> ## Documentation Index
> Fetch the complete documentation index at: https://docs.decimal.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Changelog

> Recent changes to the DecimalAI platform, SDK, and GitHub Action.

This page tracks notable changes to the **platform API**, **Python SDK**, and **`decimal-labs/regression-check` GitHub Action**. Dates use ISO 8601.

The platform follows a rolling release model — changes ship to `api.decimal.ai` continuously. The SDK and Action follow [Semantic Versioning](https://semver.org/) and are tagged on GitHub.

For breaking-change notices, subscribe to the GitHub release feeds:

* SDK: [https://github.com/decimal-labs/decimalai-python/releases](https://github.com/decimal-labs/decimalai-python/releases)
* Action: [https://github.com/decimal-labs/regression-check/releases](https://github.com/decimal-labs/regression-check/releases)

For what's coming next, see the **[Roadmap](/roadmap)**.

<Update label="2026-07-15" description="Eval simplification — one execution contract, --runs replaces pass^k">
  **skillevaluation** (`0.6.0`, schema rev 2 — [ADR-0007](/guides/skillevaluation))

  * 🗑️ **One execution contract.** The per-case `mode` enum (`single_shot`/`agentic`/`explore`/`conversation`), `user_goal`, `environment`, `simulator`, and `policy_check` are **removed** from `eval.yaml`. Every case is invoked once in a prepared workspace, may take multiple tool steps (`max_turns` caps them), and is graded on the whole trajectory by `expectations` (LLM judge) + `validators` (code). Policy-under-pressure skills are authored as **seeded-transcript** cases (the conversation-so-far is data in the prompt) instead of a scripted dialogue mode.
  * ♻️ **`--runs N` replaces per-case `trials` / pass^k.** Repetition is now a runner-level flag: the whole suite runs N times, uniformly, and results are averaged by **mean** — the headline's expected value no longer depends on the repeat count. The old pass^k AND-fold (a case passed only if all k rollouts passed) is retired.
  * The `should_trigger` boolean, its trigger-only grader exemption, and the `cases_skipped_trigger_only` disclosure stay; trigger *accuracy* scoring (menu-selection simulation) is now a hosted-runner feature.

  **Platform**

  * ✨ **`POST /skills/{skill}/benchmark/run` gained a `runs` query parameter** (1–10, default 1) — re-run the suite uniformly and average by mean.
  * The six removed per-case fields are **ignored, not rejected**, if an older client still sends them (no 422); existing suites keep working.

  **SDK** (`decimalai-python`, unreleased — next version)

  * ♻️ **`skills benchmark --trials` → `--runs N`** — a run-level parameter (does not modify your eval.yaml). `--trials` now exits with a clear redirect rather than silently changing behavior.
</Update>

<Update label="2026-07-07" description="Progressive disclosure — the load_skill tool">
  **Platform**

  * ✨ **Token-budgeted skill menus** — the routing menu is now budgeted (\~1,500 estimated tokens, max 30 rows) instead of a flat 100-row cap. `GET /skills/menu` responses gained `desc_tokens`, `rows_total`, `truncated`. `POST /skills/route` returns the full menu (`strategy: "full_menu"`) when your whole eligible menu fits the budget — and skips the embedding round-trip.
  * ✨ **Body endpoint guardrail** — `GET /skills/{skill_name}/body` accepts `max_chars` (server-side trim with an explicit marker; response gains `truncated` + `total_chars`) and `agent_name` (resolves the exact version that agent was offered, honoring pinned subscriptions). The `version` field is now the concrete version number (previously the literal string `"latest"`).
  * 🐛 **Subscribed skills are now loadable** — a skill subscribed from the public registry could appear in your menu while its body fetch returned 404. Body fetches now resolve through the same offer logic as the menu.
  * 🐛 **Visibility scoping in routing** — the menu only offers skills the calling user may see: `personal` skills to their creator, `workspace` skills to owning-workspace members (plus explicit shares). API-key calls keep org-wide behavior.
  * 🐛 **Trigger-eval fidelity** — Stage-M menu simulations no longer sample user-invoked skills as distractors (production menus never contain them), so `menu_selection_rate` grades on-distribution.

  **SDK** (`decimalai-python`, unreleased — next version)

  * ✨ **Native `load_skill(name)` tool** — on `openai_agents` and `pydantic_ai`, `install(enable_skill_loader=True)` auto-registers a tool the model calls to pull a surfaced skill's full body mid-turn. Budgeted: 3 bodies / \~6,000 estimated tokens per turn, 8 KB per body, 20s deadline; loads are recorded on the trace (`skills_loaded_by_agent`). Kill switch: `init(load_skill_tool=False)` or `DECIMALAI_LOAD_SKILL_TOOL=0`.
  * ✨ **Body guardrail on prompt injection** — `inject_skill_body=True` (all adapters) now trims and budgets injected bodies instead of injecting them un-trimmed.
</Update>

<Update label="2026-06-24" description="SDK 0.6.0 — experiments API removed">
  **SDK** (`decimalai-python` 0.6.0)

  * 🗑️ **Removed the experiments API.** The agent/dataset experiment runner (`experiment()`, `run_experiment()`, `compare_experiments()`, the offline `Eval()` helper) and the matching client methods backed `/api/v1/experiments`, which was never shipped and always returned 404. The endpoint has been formally retired. Use **regression-check** for pre-deploy A/B (`POST /api/v1/regression-check`), the **regression timeline** for post-deploy comparison, and **skill version compare** (`/api/v1/skills/analytics/compare`) for skill diffs.
</Update>

<Update label="2026-06-18" description="Webhooks, billing, and reliability">
  **Platform**

  * ✨ **HMAC webhook signing** — outbound webhooks now carry an `X-Decimal-Signature` header for verifiable delivery.
  * ✨ **Webhook retry + delivery log** — failed deliveries retry with exponential backoff, with a per-webhook delivery log in the UI.
  * ✨ **Stripe billing end-to-end** — checkout + customer portal wired through for self-serve plan upgrades.
  * ✨ **Anthropic in Playground** — the Claude provider now sits alongside OpenAI and Gemini in the prompt-testing playground.

  **SDK** (`decimalai-python`)

  * ✨ **`atexit` flush handler** — buffered traces flush on script exit, so short-lived scripts no longer lose traces silently.
</Update>

<Update label="2026-06-08" description="Demo sandbox + SkillScore v2">
  **SDK `0.4.0`** (`pip install decimalai`, requires Python 3.10+)

  * ✨ **One-command demo sandbox** — see both demos on seeded data in \~2 minutes, before instrumenting anything:
    * `decimalai demo regression` — seeds a v1→v2 agent change + trace corpus, runs the regression check, links straight to the impact report.
    * `decimalai demo skills` — seeds three skills with varied effectiveness, links to the ranked registry.
    * `decimalai demo reset` — removes all `[Demo] `-prefixed data; your own agents and skills are never touched.
  * ✨ `decimalai init` now surfaces the demo commands in its next-steps output.

  **Platform**

  * ✨ **SkillScore v2** — the registry score is now a **quality-only** composite (0–100): live eval pass rate + AI-judge quality, gated on sample size. Popularity and maintenance no longer affect the score. Skills under 10 activations/30d are *relegated* below scored skills in the default sort instead of hidden.
  * ✨ **Leaderboard axes**: Highest SkillScore (default) · Biggest lift (measured lift vs no-skill baseline) · Most Efficient (token savings) · Top live rating.

  **GitHub Action** (`decimal-labs/regression-check`)

  * ✨ **Honest behavioral nudge** — when a PR's diff contains a model change and `behavioral-check` is off, the impact comment now shows how many recorded calls *can* be verified and how (`behavioral-check: real` or post-deploy bisect). No fabricated counts.
  * 🔧 `behavioral-check: mock` no longer renders a meaningless equivalent/changed split (the mock stub always read \~100% changed); it now reports the eligible-call count and points at `real`.
</Update>

<Update label="2026-05-20" description="Skills wedge release">
  The skill registry layer that knows what works — registry, router, and observability shipped as one product.

  **Registry**

  * ✨ **Per-model effectiveness** on every registry skill — see the pass rate a skill gets on GPT-5 vs Claude Opus vs Gemini Flash, computed from production traces. "Best with" badge marks the highest-passing model.
  * ✨ **Real "Most Effective" sort** ranks by SkillScore (with a minimum-activations gate so cold-start skills don't dominate). New separate `sort=popular` for raw activation count.
  * ✨ **Activation sparkline** on every public skill page — 30-day daily trend, server-rendered SVG, zero JS.
  * ✨ **Version diff viewer** lets unauthenticated visitors compare any two published versions side-by-side.
  * ✨ **Popular forks** surfaced on detail pages so consumers can find community-iterated variants.
  * ✨ **Integration snippets** (Python SDK · `pull` · curl · agent-runtime paths) on every detail page with copy-to-clipboard.
  * ✨ **25 new flagship official skills** authored — code review, API design, data/SQL, prompt engineering, agent design, ops, docs, security. All Apache-2.0.
  * 🔧 Default browse view hides bulk-imported skills with under 10 activations so the registry feels curated. Use the **Imported** tab or search to see all 3,000+ imports.

  **Router** ([new docs](/api-reference/skills/router))

  * ✨ The `SkillRouter` is now a first-class product surface with its own page in the API reference. Documents the three strategies (full menu / smart route / on-demand body), response shape, telemetry, policy controls, and smart-routing internals.

  **Observability**

  * ✨ **Weekly skill degradation digest** — opt-in email per workspace when a skill's pass rate drops ≥15% week-over-week with at least 20 baseline activations. Thresholds tunable via env vars.

  **Share & embed**

  * ✨ **`/skills/<slug>`** is the new canonical public URL for a registry skill. Legacy `/skills/<slug>` continues to work; both share the same OG image.
  * ✨ **OpenGraph cards** dynamically rendered per skill — name, SkillScore, per-model row, activation count. Twitter, LinkedIn, and Slack unfurls show the effectiveness data on every share.
  * ✨ **Embed widget** at `/embed/skills/<slug>` — drop a 380×180px iframe into a README or blog post showing live effectiveness. Light + dark theme via `?theme=`.

  **CLI**

  * ✨ **`decimalai skills pull <slug>`** — pull any public registry skill to disk with no signup. Writes `./<slug>/SKILL.md`. Read-only (no fork, no telemetry); signup is only required to install + activate tracking.

  **New public registry endpoints**

  * `GET /api/v1/registry/skills/{id}/activations` — daily activation series for the sparkline.
  * `GET /api/v1/registry/skills/{id}/versions/{version_number}` — body markdown for any published version (powers the public diff viewer).
  * `GET /api/v1/registry/skills/{id}/lineage` already existed; now surfaced on the public detail page as "Popular community forks".
</Update>

<Update label="2026-05" description="Phase 2 release">
  **Platform**

  * ✨ Skills lifecycle is generally available: create, version, fork, subscribe, publish to registry, analytics.
  * ✨ Public skills registry (`/skills`) with SkillScore effectiveness ranking (Quality / Popularity / Maintenance).
  * ✨ Prompt Testing playground promoted from internal tool to first-class feature (`/playground`), with BYOK support for OpenAI and Gemini.
  * ✨ Multi-agent topology graph + per-sub-agent compatibility dashboard.
  * ✨ Workspace CRUD + RBAC role model in place (enforcement landing in next release; see rollout plan D-4).
  * 🔧 Manifest registration is idempotent by hash — repeated `POST /manifests` returns existing IDs.

  **SDK** (`decimalai-python`)

  * ✨ `decimalai.init(langchain=True | openai_agents=True | llamaindex=True | crewai=True | autogen=True | otel=True)` covers 6+ frameworks.
  * ✨ Skill auto-discovery from `.claude/skills/`, `.agents/skills/`.
  * ✨ Bidirectional skill sync (`POST /skills/sync` + `SkillRouter.pull_missing()`).
  * ✨ `@decimalai.trace()` decorator for any Python function.

  **GitHub Action** (`decimal-labs/regression-check`)

  * ✨ Initial release. Computes structural diff between PR manifest and production manifest; posts impact report as a PR comment.
  * ✨ `manifest_only` SDK mode for CI: runs manifest extraction without invoking the agent.
</Update>

<Update label="2026-04" description="Phase 1 release">
  **Platform**

  * ✨ Hero workflow: manifest change → batch compatibility re-score → Impact Report banner → Auto-Repair + Build Dataset stepper → JSONL export.
  * ✨ Training Data Health dashboard at `/` (health ring, category bars).
  * ✨ Drift detection toast + sidebar compat badges.

  **SDK**

  * ✨ First public version. Manifest capture, trace ingest, framework adapters.
</Update>