't add it to suites.

scripts/update_dashboard.sh <version>

Update website benchmark dashboard with new release data.

Usage:

.claude/skills/post-release/scripts/update_dashboard.sh 0.3.14

Output:

Updating dashboard for 0.3.14...

1/5 Generating Docusaurus markdown...
  ✓ Written to docs/docs/benchmarks/performance.md

2/5 Generating dashboard JSON with history...
  ✓ Written to docs/static/benchmarks/latest.json (history preserved)

3/5 Validating JSON...
  ✓ Version: 0.3.14
  ✓ Success rate: 0.627

4/5 Clearing Docusaurus cache...
  ✓ Cache cleared

5/5 Summary
  ✓ Dashboard updated for 0.3.14
  ✓ Markdown: docs/docs/benchmarks/performance.md
  ✓ JSON: docs/static/benchmarks/latest.json

Next steps:
  1. Test locally: cd docs && npm start
  2. Visit: http://localhost:3000/ailang/docs/benchmarks/performance
  3. Verify timeline shows 0.3.14
  4. Commit: git add docs/docs/benchmarks/performance.md docs/static/benchmarks/latest.json
  5. Commit: git commit -m 'Update benchmark dashboard for 0.3.14'
  6. Push: git push

What it does:

• Generates Docusaurus-formatted markdown
• Updates dashboard JSON with history preservation
• Validates JSON structure (version matches input exactly)
• Clears Docusaurus build cache
• Provides next steps for testing and committing
• Accepts version with or without 'v' prefix

scripts/extract_changelog_metrics.sh [json_file]

Extract benchmark metrics from dashboard JSON for CHANGELOG.

Usage:

.claude/skills/post-release/scripts/extract_changelog_metrics.sh
# Or specify JSON file:
.claude/skills/post-release/scripts/extract_changelog_metrics.sh docs/static/benchmarks/latest.json

Output:

Extracting metrics from docs/static/benchmarks/latest.json...

=== CHANGELOG.md Template ===

### Benchmark Results (M-EVAL)

**Overall Performance**: 59.1% success rate (399 total runs)

**By Language:**
- **AILANG**: 33.0% - New language, learning curve
- **Python**: 87.0% - Baseline for comparison
- **Gap: 54.0 percentage points (expected for new language)

**Comparison**: -15.2% AILANG regression from 0.3.14 (48.2% → 33.0%)

=== End Template ===

Use this template in CHANGELOG.md for 0.3.15

What it does:

• Parses dashboard JSON for metrics
• Calculates percentages and gap between AILANG/Python
• Auto-compares with previous version from history
• Formats comparison text automatically (+X% improvement or -X% regression)
• Generates ready-to-paste CHANGELOG template with no manual work needed

scripts/cleanup_design_docs.sh <version> [--dry-run] [--force] [--check-only]

Move design docs from planned/ to implemented/ after a release.

Features:

• Detects duplicates (docs already in implemented/)
• Detects misplaced docs (Target: field doesn't match folder version)
• Only moves docs with "Status: Implemented" in their frontmatter
• Docs with other statuses are flagged for review

Usage:

# Check-only: Report issues without making changes
.claude/skills/post-release/scripts/cleanup_design_docs.sh 0.5.9 --check-only

# Preview what would be moved/deleted/relocated
.claude/skills/post-release/scripts/cleanup_design_docs.sh 0.5.9 --dry-run

# Execute: Move implemented docs, delete duplicates, relocate misplaced
.claude/skills/post-release/scripts/cleanup_design_docs.sh 0.5.9

# Force move all docs regardless of status
.claude/skills/post-release/scripts/cleanup_design_docs.sh 0.5.9 --force

Output:

Design Doc Cleanup for v0.5.9
==================================

Checking 5 design doc(s) in design_docs/planned/v0_5_9/:

Phase 1: Detecting issues...

  [DUPLICATE] m-fix-if-else-let-block.md
              Already exists in design_docs/implemented/v0_5_9/
  [MISPLACED] m-codegen-value-types.md
              Target: v0.5.10 (folder: v0_5_9)
              Should be in: design_docs/planned/v0_5_10/

Issues found:
  - 1 duplicate(s) (can be deleted)
  - 1 misplaced doc(s) (wrong version folder)

Phase 2: Processing docs...

  [DELETED] m-fix-if-else-let-block.md (duplicate - already in implemented/)
  [RELOCATED] m-codegen-value-types.md → design_docs/planned/v0_5_10/
  [MOVED] m-dx11-cycles.md → design_docs/implemented/v0_5_9/
  [NEEDS REVIEW] m-unfinished-feature.md
                 Found: **Status**: Planned

Summary:
  ✓ Deleted 1 duplicate(s)
  ✓ Relocated 1 doc(s) to correct version folder
  ✓ Moved 1 doc(s) to design_docs/implemented/v0_5_9/
  ⚠ 1 doc(s) need review (not marked as Implemented)

What it does:

• Phase 1 (Detection): Identifies duplicates and misplaced docs
• Phase 2 (Processing):
  • Deletes duplicates (same file already exists in implemented/)
  • Relocates misplaced docs (Target: version doesn't match folder)
  • Moves docs with "Status: Implemented" to implemented/
  • Flags docs without Implemented status for review
• Creates target folders if needed
• Removes empty planned folder after cleanup
• Use --check-only to only report issues, --dry-run to preview actions, --force to move all

Post-Release Workflow

1. Verify Release Exists

git tag -l vX.X.X
gh release view vX.X.X

If release doesn't exist, run release-manager skill first.

1a. Refresh Codebase Statistics (REQUIRED — commit, don't rely on CI)

The docs/static/codebase_stats.json file drives the Codebase Statistics page (LOC/token/commit growth chart).

⚠️ The deploy workflow regenerates this file at build time but does NOT commit it back — it only bakes the result into the Pages artifact. The generator (generate_codebase_stats.sh) appends only the version it runs on to the committed history. So if a release is not captured + committed here, that version is permanently skipped from the history chart, producing visible gaps (e.g. the 0.14.1 → 0.25.0 jump that skipped every 0.15–0.24 graduation). You must run + commit this every release.

# Generate stats for THIS release and append to the committed history
AILANG_VERSION=vX.X.X bash tools/generate_codebase_stats.sh

# Sanity check: current == this release, and the new entry is in history
jq -r '.current.version, (.history[-1].version)' docs/static/codebase_stats.json

# Commit (CI will NOT do this for you)
git add docs/static/codebase_stats.json
git commit -m "data(stats): codebase statistics for vX.X.X"
git push

• codebase_stats.json current shows vX.X.X
• history includes a vX.X.X entry (no gap vs. previous release)
• Change is committed and pushed (not just regenerated locally)

Backfilling a missed gap: check out each missed tag in a throwaway git worktree, run the same counting logic, and merge the entries into history sorted by version. See the v0.15→0.24 backfill (June 2026) for the pattern.

2. Run Eval Baseline

🚨 CRITICAL: ALWAYS use --full for releases!

Correct workflow:

# ✅ RECOMMENDED for releases — adds 4-language Explorer data for ~$7 more
.claude/skills/post-release/scripts/run_eval_baseline.sh X.X.X --full --lang-harness

# Minimum acceptable for releases
.claude/skills/post-release/scripts/run_eval_baseline.sh X.X.X --full

This runs all 7 production models (extended_suite) with both AILANG and Python.

Tier scope for releases (counts re-centered after the v0.29.0 M-EVAL-FRONTIER-TIER re-tier: 7 saturated core benchmarks demoted to stretch, 8 stretch promoted to the new frontier tier):

• Default (release): --tier core,stretch,frontier — 56 benchmarks (19 core + 29 stretch + 8 frontier). This is the tier for every cloud/API model — never smoke. Smoke is the local OS-model iteration tier only (see the 🚫 note above); a cloud model added to a release baseline (e.g. a new Anthropic/OpenAI/Google model) goes straight to core,stretch,frontier.
• frontier (8): the anti-saturation discriminator tier — a frontier benchmark's defining property is that at least one frontier model FAILS it in standard mode; if every frontier model passes it, it demotes back to stretch (CURATION.md §5). Release baselines are the ONLY routine source of frontier-failure data (its authoring-time failure validation was parked as API-billed), so keep it in every release run — that data doubles as the check that the tier still discriminates.
• Dev/fast mode: --tier core — 19 benchmarks (Core is the headline metric)
• Full audit: --tier smoke,core,stretch,frontier — 79 benchmarks; only add smoke when you deliberately want the local sanity tier in the sweep, not for routine cloud baselines
• vision benchmarks are research-grade and excluded by default — opt in explicitly with --tier vision if you want to publish those numbers.

Override tier via the script's --tier flag (see run_eval_baseline.sh --help). If unsure, the default is tuned to produce a release-ready baseline in ~30–60 minutes.

Cost & time (default tier core,stretch,frontier = 56 benchmarks; ~1.5× the old 37-benchmark core,stretch figures — re-center after the first v0.29.0+ baseline):

Mode	Cost	Time	Use for
--full	~$24	~45-60 min	Standard release
--full --lang-harness	~$31	~60-80 min	Recommended — adds 4-lang Explorer data
--full --cross-harness	~$65	~60-80 min	Major releases (vX.0, quarterly)
dev (no flags)	~$5	~15-20 min	Quick validation only — never for releases

❌ WRONG workflow (what happened with v0.3.22):

# DON'T do this for releases!
.claude/skills/post-release/scripts/run_eval_baseline.sh X.X.X  # Missing --full!
# Then try to add production models later with --skip-existing
# Result: Confusion, multiple processes, incomplete baseline

If baseline times out or is interrupted:

# Resume with ALL 6 models (maintains --full semantics)
ailang eval-suite --full --langs python,ailang --parallel 5 \
  --output eval_results/baselines/X.X.X --skip-existing

The --skip-existing flag skips benchmarks that already have result files, allowing resumption of interrupted runs. But ONLY use this for recovery, not as a strategy to "add more models later".

3. Update Website Dashboard

Use the automation script:

.claude/skills/post-release/scripts/update_dashboard.sh X.X.X

IMPORTANT: This script automatically:

• Generates Docusaurus markdown (docs/docs/benchmarks/performance.md)
• Updates JSON with history preservation (docs/static/benchmarks/latest.json)
• Does NOT overwrite historical data - merges new version into existing history
• Validates JSON structure before writing
• Clears Docusaurus cache to prevent webpack errors

Test locally (optional but recommended):

cd docs && npm start
# Visit: http://localhost:3000/ailang/docs/benchmarks/performance

Verify:

• Timeline shows vX.X.X
• Success rate matches eval results
• No errors or warnings

Commit dashboard updates:

git add docs/docs/benchmarks/performance.md docs/static/benchmarks/latest.json
git commit -m "Update benchmark dashboard for vX.X.X"
git push

3a. Snapshot Local-Rig Longitudinal (M-EVAL-OS-LONGITUDINAL)

The local Ollama rig (opencode + pi + motoko on local qwen, via the os-rotation-filler) measures whether each AILANG release moves the needle for local models. Archive this release's local numbers into the version-trend history so the website can chart version-over-version evolution:

# Snapshot the rig's current numbers as this release, AND reset the active-model
# accumulator so the rotation re-measures fresh against the NEXT release.
# (Retired models — anything not matching ACTIVE_PATTERN, default qwen3-6 — stay
# frozen at their last version.)
tools/os-release-snapshot.sh vX.X.X --reset

git add docs/static/benchmarks/os/history.json docs/static/benchmarks/os/latest.json
git commit -m "Snapshot local-rig OS leaderboard for vX.X.X (longitudinal)"
git push

This appends a vX.X.X entry to docs/static/benchmarks/os/history.json (deduped by version) and clears the active-model rolling files. The website's local-rig trend chart reads os/history.json; the live table still reads os/latest.json. Run without --reset if you only want to refresh a version's numbers while they're still filling (safe, idempotent).

3b. Publish the UNIFIED Dashboard (cloud + local in one leaderboard)

The whole point of the on-device roster is comparison with cloud. Step 3 above publishes the cloud baseline into the main latest.json; this step folds the local rig's rotation results for the same release into that SAME leaderboard so the on-device models (qwen/gemma) appear alongside the cloud frontier in the main tables (ELO leaderboard, gap-trend, per-model-trend). It re-runs eval-report on the cloud baseline with --merge pointed at this release's rotation dir:

# Regenerates docs/static/benchmarks/latest.json with cloud + local UNIFIED.
# Auto-skips the merge if no rotation dir exists yet for this release
# (then it's just a cloud-only refresh, identical to step 3).
tools/publish-unified-dashboard.sh vX.X.X

git add docs/static/benchmarks/latest.json
git commit -m "Unify local rotation into main leaderboard for vX.X.X"
git push

Under the hood this is: ailang eval-report eval_results/baselines/vX.X.X vX.X.X --merge eval_results/rotation/os-rolling/vX.X.X --format=json (the wrapper adds --merge only when that rotation dir exists). Do not redirect its stdout — eval-report writes latest.json itself and preserves history. Verify ratings.agent.byLang.ailang.models now lists BOTH cloud (claude-*, opencode-or-*) and local (*-qwen*, *gemma*) models, while ratings.standard still has the cloud roster. Note the daily os-rotation-filler also runs this automatically once a release's cloud baseline exists, so this step mainly guarantees the unify happens at release.

4. Update Axiom Scorecard

Review and update the axiom scorecard:

# View current scorecard
ailang axioms

# The scorecard is at docs/static/benchmarks/axiom_scorecard.json
# Update scores if features were added/improved that affect axiom compliance

When to update scores:

• +1 → +2 if a partial implementation becomes complete
• New feature aligns with an axiom → update evidence
• Gaps were fixed → remove from gaps array
• Add to history array to track KPI over time

Update history entry:

{
  "version": "vX.X.X",
  "date": "YYYY-MM-DD",
  "score": 18,
  "maxScore": 24,
  "percentage": 75.0,
  "notes": "Added capability budgets (A9 +1)"
}

5. Extract Metrics for CHANGELOG

Generate metrics template:

.claude/skills/post-release/scripts/extract_changelog_metrics.sh X.X.X

This outputs a formatted template with:

• Overall success rate
• Standard eval metrics (0-shot, final with repair, repair effectiveness)
• Agent eval metrics by language
• Automatic comparison with previous version (no manual work!)

Update changelog automatically:

1. Run the script to generate the template
2. Insert the "Benchmark Results (M-EVAL)" section into the active changelog file in changelogs/ (find with ls changelogs/ | grep current)
3. Place it after the feature/fix sections and before the next version
4. Note: Root CHANGELOG.md is an index file — do NOT write entries there

For CHANGELOG template format, see resources/version_notes.md.

5a. Analyze Agent Evaluation Results

v0.8.0+ (chain-based - recommended):

# Find the chain ID from the latest eval run
ailang eval-chains list

# View per-benchmark pass/fail with cost and turns
ailang eval-chains view <chain-id>

# Pass rate breakdown
ailang eval-chains stats <chain-id>

# Show failures with error details
ailang eval-chains failures <chain-id>

# Generate chain-based report
ailang eval-report --from-chain <chain-id> X.X.X --format=json

Legacy (file-based):

# Get KPIs (turns, tokens, cost by language)
.claude/skills/eval-analyzer/scripts/agent_kpis.sh eval_results/baselines/X.X.X

Target metrics: Avg Turns ≤1.5x gap, Avg Tokens ≤2.0x gap vs Python.

For detailed agent analysis guide, see resources/version_notes.md.

5b. Tag-Based Analysis and Rotation Check (v0.14.0+)

After dashboard + changelog metrics, run the curation analysis primitives. These inform what to keep, demote, or promote for the next release — the suite is curated, not accumulated. See benchmarks/CURATION.md for the full philosophy.

# Tag coverage: which of the 12 canonical tags are thin or over-represented?
ailang eval-matrix eval_results/baselines/vX.X.X vX.X.X --by-tags

# AILANG-only wins: AILANG beats Python by ≥ 10pp — protect these from regressions
ailang eval-matrix eval_results/baselines/vX.X.X vX.X.X --ailang-wins

# Dual-mode saturation audit (RECOMMENDED for demotion decisions):
# Lists every benchmark's standard AND agent pass rate per language.
# Demote candidates require ≥95% on ALL 4 dimensions (std-AI, std-Py, agent-AI, agent-Py).
# Standard-only saturation is misleading: many benchmarks are 100/100 in standard
# but drop to 12-50% in agent mode — those are still valuable signal, KEEP IN CORE.
.claude/skills/benchmark-manager/scripts/audit_saturation.sh vX.X.X

# Built-in saturated check (uses topline successRate only — less reliable, prefer the script above):
ailang eval-matrix eval_results/baselines/vX.X.X vX.X.X --show-saturated

# Optional: compare this release's tag deltas against the previous baseline
ailang eval-report eval_results/baselines/vX.X.X vX.X.X --format=json

Record three things in the release notes (or the design doc retro):

• Demote candidates — saturated benchmarks that should move to stretch or be retired in the next sprint.
• Keep as value evidence — AILANG-only wins that prove the language's ROI.
• Thin tags — taxonomy gaps (<3 benchmarks in a tag) to target with new benchmarks.

This step is cheap (seconds), has no external dependencies, and produces the input for the next release's benchmark-manager / eval-gap-finder work.

6. Move Design Docs to Implemented

Step 1: Check for issues (duplicates, misplaced docs):

.claude/skills/post-release/scripts/cleanup_design_docs.sh X.X.X --check-only

Step 2: Preview all changes:

.claude/skills/post-release/scripts/cleanup_design_docs.sh X.X.X --dry-run

Step 3: Check any flagged docs:

• [DUPLICATE] - These will be deleted (already in implemented/)
• [MISPLACED] - These will be relocated to correct version folder
• [NEEDS REVIEW] - Update **Status**: to Implemented if done, or leave for next version

Step 4: Execute the cleanup:

.claude/skills/post-release/scripts/cleanup_design_docs.sh X.X.X

Step 5: Commit the changes:

git add design_docs/
git commit -m "docs: cleanup design docs for vX_Y_Z"

The script automatically:

• Deletes duplicates (same file already in implemented/)
• Relocates misplaced docs (Target: version doesn't match folder)
• Moves docs with "Status: Implemented" to implemented/
• Flags remaining docs for manual review

7. Update Public Documentation

• Update prompts/ with latest AILANG syntax (ailang prompt)
• Update prompts/devtools/ with latest toolchain docs (ailang devtools-prompt)
  • New CLI commands or flags should be added to the devtools prompt
  • Verify with: ailang devtools-prompt | grep "new-command"
• Update website docs (docs/) with latest features
• Remove outdated examples or references
• Add new examples to website
• Update docs/guides/evaluation/ if significant benchmark improvements
• Update docs/LIMITATIONS.md:
  • Remove limitations that were fixed in this release
  • Add new known limitations discovered during development/testing
  • Update workarounds if they changed
  • Update version numbers in "Since" and "Fixed in" fields
  • Test examples: Verify that limitations listed still exist and workarounds still work

    # Test examples from LIMITATIONS.md
    # Example: Test Y-combinator still fails (should fail)
    echo 'let Y = \f. (\x. f(x(x)))(\x. f(x(x))) in Y' | ailang repl
    
    # Example: Test named recursion works (should succeed)
    ailang run examples/factorial.ail
    
    # Example: Test polymorphic operator limitation (should panic with floats)
    # Create test file and verify behavior matches documentation
    

  • Commit changes: git add docs/LIMITATIONS.md && git commit -m "Update LIMITATIONS.md for vX.X.X"

8. Run Documentation Sync Check

Run docs-sync to verify website accuracy:

# Check version constants are correct
.claude/skills/docs-sync/scripts/check_versions.sh

# Audit design docs vs website claims
.claude/skills/docs-sync/scripts/audit_design_docs.sh

# Generate full sync report
.claude/skills/docs-sync/scripts/generate_report.sh

What docs-sync checks:

• Version constants in docs/src/constants/version.js match git tag
• Teaching prompt references point to latest version
• Architecture pages have PLANNED banners for unimplemented features
• Design docs status (planned vs implemented) matches website claims
• Examples referenced in website actually work

If issues found:

1. Update version.js if stale
2. Add PLANNED banners to theoretical feature pages
3. Move implemented features from roadmap to current sections
4. Fix broken example references

Commit docs-sync fixes:

git add docs/
git commit -m "docs: sync website with vX.X.X implementation"

See docs-sync skill for full documentation.

9. Verify μRAG Corpus Reindex (REQUIRED)

release-manager runs make brain-index-syntax-reset immediately after the tag pushes. This step verifies that the reset actually populated the brain with chunks tagged with the new release version — protects against silent indexer failures that would leave Claude pulling stale snippets.

Spot-check ≥5 chunks reference the active version:

EXPECTED_VERSION="$(ailang prompt --version-active)"
ailang cache search --namespace ailang-syntax --limit 5 "string" \
  | grep -c "version:${EXPECTED_VERSION}" \
  || { echo "FAIL: μRAG corpus does not reference $EXPECTED_VERSION"; exit 1; }

Quick stat check:

ailang cache stats | grep -E "ailang-(syntax|builtins|examples)"
# Expect: ailang-syntax >= 50, ailang-builtins >= 250, ailang-examples >= 100

Append a one-line audit to release notes (or eval_results/baselines/<version>/notes.md):

μRAG corpus reindex verified: <ailang-syntax count>, <ailang-builtins count>, <ailang-examples count> chunks @ <version>.

If verification fails:

• Re-run make brain-index-syntax-reset from the project root.
• Check ailang prompt --version-active returns the new release tag.
• If it returns nothing, the prompt directory wasn't published in the release — escalate to release-manager rather than masking the issue.

Resources

Post-Release Checklist

See resources/post_release_checklist.md for complete step-by-step checklist.

Prerequisites

• Release vX.X.X completed successfully
• Git tag vX.X.X exists
• GitHub release published with all binaries
• ailang binary installed (for eval baseline)
• Node.js/npm installed (for dashboard, optional)

Agent CLIs + API keys (for the agent baseline on this rig)

The agent baseline (agent_suite) routes each model to a CLI harness. To run all of them on the Studio rig, these must be installed and authenticated:

Harness (agent_cli)	Install	Auth	Used by
opencode	npm i -g opencode-ai	OPENROUTER_API_KEY	OS models (glm/minimax/deepseek) — reliable
claude	npm i -g @anthropic-ai/claude-code	logged-in Claude	sonnet anchor — ⚠️ can hang on long runs
codex	npm i -g @openai/codex	one-time: printenv OPENAI_API_KEY | codex login --with-api-key (env var alone gives 401 — codex defaults to ChatGPT-OAuth)	gpt5-4-mini
managed_agents	(none — Vertex API)	gcloud auth application-default login	gemini agent (no gemini CLI executor exists)
pi	npm i -g @mariozechner/pi-coding-agent	per-provider	optional minimal harness
motoko	go install …/motoko	OPENROUTER_API_KEY	⚠️ currently hangs — removed from agent_suite

API keys live in ~/.config/ailang/secrets.env (sourced from ~/.zshenv). Pull the cloud-managed ones with ~/.config/ailang/pull-secrets.sh (Anthropic/OpenAI/Google from Secret Manager). OpenRouter is the manual key. Gemini uses Vertex ADC, not the API key.

Verify everything resolves before a release run:

for c in opencode claude codex pi motoko; do which $c >/dev/null && echo "✓ $c" || echo "✗ $c"; done
# Note: NO gemini CLI — the @google/gemini-cli is deprecated/unused. Google agent mode
# goes through managed_agents (Vertex API via `gcloud auth application-default login`).
ailang eval-suite --agent --models agent_suite --benchmarks fizzbuzz --langs ailang --dry-run  # all should route, none "<none>"

Common Issues

Eval Baseline Times Out

Solution: Use --skip-existing flag to resume:

bin/ailang eval-suite --full --skip-existing --output eval_results/baselines/vX.X.X

Dashboard Shows "null" for Aggregates

Cause: Wrong JSON file (performance matrix vs dashboard JSON) Solution: Use update_dashboard.sh script, not manual file copying

Webpack/Cache Errors in Docusaurus

Cause: Stale build cache Solution: Run cd docs && npm run clear && rm -rf .docusaurus build

Dashboard Shows Old Version

Cause: Didn't run update_dashboard.sh with correct version Solution: Re-run update_dashboard.sh X.X.X with correct version

Progressive Disclosure

This skill loads information progressively:

1. Always loaded: This SKILL.md file (YAML frontmatter + workflow overview)
2. Execute as needed: Scripts in scripts/ directory (automation)
3. Load on demand: resources/post_release_checklist.md (detailed checklist)

Scripts execute without loading into context window, saving tokens while providing powerful automation.

Version Notes

For historical improvements and lessons learned, see resources/version_notes.md.

Key points:

• All scripts accept version with or without 'v' prefix
• Use --validate flag to check configuration before running
• Agent eval scopes benchmarks by tier (--tier core,stretch,frontier default) — benchmarks are resolved from benchmarks/*.yml by tier, not a hardcoded list
• Tag-based curation analysis runs via ailang eval-matrix --by-tags/--show-saturated/ --ailang-wins in Step 5b

Notes

• This skill follows Anthropic's Agent Skills specification (Oct 2025)
• Scripts handle 100% of automation (eval baseline, dashboard, metrics extraction, design docs)
• Can be run hours or even days after release
• Dashboard JSON preserves history - never overwrites historical data
• Always use --full flag for release baselines (all production models)
• Design docs cleanup now handles duplicates, misplaced docs, and status-based moves
  • --check-only to report issues without changes
  • --dry-run to preview all actions
  • --force to move all regardless of status