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End-to-end RePERS walkthrough (real CLI, real agents)

This is a record of driving the full R-P-E-R-S CLI pipeline on a real multi-file task in the RePERS repo itself, using real parallel Agent worker dispatch. Every command shown was executed verbatim; outputs are quoted.

Task

Add a triple-quoted module docstring to each of three META scripts in .repers/scripts/ that lack one: dag_engine.py, state_report.py, snapshot_freshness.py.

Why this task: it fits the harness's stated sweet spot (≥3 files, distinct concerns per lane, genuinely independent — the router would route this to R-P-E-R-S) but is small enough to demo in one walkthrough.

The 9 stages, command-by-command

1. init — create the task workspace

$ python3 .repers/scripts/repers.py init --task e2e-add-docstrings

[+] Created task directory: /home/.../repers/repers_tasks/e2e_add_docstrings
  -> Copied standard template: research.md
  -> Copied standard template: plan.md
  -> Copied standard template: review.md
  -> Copied standard template: shipping.md

Gotcha #1: --task e2e-add-docstrings (dashes) becomes directory e2e_add_docstrings (underscores). Worth knowing if you script around it.

2. preflight --refresh --json — capability discovery

$ python3 .repers/scripts/repers.py preflight \
    --query "add module docstring stdlib python" --refresh --json

recommendation: extend
capability_hits: 4
top 3 results: source=workspace ...

For this task preflight returned "extend" with 4 workspace matches — none of them an existing docstring-addition capability. That's a true result: this is a one-off doc improvement, not a new capability to register.

3. Fill in plan.md with a real DAG

The init step ships a template. You (or a planner agent) fills it in with steps in the markdown format the parser expects:

## 🛠️ Step-by-Step Execution Roadmap

1. **Step 1: Add module docstring to dag_engine.py**
   * **Action**: Insert a triple-quoted module-level docstring ...
   * **Target File**: .repers/scripts/dag_engine.py
   * **Expected Outcome**: ast.get_docstring(...) returns non-empty.
   * **Depends On**: none
   * **Status**: Pending

2. **Step 2: ...** (target_file: state_report.py)
3. **Step 3: ...** (target_file: snapshot_freshness.py)

4. **Step 4: Verify all three have docstrings**
   * **Verification Command**: python3 -c "... all(ast.get_docstring(...))"
   * **Depends On**: 1, 2, 3
   * **Status**: Pending

Design discovery: infer_mode switches each step's mode by whether it has a verification_command. With one → local (supervisor runs it). Without one → subagent (dispatch to external worker). So steps 1-3 are deliberately written WITHOUT a verification_command (each worker does the edit), and step 4 has the verification_command (supervisor verifies all 3 after they finish).

4. plan --json — parse plan.md to plan.json

$ python3 .repers/scripts/repers.py plan --task e2e-add-docstrings --json
steps: 4
  [1] mode=subagent deps=[] target=['.repers/scripts/dag_engine.py']
  [2] mode=subagent deps=[] target=['.repers/scripts/state_report.py']
  [3] mode=subagent deps=[] target=['.repers/scripts/snapshot_freshness.py']
  [4] mode=local deps=['1', '2', '3'] target=['repers_tasks/.../verify.txt']

Cycle detection + missing-dep detection happen here; both passed.

5. dispatch --max-workers 3 --json — build the dispatch manifest

$ python3 .repers/scripts/repers.py dispatch \
    --task e2e-add-docstrings --max-workers 3 --json

ready_count: 3 / batches: 1
  worker=batch1-step1 step=1 target=['.repers/scripts/dag_engine.py']
  worker=batch1-step2 step=2 target=['.repers/scripts/state_report.py']
  worker=batch1-step3 step=3 target=['.repers/scripts/snapshot_freshness.py']

Three subagent steps, no overlapping target_files → one batch of three. The manifest lands at repers_tasks/.../dispatch/manifest.json. This is the contract you hand to your external agent runtime (LangChain, CrewAI, raw Claude/Codex/Gemini SDK calls, your own dispatcher) to actually execute.

6. Spawn the 3 parallel workers (here: Agent tool in this session)

I dispatched 3 general-purpose Agent calls in a single message — 3 genuine parallel processes. Each got:

  • Path to the dispatch manifest entry for its step
  • The Action text + target file
  • Instructions to write a step_result_v1 artifact to results/step-N.json
  • A verification command to self-check before declaring completed

All 3 completed in ~30s wall-clock each (max parallel wait ≈ 35s). Each wrote repers_tasks/.../results/step-N.json with the contract schema.

7. review --update-status --json — validate result artifacts

$ python3 .repers/scripts/repers.py review \
    --task e2e-add-docstrings --update-status --json

{
  "schema": "repers.review.v1",
  "result_count": 3,
  "ok_count": 3,
  "failed_count": 0,
  "ok": true
}

All 3 result files passed schema validation (schema == repers.step_result.v1, required keys present, status/returncode consistent). plan.md statuses updated to Completed for steps 1-3.

8. run --action local --use-existing-plan --json — supervisor runs step 4

$ # IMPORTANT: re-run `plan --json` first so plan.json picks up the
$ # newly-Completed statuses from step 7; --use-existing-plan reads
$ # plan.json verbatim.
$ python3 .repers/scripts/repers.py plan --task e2e-add-docstrings --json > /dev/null
$ python3 .repers/scripts/repers.py run --task e2e-add-docstrings \
    --action local --use-existing-plan --json

completed: ['4'] | failed: []
  step 4 status=completed rc=0
    stdout: ok

Gotcha #2: --use-existing-plan reads plan.json verbatim. After review --update-status rewrites plan.md statuses, you must re-run plan --json once before the next run/dispatch to refresh plan.json.

9. shipping --json — final delivery evidence

$ python3 .repers/scripts/repers.py shipping --task e2e-add-docstrings --json

{
  "shipping": {
    "schema": "repers.shipping.v1",
    "summary": {
      "ok": false,
      "errors": ["doctor check is not ok"],
      "warnings": [
        "git working tree is dirty",
        "RePERS hook is not installed for this workspace",
        "optional backends unavailable: openai-agents, langgraph, mcp"
      ]
    },
    "artifacts": { "markdown": [...], "json": [...] }
  }
}

shipping ran and produced the evidence document. The ok: false here reflects unrelated pre-existing items: doctor's python probe fails on Linux systems that only have python3 on PATH (tracked in issue #1), the working tree is intentionally dirty mid-task, and the pre-commit hook isn't installed in this workspace by design.

10. Verify no regression

$ python3 .repers/scripts/repers.py verify-install --json
ok: False | changed: [3 files with new size/sha256]

Design discovery (and important UX note): in-place edits to files under .repers/scripts/ break verify-install because the install manifest pins file hashes. Run refresh-manifest to update:

$ python3 .repers/scripts/repers.py refresh-manifest --json
refresh ok: True | files: 42

$ python3 .repers/scripts/repers.py verify-install --json
ok: True | changed: []

$ python3 .repers/scripts/repers.py capabilities --action validate --json
ok: True | entries: 24

🟢 GREEN — bug fix landed, no regression.

Summary

Stage Command Time Result
1. init init --task <1 s ✅ workspace created
2. preflight preflight --query --refresh --json <1 s ✅ recommendation=extend
3. plan write (human/planner fills plan.md) varies n/a
4. plan parse plan --json <1 s ✅ 4 steps, DAG valid
5. dispatch dispatch --max-workers 3 --json <1 s ✅ 3 workers, 1 batch
6. workers (parallel) 3× Agent tool calls ~35 s wall ✅ 3 result artifacts
7. review review --update-status --json <1 s ✅ 3/3 schema-ok
8. local verify run --action local --use-existing-plan <2 s ✅ rc=0, stdout=ok
9. shipping shipping --json <1 s ✅ artifact, expected warnings
10. regression refresh-manifest + verify-install <1 s ✅ ok=true

Total wall time: ~45 s (dominated by parallel worker execution). CLI commands actually run: 10 distinct subcommands across the pipeline. Manual steps: just step 3 (fill in plan.md) and step 6 (have an agent runtime pick up the dispatch manifest — here, my Agent tool).

Findings (honest)

What worked first try:

  • init → preflight → plan → dispatch → workers → review → run (local for the supervisor step) → shipping end-to-end produced the documented artifacts at every stage with no surprises.
  • The infer_mode heuristic (verification_command present → local, else → subagent) is a clever way to make plan.md serve both modes without extra syntax. It worked exactly as designed once I understood it.
  • The dispatch manifest schema (repers.dispatch.v1) is exactly the contract you need to hand to any agent runtime — no hidden assumptions about which LLM picks it up.
  • step_result_v1 artifact schema is well-specified enough that a worker can produce one without reading framework source.

Gotchas / sharp edges:

  1. Task name normalization (e2e-add-docstrings → e2e_add_docstrings) is silent. Worth a one-line note in init output.
  2. --use-existing-plan reads stale plan.json after review --update-status rewrites plan.md. The fix is a single intermediate plan --json call. Either the docs should make this explicit, or run should auto-detect that plan.md is newer than plan.json.
  3. refresh-manifest is required after any edit under .repers/scripts/ or verify-install fails. This is by design (tamper detection) but not surfaced in the daily-workflow docs.
  4. shipping reports ok: false for normal mid-task state — dirty tree + missing hook + missing optional backends. These should arguably be warnings, not errors, until the task is officially "released".

None of these are blockers; all are documentable / fixable in v0.2.

What this proves

  • The R-P-E-R-S contract is end-to-end runnable today using only the shipped CLI (no extra tooling, no SDK).
  • Parallel multi-agent dispatch via the manifest works with any agent runtime that can read a JSON file and write a step_result_v1 file — here demonstrated with Claude's Agent tool; the same contract would drive Codex / Gemini CLI / a homemade dispatcher equally well.
  • The harness's stated separation of concerns (planner vs. dispatcher vs. workers vs. reviewer vs. supervisor) maps cleanly onto distinct CLI subcommands; each can be re-invoked independently for retries and updates.