Agent skill

Crud Purge Stale Eval Placeholders

by open-thoughts in open-thoughts/OpenThoughts-Agent

Safely purge stale, never-populated sandboxjobs placeholder rows (eval launches that died/stalled before scoring) from the OT-Agent Supabase registry.

Apache-2.0Auto-check passed

Install Crud Purge Stale Eval Placeholders

skills CLI
$ npx skills add open-thoughts/OpenThoughts-Agent --skill crud-purge-stale-eval-placeholders -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install open-thoughts/OpenThoughts-Agent crud-purge-stale-eval-placeholders --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/open-thoughts/OpenThoughts-Agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/crud-purge-stale-eval-placeholders .claude/skills/crud-purge-stale-eval-placeholders && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
crud-purge-stale-eval-placeholders
GitHub stars
301
Token cost
~2.9k tokens
SKILL.md length
737 words
Files
1
Skills in repo
44
Repo updated
First seen
Licence
Apache-2.0

At a glance

Safely purge stale, never-populated sandboxjobs placeholder rows (eval launches that died/stalled before scoring) from the OT-Agent Supabase registry.

  • Works in 5 steps: Connect (run LOCALLY) → What makes a row "stale and removable" → Cross-user FK safety (MANDATORY pre-check) → …
  • The registry is clogged with dead placeholder eval rows
  • SKILL.md covers 0. Connect (run LOCALLY), The metrics field has TWO…, 1. What makes a row "stale and… and 2. Cross-user FK safety…, plus 4 more sections
  • Calls python; needs SUPABASE_SERVICE_ROLE_KEY

What it does

Crud Purge Stale Eval Placeholders is an agent skill from open-thoughts/OpenThoughts-Agent. Safely purge stale, never-populated sandboxjobs placeholder rows (eval launches that died/stalled before scoring) from the OT-Agent Supabase registry. Removes ONLY dead Pending/Started rows WE OWN that are 36h old with null metrics/stats/endedat, via the mandatory cross-user FK-safety pre-check + the REQUIRED grandchild→child→job cascade delete (sandboxtrialmodelusage → sandboxtrials → sandboxjobs). Other users' stale rows are REPORTED, never deleted. DRY-RUN first, then delete, then re-read. Use when the…

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Supabase. The repository describes itself as: Data recipes and robust infrastructure for training AI agents. The licence is Apache-2.0.

When your agent uses it

  • The registry is clogged with dead placeholder eval rows
  • A sweep flags stale Pending/Started/Running eval entries
  • The eval listeners dedup is mis-firing on dead rows

Example prompts

  • “Running”
  • “/crud-purge-stale-eval-placeholders”

Requirements

  • Python 3
  • A credential in SUPABASE_SERVICE_ROLE_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Connect (run LOCALLY)
  2. What makes a row "stale and removable"
  3. Cross-user FK safety (MANDATORY pre-check)
  4. The cascade — sandbox_jobs.id IS FK'd (REQUIRED)
  5. Procedure — DRY-RUN, review, delete, re-read

What it can do on your machine

Read from SKILL.md and the folder at commit 3bd1917. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • SUPABASE_SERVICE_ROLE_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Crud Purge Stale Eval Placeholders loads about 2.9k tokens when it runs. Until then it costs about 205 tokens; SKILL.md has 737 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~205
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from open-thoughts/OpenThoughts-Agent at commit 3bd1917, republished under its Apache-2.0 licence (© open-thoughts). 737 words, ~2,895 tokens.

Download SKILL.mdSave it as .claude/skills/crud-purge-stale-eval-placeholders/SKILL.md (or your agent's skills folder).
name
crud-purge-stale-eval-placeholders
description
Safely purge stale, never-populated `sandbox_jobs` placeholder rows (eval launches that died/stalled before scoring) from the OT-Agent Supabase registry. Removes ONLY dead `Pending`/`Started` rows WE OWN that are >36h old with null `metrics`/`stats`/`ended_at`, via the mandatory cross-user FK-safety pre-check + the REQUIRED grandchild→child→job cascade delete (`sandbox_trial_model_usage` → `sandbox_trials` → `sandbox_jobs`). Other users' stale rows are REPORTED, never deleted. DRY-RUN first, then delete, then re-read. Use when the registry is clogged with dead placeholder eval rows, when a sweep flags stale Pending/Started/"Running" eval entries, or when the eval listener's dedup is mis-firing on dead rows. The general CRUD/read/aggregation skill is `crud-otagent-supabase`.

crud-purge-stale-eval-placeholders

A guardrailed DELETE of dead placeholder sandbox_jobs rows left when an eval launch dies/stalls before it scores. Every launch creates a placeholder (Pending→Started) before a result exists; if the run dies, the placeholder remains (no Finished, no metrics, no stats), clogging the table and the eval listener's dedup. Removes only the dead placeholders we own.

This is a DELETE on a shared table. The cross-user FK-safety pre-check (§2) and the cascade (§3) are both MANDATORY — a plain sandbox_jobs delete FK-fails Postgres 23503, and skipping the ownership check can wipe another user's rows. DRY-RUN first, then delete, then re-read.

0. Connect (run LOCALLY)

Run from the Mac with the otagent env; source the local secrets (sets SUPABASE_URL + SUPABASE_SERVICE_ROLE_KEY).

bash
cd /Users/benjaminfeuer/Documents
set -a; source "${DC_AGENT_SECRET_ENV:?set DC_AGENT_SECRET_ENV to the secrets file first}"; set +a
/Users/benjaminfeuer/miniconda3/envs/otagent/bin/python - <<'PY'
import os
from supabase import create_client
c = create_client(os.environ["SUPABASE_URL"], os.environ["SUPABASE_SERVICE_ROLE_KEY"])
PY
  • SUPABASE_SERVICE_ROLE_KEY bypasses RLS (full read/write) — nothing stops you mutating other users' rows, which is why §2 is mandatory.
  • Schema DDL lives at /Users/benjaminfeuer/Documents/OpenThoughts-Agent/schema/ — read sandbox_jobs / sandbox_trials / sandbox_trial_model_usage when unsure of a column.

The metrics field has TWO shapes — always extract via the helper

metrics is jsonb and appears as either a list of {"name","value"} dicts or a plain dict. The qualifier (§1) tests metrics is None via this shape-robust helper — never via metrics["accuracy"] directly:

python
def get_metric(metrics, key="accuracy"):   # key: "accuracy" or "accuracy_stderr"
    if metrics is None: return None
    if isinstance(metrics, dict):           # {"accuracy": 0.25, ...}
        return metrics.get(key)
    if isinstance(metrics, list):           # [{"name":"accuracy","value":0.25}, ...]
        for e in metrics:
            if isinstance(e, dict):
                if e.get("name") == key:    return e.get("value")
                if e.get(key) is not None:  return e.get(key)
    return None

1. What makes a row "stale and removable"

Schema facts that drive the filter (verified against schema/sandbox_jobs):

  • Timestamps are created_at / started_at / ended_at / submitted_at — there is NO updated_at. Use created_at for absolute age, and started_at (set when the job leaves Pending) as the secondary recency gate.
  • n_trials is the PLANNED trial count from config, NOT progress — a brand-new placeholder already has n_trials=128. Do NOT read n_trials as a "populated" signal.
  • The real "never populated" signals are metrics IS NULL (no score) AND stats IS NULL (no per-trial progress). Empirically every Pending/Started row has BOTH null; a live job that had begun scoring would have a non-null stats. (ended_at is also always null for these.)
  • job_status enum: Pending / Started / Finished (+ failure states). There is no literal "Running" status — a stale "running" entry is a stale Started row.

A row qualifies for removal iff ALL hold:

  1. job_status IN ('Pending','Started') — never Finished/a failure state.
  2. metrics IS NULL (no real accuracy via get_metric) AND stats IS NULL (never populated).
  3. ended_at IS NULL (didn't terminate into a recorded result).
  4. Age > 36h: created_at ≥ 36h ago AND, if started_at is set, started_at ≥ 36h ago (whichever is more recent must still be older than 36h) — so a legitimately-RUNNING recent eval (Pending/Started but <36h) is EXCLUDED.
Show full SKILL.md (335 more words)Show less

2. Cross-user FK safety (MANDATORY pre-check)

Restrict every delete to rows you own; never delete another user's rows without authorization.

Default-scope to OUR rows (the eval/re-eval owners feuer1, bfeuer00, penfever, benjaminfeuer — all four are the operator's own accounts; matches the sibling crud-purge-below-gate-evals OURS). Stale rows owned by GENUINELY OTHER users (zhuang1, richard.zhuang, …) are REPORTED with counts, never deleted — surface them to the supervisor. The match and the job-delete are both scoped by username IN OURS so a scope error cannot leak across users.

3. The cascade — sandbox_jobs.id IS FK'd (REQUIRED)

sandbox_jobs.id IS FK'd by a child chain — a plain delete fails Postgres 23503 foreign-key-violation. The chain is:

sandbox_trial_model_usage.trial_id → sandbox_trials.id → sandbox_jobs.id
         (grandchild)                     (child)            (job)

To delete a sandbox_jobs row you MUST cascade grandchild → child → job: delete its sandbox_trial_model_usage rows, then its sandbox_trials rows, then the sandbox_jobs row. The children carry NO username — ownership is TRANSITIVE from the job, so once you've asserted you own the JOB (§2), the whole cascade is FK-safe and yours. Still NEVER delete a job (or its cascade) you don't own.

4. Procedure — DRY-RUN, review, delete, re-read

python
import os
from datetime import datetime, timezone, timedelta
from supabase import create_client
c = create_client(os.environ["SUPABASE_URL"], os.environ["SUPABASE_SERVICE_ROLE_KEY"])
NOW = datetime.now(timezone.utc); CUTOFF_H = 36
OURS = {"feuer1", "bfeuer00", "penfever", "benjaminfeuer"}   # the operator's eval/re-eval owners we may delete

def age_h(ts):                          # hours since an ISO ts (None -> None)
    return None if not ts else (NOW - datetime.fromisoformat(ts)).total_seconds()/3600

def qualifies(r):
    if r["job_status"] not in ("Pending", "Started"):       return False
    if get_metric(r["metrics"]) is not None:                return False   # has a real score
    if r["stats"] is not None:                              return False   # has progress -> not "never populated"
    if r["ended_at"] is not None:                           return False   # terminated into a result
    ca = age_h(r["created_at"]); sa = age_h(r["started_at"])
    recent = min(x for x in (ca, sa) if x is not None)      # most-recent activity
    return recent is not None and recent > CUTOFF_H         # older than 36h

rows = c.table("sandbox_jobs").select(
    "id,job_name,username,job_status,created_at,started_at,ended_at,n_trials,metrics,stats,model_id,benchmark_id"
).in_("job_status", ["Pending", "Started"]).execute().data
q = [r for r in rows if qualifies(r)]

# Safety assert: nothing we matched may carry a real score/progress (never guess-delete)
bad = [r for r in q if r["stats"] is not None or get_metric(r["metrics"]) is not None]
assert not bad, f"STOP: {len(bad)} matched rows have stats/metrics — ambiguous, surface to supervisor"

ours   = [r for r in q if r["username"] in OURS]
others = [r for r in q if r["username"] not in OURS]
from collections import Counter
bm = {b["id"]: b["name"] for b in c.table("benchmarks").select("id,name").execute().data}
mn = {m["id"]: m["name"] for m in c.table("models").select("id,name").execute().data}
print(f"QUALIFY total={len(q)}  OURS={len(ours)}  OTHERS(report-only)={len(others)}")
print("OURS by user:", Counter(r['username'] for r in ours))
print("OTHERS by user:", Counter(r['username'] for r in others))
for r in ours[:10]:                     # sample: id, user, model, benchmark, status, age
    print(f"  {r['id']} | {r['username']} | {mn.get(r['model_id'],'?')[:40]} | "
          f"{bm.get(r['benchmark_id'],'?')} | {r['job_status']} | {age_h(r['created_at']):.0f}h")

# --- DELETE (ours only, idempotent, scoped id + username) — run AFTER reviewing the dry-run ---
# ⚠️ CASCADE: sandbox_jobs.id IS FK'd — `sandbox_trials.job_id → sandbox_jobs.id` and
# `sandbox_trial_model_usage.trial_id → sandbox_trials.id`. A plain sandbox_jobs delete FK-fails (23503);
# delete grandchild → child → job. Children carry no username (ownership transitive from the job you own).
DELETE = False                          # flip to True to execute
if DELETE:
    for r in ours:
        trial_ids = [t["id"] for t in
                     c.table("sandbox_trials").select("id").eq("job_id", r["id"]).execute().data]
        for i in range(0, len(trial_ids), 200):       # chunk to keep the IN() lists sane
            chunk = trial_ids[i:i+200]
            if chunk:
                c.table("sandbox_trial_model_usage").delete().in_("trial_id", chunk).execute()  # grandchild
        c.table("sandbox_trials").delete().eq("job_id", r["id"]).execute()                       # child
        c.table("sandbox_jobs").delete().eq("id", r["id"]).eq("username", r["username"]) \
         .in_("job_status", ["Pending", "Started"]).execute()                                    # job (yours)
    # re-read: confirm gone + that NO Finished/scored row was touched
    left = c.table("sandbox_jobs").select("id").in_("id", [r["id"] for r in ours]).execute().data
    fin  = c.table("sandbox_jobs").select("id").eq("job_status", "Finished") \
            .in_("id", [r["id"] for r in ours]).execute().data
    assert not left, f"{len(left)} of ours survived"; assert not fin, "touched a Finished row!"
    print(f"DELETED {len(ours)} ours; OTHERS left for supervisor: {Counter(r['username'] for r in others)}")

Guardrails

  • Cross-user FK safety is MANDATORY. Scope both the match and the job-delete by username IN OURS. Other users' stale rows are reported with counts, never deleted.
  • The cascade is REQUIRED. sandbox_jobs.id IS FK'd; a plain delete fails 23503. Delete grandchild (sandbox_trial_model_usage) → child (sandbox_trials) → job, in that order.
  • DRY-RUN first, then delete, then re-read. Leave DELETE = False until you've reviewed the sample. After deleting, re-read to confirm the rows are gone AND that no Finished/scored row was touched (the post-delete assert).
  • Never guess-delete. STOP + surface if any qualifying row has a non-null stats/metrics (ambiguous "never populated").
  • Never raise CUTOFF_H — the 36h floor excludes legitimately-running recent evals.
  • n_trials is planned, not progress. A placeholder already has n_trials=128; do not read it as a populated signal.
  • Reads are free; deletes are dangerous. The service-role key bypasses RLS.
  • crud-otagent-supabase — the general Supabase read/aggregate/write skill (ID/OOD scores, model registration, the full get_metric/set_stat helpers). Reach for that one for anything other than the stale-placeholder purge.

© open-thoughts, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/crud-purge-stale-eval-placeholders of open-thoughts/OpenThoughts-Agent.

Open the folder on GitHubat commit 3bd1917

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Works with

Questions about Crud Purge Stale Eval Placeholders

What does Crud Purge Stale Eval Placeholders do?

Safely purge stale, never-populated sandboxjobs placeholder rows (eval launches that died/stalled before scoring) from the OT-Agent Supabase registry. Crud Purge Stale Eval Placeholders is an agent skill from open-thoughts/OpenThoughts-Agent. Safely purge stale, never-populated sandboxjobs placeholder rows (eval launches that died/stalled before scoring) from the OT-Agent Supabase registry.

When should I use Crud Purge Stale Eval Placeholders?

Crud Purge Stale Eval Placeholders fits situations like: the registry is clogged with dead placeholder eval rows; A sweep flags stale Pending/Started/Running eval entries; the eval listeners dedup is mis-firing on dead rows.

How do I install Crud Purge Stale Eval Placeholders in Claude Code?

Run `npx skills add open-thoughts/OpenThoughts-Agent --skill crud-purge-stale-eval-placeholders -a claude-code`. Or copy the skill folder (.agents/skills/crud-purge-stale-eval-placeholders in open-thoughts/OpenThoughts-Agent) into .claude/skills/crud-purge-stale-eval-placeholders in your project. Claude Code loads it when a task matches its description.

How do I install Crud Purge Stale Eval Placeholders in Codex?

Run `npx skills add open-thoughts/OpenThoughts-Agent --skill crud-purge-stale-eval-placeholders -a codex`. Or copy the skill folder (.agents/skills/crud-purge-stale-eval-placeholders in open-thoughts/OpenThoughts-Agent) into .agents/skills/crud-purge-stale-eval-placeholders in your project. Codex loads it when a task matches its description.

Can I use Crud Purge Stale Eval Placeholders in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add open-thoughts/OpenThoughts-Agent --skill crud-purge-stale-eval-placeholders -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/crud-purge-stale-eval-placeholders, .gemini/skills/crud-purge-stale-eval-placeholders, .github/skills/crud-purge-stale-eval-placeholders and .opencode/skills/crud-purge-stale-eval-placeholders in your project.

What does Crud Purge Stale Eval Placeholders need to run?

Going by SKILL.md and its folder, Crud Purge Stale Eval Placeholders needs the command-line tools its instructions call (python) and credentials named SUPABASE_SERVICE_ROLE_KEY. Our summary lists: Python 3; A credential in SUPABASE_SERVICE_ROLE_KEY.

Does Crud Purge Stale Eval Placeholders access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Crud Purge Stale Eval Placeholders safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Crud Purge Stale Eval Placeholders use?

Crud Purge Stale Eval Placeholders is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Crud Purge Stale Eval Placeholders use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Crud Purge Stale Eval Placeholders?

Skills that share tags, products or a category with Crud Purge Stale Eval Placeholders: Supabase Postgres Best Practices (supabase/agent-skills, 2.7k stars), Supabase Development and Debugging (supabase/agent-skills, 2.7k stars), Clickhouse Logs Queries (supabase/supabase, 111k stars) and Security Review (jewbetcha/opentrace, 116 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Crud Purge Stale Eval Placeholders?

open-thoughts (a GitHub organization) maintains it in open-thoughts/OpenThoughts-Agent, which has 301 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on September 28, 2026.

Source: open-thoughts/OpenThoughts-Agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.