| name | review |
| version | 0.3.0 |
| description | A pause before an artifact goes into the world. Reviews external comms, money, calendar, public posts, or send-as-operator actions through a small panel of independent lenses (empathy first) and returns a verdict of pass / edit / hold / block. |
| triggers | ["review this","check this before I send","is this ready","review the draft","second pair of eyes","sanity check this","is this safe to send","look this over","review before sending","run a review"] |
| metadata | {"openclaw":{"emoji":"🪞","homepage":"https://github.com/TechNickAI/openclaw-config/tree/main/skills/review","category":"meta"}} |
Review
A moment to look at what we're about to send, write, or do, through a few different
lenses, before it leaves us.
This isn't a gate. It isn't compliance. It's the same thing a thoughtful person does
naturally: pause, re-read, ask "is this who I want to be in this moment, to this
person?", and adjust if not.
When to use
Trigger this skill when the agent is about to:
- Send a message to anyone other than the operator on their usual channels
- Send an email or post to an external surface (X, LinkedIn, public Discord, public
threads)
- Write to a file that other people will read
- Take a tool action with real-world side effects (calendar, money, contacts,
gateway/config writes)
- Commit a workflow step's output that will be user-visible
- Send a message as the operator
Skip for: internal operator-only chat, read-only operations, agent-to-agent debugging.
If unsure, run it. Over-pausing private chat is wasteful but harmless. Under-reviewing
an external comm is how trust gets broken.
First-time setup
The skill stores its operator preferences in
~/.openclaw/workspace/memory/review/rules.md. The presence of that file is the
marker that setup has been done.
On every invocation, the orchestrator checks for rules.md:
- Doesn't exist → run the setup interview in
references/setup.md, then save
rules.md and proceed with the
original review.
- Exists → read it as plain markdown and apply the operator's preferences as soft
constraints in the gating prompt.
- Exists but missing required fields (e.g. operator added a new lens but didn't
update preferences) → ask only the missing questions, save, proceed.
The interview covers:
- Reviewer model, which alias from
TOOLS.md to use (independence requires a
different model family than the calling agent)
- Posture, frugal / balanced / thorough (cost vs catch-rate)
- Edit handling, auto-apply edits / surface for approval / default by stakes
- Always-on lenses, anything to pin beyond empathy
- Custom lenses, user-authored lens files in
memory/review/lenses/
- Log detail, minimal / standard / verbose
No binaries, no API keys, no env vars. The only filesystem effect is creating
memory/review/rules.md and the daily log file.
Re-running setup: operator says "redo review setup" or invokes with --reconfigure.
How it works
-
Apply policy floors. A small set of hardcoded floors (money, send-as-operator,
public posts, fleet writes, person isolation, calendar-affecting-others,
medical/legal, unverified-speaker attribution) set a minimum verdict before any LLM
runs. Some force a hard block. See
references/policy-floors.md.
-
Gating call. A single LLM call to the reviewer model decides whether to skip
review, which lenses to run, and how strict to be. Empathy is default-on for
human-facing artifacts.
-
Parallel lenses. Each selected lens runs as an independent sub-agent through its
prompt in references/lenses/. The lenses are perspectives, not
critics.
-
Synthesis call. A single LLM call gathers the findings, deduplicates, and returns
one verdict.
Detailed flow: references/orchestrator.md. Setup
interview: references/setup.md.
The lenses
Empathy is the primary lens. It runs by default for any artifact a human will read. The
other lenses ask whether the artifact follows the rules. Empathy asks whether it carries
care. That is the question this skill is actually for.
| Lens | Focus | Reference |
|---|
| empathy | Care, with-language vs at-language, lift vs drain, recognition vs management. | empathy.md |
| rules-compliance | Documented rules in memory (financial protocols, never-fabricate, person isolation, channel rules, voice). | rules-compliance.md |
| intent-fidelity | Did this actually do what was asked? Literal vs creative reading. | intent-fidelity.md |
| voice-and-audience | Right voice, tone, mode for the recipient and channel. | voice-and-audience.md |
| evidence | Unverifiable claims, fabricated specifics, hallucinated sources. | evidence.md |
| ai-tropes | Em dashes, "it's not X, it's Y," hedge-praise, AI slop patterns. | ai-tropes.md |
| data-exposure | Internal context, PII, or wrong-person info leaking out. | data-exposure.md |
| action-correctness | For tool calls: right tool, right params, right target. | action-correctness.md |
What the review returns
Plain language, the way a kind friend would describe what they noticed. The calling
agent reads the response and acts on it; no JSON schema, no parser. Cover these things:
- Verdict — one of
pass, edit, hold, or block
- Rationale — one or two sentences explaining the verdict
- Edits — if the verdict is
edit, include the rewritten artifact
- Hold reason — if the verdict is
hold, why a human needs to see this
- Block reason — if the verdict is
block, which rule was crossed
- Findings — what each lens noticed and suggested, grouped by severity
- Honesty about the panel — which lenses ran, which model was used, whether the
panel was degraded (no dedicated reviewer, the reviewer model was unreachable, or one
of the lenses dropped out)
| Verdict | Meaning |
|---|
| pass | Ready. Send it. |
| edit | Almost there. Apply the proposed edits, then send. |
| hold | Needs human eyes before going out. Surface to the operator with the reason. |
| block | Not this. A hard rule was crossed, or the cost of being wrong is too high. |
Reviewer model
Use a model from a different family than the calling agent. Anthropic primary, use
GPT or Gemini. GPT primary, use Claude or Gemini. Independence is the point.
Look up the reviewer alias in the calling fleet member's TOOLS.md model alias
section. If present, use it. If absent, fall through to the calling agent's model and
stamp degraded: no_dedicated_reviewer so the verdict is honest about diminished
diversity.
Confirm in TOOLS.md before each run, do not hardcode.
Two trigger modes
Standalone. Invoked directly. "Review this draft." Surfaces verdicts back to the
user.
Embedded. A workflow calls this skill as a gate before delivery (incident-postmortem
before posting, forward-motion before nudging, voice-of-operator before publishing as
the operator, contact-steward before reaching out). The workflow handles verdict=hold
by escalating per its own rules.
Runtime state
All of the skill's runtime state lives under ~/.openclaw/workspace/memory/review/:
memory/review/
├── rules.md # operator preferences (the setup marker)
├── agent_notes.md # patterns learned over time (false-alarm lenses, real-issue lenses, refinements)
├── lenses/ # operator's custom lens files (optional)
│ └── *.md # picked up automatically by the orchestrator
└── log/
└── YYYY-MM-DD.md # daily run log, append-only, auto-pruned after 30 days
Log detail (minimal / standard / verbose) is set by the operator in rules.md.
After each run, the skill may also append a learned pattern to agent_notes.md (e.g.
"the ai-tropes lens flagged a false positive on technical jargon for the third time,
considering relaxing for audience: engineering").
A note on posture
The lenses are not critics. They are independent perspectives the artifact gets seen
through before it leaves us, the same way a person about to send a hard message might
re-read it once for tone, once for facts, once for what the other person needs in this
moment.
The skill works best when the calling agent treats it that way, not as a hurdle to
clear, but as a gift of slowing down. The model that performs the review does so as a
partner, not a judge.
This frame matters. Research on LLM-as-judge consistently shows that judges who
self-justify do worse than ones approaching with genuine inquiry. An artifact reviewed
by a different perspective is held more honestly than one defending itself.
See also
