Turn timeline fragments, Slack logs, and pager history into a complete blameless postmortem — impact summary, clean timeline, contributing factors instead of a scapegoat, action items with owners and due dates, and lessons that survive the week.
Scan your OpenClaw config for the settings that quietly hand your agent too much power: unrestricted exec, open inbound DMs, secrets committed in config, the deny-write bypass, sandbox turned off, dangerous Docker binds, and elevated tools. Read-only, plain-English findings, grounded in the OpenClaw docs.
1
openclawsecurity-auditlinter+2
Background Agent Task Brief Writer — Delegate to Unattended Agents Without the Surprise PR
Write the delegation brief that lets a background or async agent succeed unattended — precise goal, hard constraints, testable acceptance criteria, a verification plan, and stop-and-escalate rules. Turns "go fix the flaky tests" into a spec an agent can actually execute.
1
delegationbackground-agentsasync-agents+6
EU AI Act Compliance Docs Writer — Model Cards, AI Disclosures, and Risk Tier Worksheets, Counsel Ready
Draft the AI documentation the EU AI Act expects — model cards, AI-use disclosures, transparency notices, risk-tier worksheets, and technical-doc skeletons. Counsel-ready drafts from a regulated-industry documentation professional, with templates included.
1
eu-ai-actcompliancedocumentation+6
Manuscript Submission Readiness Pack — Pre Submission Check Plus a Drafted Cover Letter
A complete pre-submission check for your research manuscript — journal requirements, reporting guidelines, ICMJE authorship, disclosures, registration, and reference integrity — with a READY or NOT YET verdict, a prioritized fix list, and a drafted, journal-ready cover letter.
Red-team your own AI agent for prompt-injection and tool-misuse vulnerabilities before it ships — then fix them. Maps your attack surface, generates a defensive test plan with the safe behavior expected for each case, and gives a prioritized mitigations list. Defensive use only.
1
llm-securityprompt-injectionagent-security+6
LLM Cost and Token Economics Modeler — Project Your Agent's Spend and Cut It in Half
Model what your LLM app or agent will cost, find where the money goes, and get a plan to cut it. Per-request and monthly projections, ranked cost drivers, an optimization plan with estimated savings, and unit economics against your pricing — with the arithmetic shown.
Select the smallest honest verification set for a change, including targeted tests, manual checks, missing-test recommendations, a broader fallback, and named remaining risk.