AI & Agentslow risk
x-sim
Offline X audience simulation and evaluation. Use when evaluating draft tweets, handle bios, pinned-post ideas, or promotion angles against real scraped X context without posting or mutating X state.
yourconscience/dotagents·skills/x-sim/SKILL.md
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Skill 指令
在 GitHub 查看原始檔案 ↗# x-sim
Use `x-sim` to simulate how real X audiences may react to a draft tweet, bio, pinned post, or handle-promotion angle.
This skill is offline-only. It reads scraped X data through `x-cli`, stores local context in SQLite, and produces evaluation reports. Never post, like, reply, follow, DM, or mutate X state from this skill.
## Example: Predicting Model-Announcement Tweets
Real e2e run (2026-06-10): predict how a frontier-model announcement would land, using followed AI accounts as the audience corpus.
```bash
x-cli following @yourhandle --count 100 --json # pick relevant accounts from who you follow
go run ./tools/x-sim init
for h in AnthropicAI _catwu trq212 karpathy thehypedotnews steipete; do
go run ./tools/x-sim source add-account @$h
done
go run ./tools/x-sim source add-search "claude fable"
go run ./tools/x-sim sync --since 3m --limit-per-source 50 # synced 215 attributed tweets
go run ./tools/x-sim brief --topic "fable" --out /tmp/x-sim-fable-brief.md
go run ./tools/x-sim eval-tweet --text "Introducing Claude Fable 5, our most capable model yet..." \
--topic "fable" --out /tmp/x-sim-pred.md
```
The brief surfaced the real announcement (`@claudeai`: "a Mythos-class model that we've made safe for general use") plus audience echo (`@karpathy`: "same underlying model as Mythos but with added safeguards"). Comparing the draft against that evidence showed the predicted capability framing matched, but the actual hook was the safety-derivative angle - the kind of gap this skill exists to catch before posting.
## Core Rules
- Use `x-cli --json` for X reads; do not call raw X endpoints when `x-cli` covers the task.
- Keep the canonical store local: `${XDG_DATA_HOME:-~/.local/share}/dotagents/x-sim/x-sim.sqlite`, or `X_SIM_DB` when set.
- Default context window is the last 6 months.
- Treat simulation scores as directional, not truth. Ground every claim in local scraped examples when possible.
- Run the final voice pass through `humanizer` rules: remove generic hype, keep concrete evidence, and preserve the user's direct style.
- If asked to publish, schedule, or perform account actions, stop and explain that this skill only evaluates offline.
## CLI
From this skill directory:
```bash
go run ./tools/x-sim init
go run ./tools/x-sim source add-account @handle
go run ./tools/x-sim source add-search "agent evals"
go run ./tools/x-sim sync --since 6m --limit-per-source 200
go run ./tools/x-sim brief --topic "agent evals" --out /tmp/x-sim-brief.md
go run ./tools/x-sim eval-tweet --text "draft tweet" --topic "agent evals" --out /tmp/x-sim-report.md
go run ./tools/x-sim eval-handle --bio "..." --promotion "..." --out /tmp/x-sim-handle.md
```
The CLI is intentionally deterministic. Use the model's judgment to add richer audience emulation on top of the report, but keep the report grounded in the local DB.
## Workflow
1. **Initialize and verify**
- Run `x-cli auth status`.
- Run `go run ./tools/x-sim init`.
2. **Add sources**
- Add relevant accounts and searches with `source add-account` and `source add-search`.
- For broad topics, prefer focused search strings over generic keywords.
3. **Sync context**
- Run `sync --since 6m`.
- If sync fails, fix `x-cli` auth or query shape first; do not use account-mutating commands.
4. **Create a brief**
- Run `brief --topic ...`.
- Read the source handles, recurring terms, and evidence tweets.
5. **Evaluate**
- Use `eval-tweet` for draft tweets.
- Use `eval-handle` for bio, pinned-post, and promotion positioning.
- Compare scores across personas: technical builder, skeptical founder/operator, AI-agent power user, casual X reader, and contrarian critic.
6. **Humanize**
- Rewrite the best candidate so it sounds like the user.
- Prefer concrete nouns, actual measurements, and visible tradeoffs.
- Cut generic hype and vague "AI thought leader" positioning.
## Output Shape
Return a short markdown review:
```text
Verdict:
- ...
Likely audience reaction:
- ...
Best revision:
...
Why:
- ...
Risks:
- ...
Evidence:
- @handle: source tweet summary/link
```
## Safety Boundary
Allowed:
- `x-cli auth status`
- `x-cli timeline user ... --json`
- `x-cli search ... --json`
- local SQLite reads/writes
- markdown reports
Disallowed:
- posting, replying, liking, reposting, following, unfollowing, bookmarking, or DMing
- reading or printing X credential files
- pretending simulated reactions are measured engagement