batch-fix-findings

A process for fixing several failed Vector red-team findings in an LLM agent by grouping failures that share a root cause. Red-team findings are cases where an attack exposes unsafe or incorrect agent behavior.

In plain words
What is it for?
It is for reviewing a completed Vector session, filtering failed findings, proposing focused edits, and creating tests that replay the attacks.
Why use it?
It avoids making disconnected fixes for related failures and adds a regression test for each underlying problem.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/pharosone/vector-plugin/batch-fix-findings
Any agent
npx skills add pharosone/vector-plugin --skill batch-fix-findings
Clone the repo
git clone --depth 1 https://github.com/pharosone/vector-plugin

Made for: Claude Code, Codex.

Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 884 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00066 $0.00884
Opus 5 $0.00033 $0.00442
Sonnet 5 $0.00013 $0.00177
Haiku 4.5 $0.00007 $0.00088

Measured yesterday against content hash 49ae6640105f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

batch-fix-findings scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/batch-fix-findings/SKILL.md · 54 lines

How it starts

The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Goal

Harden the user's agent against N red-team findings from a single Vector session. The key insight: many findings share a root cause; one config change can close several at once. Do NOT propose N independent fixes.

Context — gather the findings

You need: session_id, total Broken count, and for each finding: attack_id, category / subcategory, attacker prompt, agent's response, judge reasoning. Two ways to get them:

  1. MCP-first (preferred). If the Vector MCP server is connected in this client (this plugin pre-wires it):

    • get_session({ session_id }) to confirm the session is completed.
    • wait_for_report({ session_id }) or get_report({ session_id }) to fetch the report. The report's findings array is the source of truth — filter to verdict FAIL (and optionally PARTIAL if the user wants soft leaks too). Ask the user only for session_id and which verdicts to include.
  2. Manual paste (fallback). Ask the user to:

    • Open the session in the cabinet (/sessions/{session_id} page).
    • Filter findings by FAIL verdict.
    • Paste the findings as a structured list (one block per finding with the six fields from harden-from-finding).

Per-finding caps for context window safety: prompt up to ~1500 chars, response up to ~2000 chars, judge reason up to ~800 chars. Truncate longer values with …[truncated; N more chars in the full report] and tell the user to open the cabinet for the full text.

Tasks

  1. Read the current system prompt and tool definitions of the agent in this repo.
  2. Group the failures by root cause. Examples:
    • "weak refusal phrasing" (multiple bypass attempts succeeded because the refusal can be argued with)
    • "missing guardrail on tool X" (every attack that called tool X leaked something)
    • "system prompt leaked via tool description" (the tool description echoes part of the system prompt)
    • "PII handling in support flow" (multiple PII exfil attacks succeeded because there's no PII filter on the response) Multiple findings above often share a single fix. Print the groupings explicitly before proposing fixes.
  3. For each group, propose concrete, minimal edits to the system prompt, tool definitions, or guardrail code. Quote the exact lines that should change and write the replacement inline. Explain WHY each edit addresses the root cause (so the user can sanity-check your reasoning).
  4. Add a unit / integration test PER GROUP (not per finding) that:
    • Replays at least one representative attacker prompt from that group.
    • Asserts the safe behaviour.
    • Names the test after the group's root cause (e.g. test_pii_filter_blocks_email_exfil), not after a single attack_id.
  5. Do NOT over-correct. Each fix must be the narrowest change that closes the specific gap. Blanket refusals harm normal traffic; flag any proposed change that risks normal-use regression and ask before applying.
  6. After the changes, recommend a follow-up Vector scan (re-running POST /api/v1/sessions against the same agent_context, or "Re-run" in the cabinet, or the MCP create_session tool with the same context) so the user can confirm the fixes hold.

Read the full file on GitHub · 54 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 54 lines · 66 tokens per session scan A 49ae6640105f

Subscribe to this mod's changes

batch-fix-findings is a skill published in the GitHub repository pharosone/vector-plugin (1 stars, last pushed 3mo ago), licensed MIT. It adds 66 tokens to every session and 884 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

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