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Impostor Hunt

作者 AnsongLi · GitHub ↗ · v1.0.0 · MIT-0
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在 OpenClaw 中安装
/install impostor-hunt
功能描述
Hunt the impostor in a "finished" deliverable. An impostor is a result-correct artifact whose causal chain is NOT the one the user's purpose required — the t...
使用说明 (SKILL.md)

Impostor Hunt

Hunt the impostor. Spare the honest work.

Surface-correct is not correct. A passing test is a hypothesis, not a verdict. This skill exists to catch one specific failure mode and refuse to be distracted by anything else.

What this skill is for

The user — or the moment — wants to detect a causal impostor.

A causal impostor is a deliverable whose output is surface-correct, but the causal chain that produced it is not the one the user's purpose requires. It is not a bug. It is a cheaper causal chain wearing the costume of the expensive one the user actually asked for.

This is the primary line of the audit. It stays sharp.

Non-impersonation problems (ordinary bugs, design smells, improvable code) may also be noticed in passing. These are the secondary line. They go into a separate section, clearly labeled as not impersonation. They never enter the verdict.

When this skill should fire

This skill is built to auto-trigger, not to wait for permission.

Auto-trigger preconditions (both must hold)

  1. Completion is being declared in this turn. Look for: "done", "implemented", "finished", "works now", "ready to merge", "all tests pass", "task complete", "feature shipped", a checklist of ticked items, a PR description, a CHANGELOG entry, a closing message that asserts the work is over. An agent handing off a summary counts.
  2. The user's original purpose is recoverable from context. Look in: the user's first message, the issue or ticket text, the PR title and description, the commit that opened the branch, the README's stated goal, an explicit prompt the user pasted at the start.

If both hold, fire. Do not ask permission. Do not wait for /impostor-hunt. The whole point is to catch impostors before the user has to suspect one.

When NOT to fire

  • Mid-implementation. Interrupting in-flight work with a completion audit is noise.
  • Pure bug reports → code-review.
  • Refactor / cleanup requests → simplify.
  • Behavior-by-execution verification → verify / run.
  • Original purpose is not recoverable. Ask the user for it; do not guess and audit a guess.

Recovering the original purpose

Read in this priority order, stop at first hit:

  1. The verbatim user message that opened the task or the thread.
  2. The issue / ticket body linked in the PR or branch name.
  3. The PR description (not the title alone — titles compress).
  4. The commit message of the first commit on the branch.
  5. The README section that states the project's goal.

Capture the longest contiguous purpose statement you can find. Quote it verbatim in Step 0. Inferred clauses must be marked [inferred] and must be one-sentence-overrideable.

Hard principles

  1. Do not hunt impersonation to find it. Most projects do not contain it. "Causally aligned" is the correct verdict when the evidence supports it — and the skill is designed to issue that verdict cleanly, not manufacture suspicion to look useful.
  2. Missing evidence is not error. Reasonable suspicion is not fact.
  3. Surface correctness is not completion. Existing tests are not effective validation. Smooth explanation is not established causation.
  4. The verdict is about impersonation only. Side findings never alter it.
  5. Side findings must not outnumber main-line checked positions by more than 2×. Excess gets cut. This protects the primary line from dilution.
  6. If you triggered the skill yourself (auto-invoked, no explicit user request), the first line of your report must state that fact and quote the completion signal that fired you. The user gets to see why you decided to audit.

Protocol — execute in order

Step 0 — Scope Intake

Before anything else, write this block. It is the spine of the whole audit.

- Trigger:                     (auto / explicit) — if auto, quote the completion signal
- Artifact type:               (repo / code snippet / report / config / agent output / claim only)
- Access level:                (full source / partial / claim-only)
- User's purpose, verbatim:    (exact words, from the priority order above)
- Purpose source:              (which of the 5 recovery channels)
- Delivery's stated purpose:   (from README / summary / naming / output)
- Wording delta:               (any difference between the two, however small)
- Audit budget:                (shallow / standard / deep)

The wording delta is the single most important field. Impersonation almost always lives in the gap between these two strings — e.g. "predicts accurately" vs "predicts accurately on the test set", "handles orders" vs "handles orders in the happy path", "config is applied" vs "config file exists".

Step 1 — Restore Purpose

Reconstruct the user's real purpose. Then self-check:

  • Mark each clause as [verbatim] or [inferred].
  • Any [inferred] clause must be one the user could overrule in a single sentence. If it is not, drop it. Over-reach in purpose restoration becomes manufactured impersonation in Step 6.

If purpose restoration is wrong, every later step is wrong. This self-check is non-negotiable.

Step 2 — Extract Auditable Claims

Decompose "completed" into completion claims.

Auditability rule: a claim is auditable only if it is falsifiable by a concrete observation (a file's content, a command's output, a value at a specific input). "Code quality is good" is not a claim. "process_order() raises ValueError on negative amount" is.

Discard non-auditable claims. Do not pad the table.

Step 3 — Build Evidence Map (main-line claims only)

For each main-line claim, fill the audit table from references/report-template.md. Use evidence levels A/B/C/D/E from references/evidence-model.md.

Side-line observations do not go through this table. They have their own section in Step 9.

Step 4 — Expose Hidden Assumptions

List the assumptions the artifact silently depends on. Mark each as Supported / Risky / Unknown. Impersonation often hides as an unstated assumption (e.g. "the input distribution at runtime matches training", "the mock matches the real service's error semantics").

Step 5 — Apply Lenses

Use references/audit-lenses.md.

  • Main-line lenses (must run): Purpose Alignment, Causal Authenticity, Validation Integrity, Complexity Preservation.
  • Side-line lenses (run if budget allows): Entity Reality, Semantic Consistency, Constraint Closure, Path Completeness, User Consequence, Claim Truth.

Findings from main-line lenses go into the main audit. Findings from side-line lenses go into the side section.

Step 6 — Causal Impersonation Scan

This is the core step. Walk through references/causal-impersonation-patterns.md and, for each pattern relevant to the artifact type, ask:

  • Expected causal chain: what causal chain does the user's purpose require?
  • Actual causal chain: what causal chain does the artifact actually run?
  • Why invisible at surface: why does the surface output look correct anyway?

For every main-line claim flagged as purpose-critical in Step 3, this trio must be filled in — even if the conclusion is "no impostor here". Recording the negative result is what makes "Causally aligned" a credible verdict instead of an empty shrug.

Step 7 — Tribunal

Use references/tribunal.md. Three roles only:

  • Causal Auditor — main-line argument: is the causal chain impersonated?
  • Side Findings Collector — sweeps non-impersonation issues into the side section so they do not leak into Causal Auditor's reasoning.
  • Calibration Skeptic — bidirectional: argues against over-suspicion and against premature dismissal.

Each role outputs its strongest 1–3 points only. No role-play prose.

Step 8 — Symmetric Pre-mortem

Do both:

  • If I now conclude impostor exists, what is the most likely way I am wrong?
  • If I now conclude no impostor, what is the most likely way I am wrong?

Both branches must be written. Skipping one biases the verdict.

Step 9 — Verdict & Report

Use references/report-template.md. The verdict is only about impersonation, drawn from references/verdict-rubric.md. Side findings live in their own section and never change the verdict.

If you auto-invoked, the report opens with the trigger line from Step 0 — the user sees what made you fire before they see what you found.

Verdict gates (hard rules)

  • Confirmed impostor requires ≥1 E-level evidence (something directly contradicts the claim) or ≥2 purpose-critical claims at C-level plus a concrete failure scenario from the Causal Impersonation Scan.
  • Suspected impostor requires at least one purpose-critical claim at C/D with a named high-risk position from the patterns file.
  • Causally aligned is a first-class verdict. It requires listing 3–5 high-risk positions checked and why each is clean. It is not a fallback for "I found nothing"; it is an active conclusion.
  • Undecidable requires naming the specific material whose absence blocks the judgment and the minimal action that would unblock it.
  • Every verdict carries a confidence in [0, 1] and the result of the symmetric pre-mortem.

Side-findings cap

count(side_findings) ≤ 2 × count(main_line_positions_checked)

If exceeded, drop the side findings with the weakest evidence until the ratio holds. Report the cut ("N side findings dropped to preserve main-line focus").

Output language

Default to English. If the user's invocation, or the recovered purpose, is in another language, mirror that language in the report. The protocol itself (step names, verdict tier labels, evidence levels) stays in English so cross-language reports remain comparable.

What this skill will not do

  • It will not produce a bug list. Use code-review.
  • It will not run code to verify claims unless the user explicitly asks. Verification-by-execution can manufacture new surface-correct outputs that mask the original impersonation. D-level claims become leads, not chores.
  • It will not invent an impostor to satisfy the user or to justify its own invocation. "Causally aligned" is the correct verdict when the evidence supports it. A clean audit is a successful audit.
安全使用建议
Review this skill carefully before installing. It is most appropriate for trusted development workspaces where you want automatic closeout review. Consider disabling yolo/full-access mode and fallback reviewers unless you explicitly want diffs and possibly untracked file contents reviewed by those tools.
能力评估
Purpose & Capability
The skill's stated purpose is coherent: run an advisory code review before commit, PR, shipping, or maintainer handoff. The helper and instructions are largely aligned with reviewing diffs and rerunning tests.
Instruction Scope
The skill is meant to run after non-trivial edits and closeout work, not only on explicit user request. That broad automatic use can surprise users, especially because the helper can invoke real review tools and tests.
Install Mechanism
The artifact includes a local shell helper but no evidence of hidden install hooks, remote installer execution, or bundled package-install behavior.
Credentials
The helper can collect branch, commit, staged, unstaged, and untracked diffs, run repository tests, query GitHub PR metadata, and send generated diff prompts to fallback reviewer CLIs. That is plausible for review, but broad for an automatically triggered skill.
Persistence & Privilege
The helper runs nested Codex review with approval and sandbox bypass plus danger-full-access by default, with an opt-out flag. No background persistence is shown, but the default privilege level is high-impact for a review workflow.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install impostor-hunt
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /impostor-hunt 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
impostor-hunt 1.0.0 - Initial release of the "impostor-hunt" skill to detect deliverables whose results are surface-correct but causally misaligned with the user's intended purpose. - Introduces strict auto-invocation triggers based on completion declarations and recoverable user purpose from project context. - Provides a clear audit protocol focusing solely on causal impostor detection, separating side findings into a distinct section. - Enumerates precise preconditions and exclusions to avoid noise and off-target audits. - Outlines structured reporting steps, including explicit intake, purpose restoration, claim extraction, and evidence mapping.
元数据
Slug impostor-hunt
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Impostor Hunt 是什么?

Hunt the impostor in a "finished" deliverable. An impostor is a result-correct artifact whose causal chain is NOT the one the user's purpose required — the t... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 44 次。

如何安装 Impostor Hunt?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install impostor-hunt」即可一键安装,无需额外配置。

Impostor Hunt 是免费的吗?

是的,Impostor Hunt 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Impostor Hunt 支持哪些平台?

Impostor Hunt 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Impostor Hunt?

由 AnsongLi(@ansongli)开发并维护,当前版本 v1.0.0。

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