Your agents worked all night. Here's exactly what changed.
Reelier records the run that worked, freezes it as a replayable skill, and replays it deterministically — every run comes back as a receipt: proof of what the agent did and what changed because of it. Agents make claims. Reelier writes receipts.
Agent-authored PRs (Dependabot, Claude, Codex, Cursor, …) get a receipt comment in seconds: author, files changed, declared scope vs. what actually changed, sensitive paths flagged. No workflow file, no CLI, no config.
→ Install the Reelier receipts GitHub App — free on public repos, forever.
Reelier receipt — agent PR Author:
dependabot[bot]· Files changed: 2 (+119 −41) Declared scope: none (add.reelier/scope.ymlto enable unexpected-write detection) Sensitive paths touched: ⚠ 1 —package-lock.jsonProves scope and change, not correctness
A real receipt from Reelier's own repos — see one live. Declare scope per agent in .reelier/scope.yml (or a reelier-scope block in the PR body) and the receipt reports unexpected writes. The receipt proves scope and change, never correctness or safety.
AI agents are non-deterministic — the same prompt, a different result every run — and they'll claim they did the work whether they did or not. Reelier records the run that worked, replays it deterministically, and writes a signed receipt that proves it. Point it at your existing CI in one workflow — it adds a verifiable receipt, it doesn't replace your stack.
Measured on a real head-to-head benchmark, same task, same data (full method):
- 1,000 / 1,000 replays byte-identical
- Every replay ships a signed receipt — proof of what ran and what changed, never a claim
- 0 LLM calls at replay — deterministic re-execution, not re-reasoning
Deterministic replay is also ~50× cheaper and ~59× faster than re-running the agent, on the same benchmark.
npm i -g reelier && reelier init# No Node install needed — same commands via Docker:
docker run --rm ghcr.io/seldonframe/reelier --helpreelier init [--dry-run] performs one checkpointed local inspection across all three Reelier paths: Path A observation coverage, Path B replay/freeze candidates, and Path C boundable/outcome-capable/shadow-only/unsupported connections and candidates. It does not deploy, gate, dispatch, upload, copy credentials, or rewrite host configuration. --dry-run writes nothing; the normal command writes only sanitized artifacts below .reelier/init/.
Teach your coding agent when to reach for Reelier. Same two commands, either host:
claude plugin marketplace add seldonframe/reelier
claude plugin install reelier@seldonframecodex plugin marketplace add seldonframe/reelier
codex plugin add reelier@seldonframeThis installs two Agent Skills and nothing else. reelier-replay teaches your agent to freeze a
repeatable tool-call job and replay it at 0 tokens. reelier-write-safety covers bounding an
agent's writes before you grant them: what the recorder sees, what a policy refuses, and what a
receipt does and does not prove. It ships no MCP servers, so it does not wrap, observe, or gate
any tool call on its own; the reelier CLI does that, and the skills drive it via npx. Packaged in both the Agent Plugins v1.0.0 format (plugin/agent-plugins/) and the Claude Code format (plugin/claude/), generated from one source by scripts/build-plugin-packages.mjs.
Verified end to end on codex-cli 0.147.0-alpha.1.2: both formats install, enable, and the skill reaches the model. Other hosts are untested, and per-host status is tracked in docs/specs/agent-plugins-coverage-v1.md §4 rather than claimed here.
reelier init --dry-run # inspect Path A/B/C locally; write nothing
reelier init # persist resumable sanitized inspection artifacts
reelier run <name>.skill.md # replay deterministically — 0 tokens (read-only by default)
reelier diff <name> # SAME or DRIFTED, per step — exit 1 on drift
reelier push <name>.skill.md # sync receipts to your ledger (opt-in)
reelier ci # write a workflow: drift-CI + PR receipts, one command- Inspect, then record or freeze.
reelier initreveals observed coverage and local candidates without changing routes.reelier mcp --wrap "<mcp server>"proxies live tools;reelier scan/from-sessionfreezes supported history. - Compile.
reelier compileturns a trace into aSKILL.md— 0 LLM calls, minimal assertions, honest gaps printed as Open questions. - Replay.
reelier runreplays it at Level 0 — no LLM, byte-identical, read-only by default (writes need--allow-writes). - Diff.
reelier diffreports SAME or DRIFTED per step, with the failing assertion as the why — exit 1 on drift. - Log in.
reelier loginconnects this machine to Reelier Cloud with a device code in your browser — or setREELIER_CLOUD_URL/REELIER_CLOUD_KEYfor CI and self-hosting. - Push. Every run is a receipt;
reelier pushoptionally syncs it to a ledger for a permalink and an embeddable verified-replay badge.
Already have an Agent Skill? Convert it — your skill, minus the model:
reelier compile trace.jsonl --from-skill ./my-skill/SKILL.md| Test | Command | Answers |
|---|---|---|
| Determinism | reelier run <skill.md> |
Does this still do what it did? |
| Recovery | reelier run <skill.md> --fail N |
If this broke, would the skill notice and heal? |
| Drift | reelier run <skill.md> --wrap "<your mcp server>" |
Has the world moved out from under this skill? |
Taxonomy due to Mads Hansen's review of the launch post. Full semantics for each test, including recovery injection and manifest guardrails: docs/REFERENCE.md.
Dependabot and Renovate open the PR and run your test suite — but neither knows what your agent actually does at runtime, so a dependency bump that silently changes a tool call's shape (a renamed field, a new default, a different error) sails through with green unit tests. This is the check they don't run.
Copy .github/workflows/reelier-bump-check.yml into your repo, point skill: at your own recorded .skill.md file(s), and it will: gate to PRs from dependabot[bot]/renovate[bot] (or a dependencies label), install the bumped dependency, replay your recorded skill live against it at --max-level 0 (0 tokens), and fail the check on the exact step that drifted.
This tests dependency and MCP-tool-call behavior — it does not test model upgrades; --max-level 0 never calls an LLM. Full listing copy and setup: docs/marketplace-listing.md.
A pushed receipt carries a ladder of independently-verifiable claims — not one blanket "verified." Depending on what you turn on, it can be signed, timestamped, CI-attested, and carry cross-checkable provider request-ids. reelier verify recomputes every claim offline, and a claim you haven't enabled just renders as an honest gap, never a shamed one.
See a real one: reelier.com/r/HWBdmGob9KeHRqXi-OEaRD0z.
Full 8-rung ladder, what each rung does and doesn't prove: docs/REFERENCE.md.
| Employee lifecycle | Reelier equivalent |
|---|---|
| Skillify a session | reelier from-session |
| Performance review | reelier run + reelier diff |
| Fleet maintenance | scheduled replays + drift alerts |
| The record | signed receipts |
"Verified" describes the record, never the agent — a receipt proves what ran and what changed, not that the agent was good at its job.
An employment contract doesn't make an employee good — it makes what they did visible and bounded. Same here: receipts prove scope and change, never correctness.
- Solo dev / OSS maintainer — a real regression test again; drift can't pass silently.
- Team shipping agent changes — "it ran clean" becomes a checkable PR artifact, not a claim.
- Agency running agents for clients — signed, timestamped proof-of-delivery a client can verify.
- Marketplace buyer or seller — corroborated receipts are reviews that can't be astroturfed.
- Audit-facing ops — a signed, CI-attested trail of every write, idempotency key included.
MIT, free forever (versions ≤0.16.0 remain AGPL-3.0). Your data — skills, traces, runs — is specified in SPEC.md, so leaving is copying a folder.
Contributing: issues and PRs welcome — SPEC.md is the source of truth for formats; fix the code, not the spec. npm test before a PR.
If Reelier saved you a re-run, star it ⭐ — it's how other builders find it.