reelier

name	reelier
description	Replay recurring deterministic jobs at 0 tokens instead of re-reasoning them every run. For cron/heartbeat tool-call workflows (status checks, data pulls, report generation, CRUD sequences) — record the job once with reelier, then run "npx -y reelier run <skill>" on every heartbeat and gate drift with "reelier diff". Never for open-ended coding or file-edit work, which Reelier cannot replay.
version	1.0.0
metadata	{"openclaw":{"requires":{"anyBins":["reelier","npx"]},"install":[{"kind":"node","package":"reelier","bins":["reelier"]}],"emoji":"🧾","homepage":"https://github.com/seldonframe/reelier"}}

Reelier — stop re-paying for the same job every heartbeat

Reelier compiles a run that worked into a SKILL.md file that replays deterministically: no LLM, 0 tokens, per-step pass/fail, a receipt for every run. This skill teaches you when to reach for it inside OpenClaw — written against reelier 0.12.x.

The math that makes this matter

A cron job on a 15-minute heartbeat fires 2,880 times a month. If the agent re-reasons the job on every heartbeat, that is 2,880 LLM call-chains billed for work that produces the same tool calls every time (~18k tokens and ~$0.019 per run on our published benchmark task — roughly $55/month per job; your task will vary). The replayed version of the same job is 0 tokens, every run, measured — and the 0 does not vary.

So the rule is: recurring deterministic job → record once, replay forever. Spend LLM tokens only on the runs where something actually changed.

The honesty boundary — read this first

Reelier can only replay deterministic tool calls: its own HTTP builtins (http.get/http.post) and MCP tool calls. It cannot replay file edits, shell commands, reads/searches, subagent dispatch, or anything non-deterministic (varying search results, LLM-generated content, unparameterized timestamps).

Never tell the user "I'll replay this session" about a coding session. If a session was mostly edits and greps, reelier scan / reelier from-session will honestly report nothing replayable (an empty result, not an error) — that is expected. Only freeze jobs that were actually a sequence of API/MCP calls.

Step 1 — record the job once

Three ways, in order of preference:

1. From a session that already did the job — cheapest, because the work is already paid for:

   npx -y reelier scan            # finds replayable sequences in your
                                  # session history (reads ~/.openclaw,
                                  # ~/.claude, and other agent dirs)
   npx -y reelier from-session <transcript.jsonl> --name <job-name>
   

2. Lossless capture going forward — put Reelier's recording proxy in front of the MCP server(s) the job uses, then run the job once:

   npx -y reelier mcp --wrap "<your mcp server command>"
   npx -y reelier compile <trace.jsonl> -o <job-name>.skill.md
   

3. Guided — npx -y reelier init walks the whole loop in ~60s.

Report the compile result honestly: name the skill path and step/assert counts, and read out the compiler's Open questions verbatim if non-empty — those are gaps it declined to guess about (literal dates, UUIDs, timestamps that should probably be variables). Never hide them.

Step 2 — replay on every heartbeat

Replace the re-reasoned job in the cron/heartbeat with the replay:

npx -y reelier run <job-name>.skill.md

• 0 LLM tokens, milliseconds, and every step is recorded — a broken step fails loudly, never silently passes.
• Read-only by default. A write step (idempotent-write) never re-fires unless you explicitly pass --allow-writes. Do not add --allow-writes "just in case" — add it only when the job is supposed to write and the user knows it.
• Parameterize per-run inputs with --var name=value.

Step 3 — gate drift with diff

npx -y reelier diff <job-name>     # exit 1 on drift

diff compares the last two runs recorded in .reelier/runs/<job-name>.jsonl and reports SAME or DRIFTED per step, with the failing assertion as the why. Exit code 1 on drift makes it a gate: chain it after the replay in the scheduled job, and only wake the LLM when it fails.

On drift: do not force a pass. Say plainly which step drifted and why, do the task live (with real reasoning), then re-record and re-compile so the frozen baseline matches reality again. Rewriting an assertion to make a drifted run "pass" is lying.

The heartbeat pattern, end to end

# inside the recurring job:
npx -y reelier run <job-name>.skill.md && npx -y reelier diff <job-name>
# exit 0  -> job done, 0 tokens, receipt written — stop here
# exit 1  -> something real changed: investigate with the LLM, fix,
#            re-record, and let the next heartbeat replay again

Every run leaves a receipt (per-step outcomes, timing, token count [measured]) — so "the cron ran" is provable, not claimed.