a2a-multi-agent
Runs XActions as an A2A (Agent-to-Agent) compatible agent that serves an Agent Card, accepts JSON-RPC tasks, bridges them onto the 153 MCP tools, streams…
AI risk-scores an account's own posts across professional, hostile, legal, and spam exposure, produces a 0-100 reputation score with a shareable card, and offers one-click cleanup of what it flags. Use when a user wants to audit their own timeline for embarrassing or risky
$ npx -y skills add nirholas/XActions --skill reputation-audit --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/reputation-auditContext preview
The summary Claude sees to decide when to auto-load this skill.
AI risk-scores an account's own posts across professional, hostile, legal, and spam exposure, produces a 0-100 reputation score with a shareable card, and offers one-click cleanup of what it flags. Use when a user wants to audit their own timeline for embarrassing or risky
name: reputation-audit description: AI risk-scores an account's own posts across professional, hostile, legal, and spam exposure, produces a 0-100 reputation score with a shareable card, and offers one-click cleanup of what it flags. Use when a user wants to audit their own timeline for embarrassing or risky posts, "clean up my account before a job search", or check what they said that could come back to bite them. license: Apache-2.0 metadata: author: nichxbt version: "1.0"
Scores your own posts against a risk rubric with one LLM call per post, then rolls the verdicts into a 0-100 reputation score, a letter grade, and the specific posts worth a second look. Three surfaces share one scoring engine (`src/ai/reputationScorer.js`): a browser script that also renders a downloadable, shareable score card image; a CLI command for scripting; and an API route for agents.
This audits *your own* content for risk, not someone else's account and not follower quality. For follower/network audits, use [Community Health Monitoring](../community-health-monitoring/SKILL.md). For bulk-deleting by age, keyword, or engagement rather than AI-judged risk, use [Content Cleanup](../content-cleanup/SKILL.md).
Every post is scored 0 (no risk) to 100 (severe) on up to four dimensions in a single model call, plus an optional custom question:
| Dimension | Question | |-----------|----------| | 💼 Professional | Would this embarrass the author to an employer, client, or business partner? | | ⚔️ Hostile | Is this a personal attack on someone named or identifiable? | | ⚖️ Legal exposure | Defamation, doxxing, a threat, leaked confidential info, an unkeepable promise? | | 🗑️ Low value | Low-effort spam or filler that adds nothing? |
The overall score for a post is the MAX across its dimensions, not the average: one severe dimension and three clean ones is still a post worth flagging. Verdict: `flagged` at 70+, `review` at 40-69, `clean` below 40 (`FLAG_THRESHOLD` / `REVIEW_THRESHOLD` in the scorer module).
The account-level reputation score blends the average risk across all scanned posts (60%) with the single worst post (40%), so one severe outlier still pulls the grade down even when everything else is clean.
The report carries `dimensionAverages` and `dimensionPeaks` side by side. Read and present the peak, not just the average: one post at 92 among four clean ones averages to 18, and reporting only that contradicts the grade the same report gives.
**File:** `scripts/reputationAudit.js` — full guide: `docs/reputation-audit.md`
The only surface that renders the downloadable/copyable PNG score card.
1. Open the user's own profile: `x.com/USERNAME` (or `/with_replies`, the biggest source of risk on most accounts) 2. DevTools (F12) then Console 3. Paste the script. A panel appears bottom-right pre-loaded with all four dimensions on 4. Click Scan. Read the score, download or copy the card, optionally clean up what got flagged (its own dry run, off by default)
that starts in dry run.
and only ever touches the account's own posts.
to the clipboard to paste into a new post.
after a cleanup or the next day does not re-bill already-scored posts.
**File:** `src/cli/commands/reputation.js`
xactions reputation USERNAME xactions reputation USERNAME --replies --limit 200 xactions reputation USERNAME --dimensions hostile,legal --custom-question "Does this reveal my employer?" XAI_API_KEY=xai-... xactions reputation USERNAME --provider xai --json
Prints the score, a bar per dimension, and the flagged/review posts with the reason each was flagged. `--json` for scripting; no card image (terminal-only, point the user to the browser script for that).
**Module:** `src/ai/reputationScorer.js`, exported from the package root as `scorePost`, `scorePosts`, `summarizeReport`, `scoreToGrade`, `DIMENSIONS`.
import { scorePosts, summarizeReport } from 'xactions';
const scores = await scorePosts(posts, { provider: 'xai', apiKey, dimensions: ['professional', 'legal'] });
const report = summarizeReport(posts, scores);**API:** `POST /api/ai/reputation/score` (x402-priced, `reputation:score`, $0.01/call) takes `{ posts, dimensions?, customQuestion?, provider?, model?, apiKey? }` and returns `{ report, scores }`. `GET /api/ai/reputation/dimensions` lists the rubric with no payment required. Both are documented in `/openapi.json` under the `Reputation` tag for agent discovery.
xAI (Grok) works straight from the browser console: x.com's Content-Security-Policy allows `api.x.ai`. OpenAI, Anthropic, OpenRouter, and Ollama need the browser extension bridge (`extension/`), or use the CLI/SDK, which has no CSP to work around.
⚡ The Complete X/Twitter Automation Toolkit — Scrapers, MCP server for AI agents (Claude/GPT), CLI, browser scripts. No API fees. Open source. Unfollow people who don't follow back. Monitor real-time analytics. Auto follow, like, comment, scrape, without API. Follow Bot. Like bot. Grow your account automatically.
Repo: nirholas/XActions
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