agentsclimarketplace

Sweep

Skill ronniepinnell/casper/skills/sweep

Massive multi-agent audit sweep — the generalized FAB-project pattern. Fans out parallel domain auditors over a whole system (codebase, data stack, spec tree, product surface), adversarially verifies findings, and converges to a graded report where every finding lands as a ticket, gate, or patch. Use for "audit everything", pre-launch reviews, post-chaos reconciliation, or grading a stack against best-in-class.From its SKILL.md

Install
npx -y skills add ronniepinnell/casper --skill sweep

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

5.5 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

/sweep — The Full-System Audit Fan-Out

The pattern behind every high-yield mega-audit (21-agent data audits, FAB-style architecture maps, exit dossiers): decompose into dimensions → independent parallel auditors → adversarial verification → graded synthesis → every finding becomes a mechanism. One agent can't hold a system; a swept fan-out can. This skill is the repeatable harness.

Siblings (if your workflow has them): a per-object forensic review skill goes deep on ONE planning object against its claims; a cleanup skill is fix-first reconciliation. /sweep is breadth-first discovery across a whole system — it FEEDS both.

Invocation

/sweep data stack --grade            # full-stack audit, scored vs best-in-class
/sweep ui/dashboard                  # product-surface sweep
/sweep docs/specs vs src --drift     # spec-vs-reality sweep (mass /drift)
/sweep security                      # one dimension, full depth

Phase 0 — Scope & dimension slate (inline, before any fan-out)

  1. Fix the boundary: what's IN the sweep (paths, schemas, surfaces) and what's explicitly OUT. An unbounded sweep never converges.
  2. Pick dimensions — default slate, prune/extend per scope:
    • correctness (does it do what it claims) · spec-drift (mass /drift)
    • security/permissions · performance/scale · data quality
    • test coverage (real tests, not shells) · architecture/placement (/altitude violations, duplicated owners) · honesty (claimed-done vs actually-done — the completion-audit dimension) · UX/product (if user-facing)
  3. Declare the sweep's own gates (/gate): effort ceiling, and a finding budget ("if >N criticals in dimension X, stop sweeping and start fixing").

Phase 1 — Fan-out (parallel, one auditor per dimension)

Dispatch independent subagents/workflow stages, one per dimension. Each auditor's prompt MUST carry a handoff contract:

  • the dimension's question, the scope boundary, where to look first
  • evidence format: every finding = one line of file:line / query / output proof — no vibes, no "seems like"
  • severity grammar: Critical (wrong results / data loss / security) · High (will bite soon) · Medium (debt) · Low (polish)
  • return findings as structured list, not prose

Auditors are blind to each other — convergent findings from independent auditors are the strongest signal a sweep produces. Note them explicitly.

Phase 2 — Adversarial verification (the step lazy sweeps skip)

Raw findings lie. Before synthesis:

  1. Dedup across dimensions (same root cause surfaces in many coats — use /altitude to name the shared layer).
  2. Every Critical/High gets a /refute pass by a verifier that did NOT find it: reproduce it or kill it. Verdicts: CONFIRMED / REFUTED / PLAUSIBLE.
  3. Only CONFIRMED findings may use the word "broken" in the report. PLAUSIBLE ships in an appendix, clearly labeled.

Phase 3 — Graded synthesis

One report, led by the number that matters:

SWEEP: <scope> | <n> dimensions | X confirmed (C/H/M/L: a/b/c/d) | grade: B-
  • If --grade: score each dimension 1–5 against the best-in-class benchmark for its domain, WITH evidence per score. The grade's job is honesty, not motivation — a B- that's real beats an A that isn't.
  • Convergent findings and systemic patterns first (three dimensions hitting the same subsystem = an /altitude problem, not three bugs).
  • The "what's NOT broken" section is mandatory — a sweep that only lists problems can't be used to decide what's safe to build on.

Phase 4 — Findings become mechanisms (or the sweep was theater)

Every confirmed finding lands as exactly one of:

  • patch (fixed in the sweep's follow-up PRs, smallest first)
  • ticket (filed with the evidence line attached, severity mapped)
  • gate (a tripwire/CI check so the class can't recur — preferred for anything that recurred)
  • accepted risk (/verdict log, named human on the acceptance)

Close by logging the sweep itself: /verdict log SWEEP: <scope> | grade | top systemic finding | date. The next sweep of the same scope starts by diffing against this line — grades that don't move are the real report.

Rules

  • Breadth-first, fix-later. Auditors that stop to fix lose coverage; the single exception is a Critical actively corrupting data — stop the sweep, raise it immediately.
  • Cap the fan-out to what the finding budget can absorb. 21 agents producing 400 findings nobody triages is worse than 6 producing 40 that all land.
  • Sweeps are periodic, not heroic: the value compounds when grade N+1 is compared against grade N.

Composes with

  • /drift (the spec-drift dimension), /refute (Phase 2), /altitude (dedup + systemic naming), /gate (sweep budgets + recurrence tripwires), /verdict (the sweep ledger line). If your workflow has a per-object deep-review or planning-review step, a sweep is its strongest input.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 326,506. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.