情绪.skill / Emotion Skill

Positive routing for coding agents under pressure. Use when repo debugging, scoped implementation, repeated-failure recovery, evidence-demanding review, caut...

Security Scan

The skill's code, runtime instructions, and declared requirements are consistent with a local, policy/routing-only router for coding agents; it does not request unrelated credentials or hidden installs, but it does persist small host-owned JSON files when used and exposes an audit opt-in for raw affect, so review the persisted data and full engine source before production use.

Purpose & Capability

The name/description (positive routing for coding agents) matches the files and runtime behavior: the engine computes routing/guidance fields and the adapter persists small host-owned JSON profiles/state. No unrelated env vars, binaries, or cloud credentials are requested.

Instruction Scope

SKILL.md and the code focus on ingesting recent user turns, runtime pressure metadata, and optional store state to emit a compact host contract. The minimal_host_adapter reads/writes only the documented host files (user_profile.json, last_state.json, calibration_state.json) and merges store with incoming event payloads as expected. The skill documents an explicit audit opt-in for raw affect and warns not to inject raw labels into model prompts.

Install Mechanism

No install spec; runtime is Python-only using the standard library. scripts/download_smoke.py and other helpers run local smoke tests; there are no network downloads or third-party package installs in the provided files.

Credentials

The skill declares no required environment variables or external credentials. The runtime writes and reads host-owned profile/state JSON (intended). There are no hidden credential accesses in the presented files. The only sensitive opt-in is host_capabilities.include_raw_emotion, which intentionally exposes internal labels/emotion_vector for audit.

Persistence & Privilege

The skill is not force-installed (always:false). The minimal host adapter can persist three host-owned JSON files under a host-specified --store-dir when persistence is enabled; this is intended behavior but means hosts should choose storage locations and permissions carefully. agents/openai.yaml allows implicit invocation (allow_implicit_invocation: true), which is a policy choice documented in the manifest.

Assessment

This package appears coherent and local: it computes routing/guidance JSON and only writes three documented host files when you enable persistence. Before installing to production: (1) run the included smoke test (python scripts/download_smoke.py) to confirm behavior in your environment; (2) inspect the full scripts/emotion_engine.py for any unexpected network calls (the docs state none, but review the full file if you need high assurance); (3) pick a secure, host-controlled --store-dir and review what will be persisted (user_profile.json, last_state.json, calibration_state.json); (4) be cautious if you enable raw-affect audit (host_capabilities.include_raw_emotion) because that exposes internal labels/emotion_vector to the host output; (5) if you do not want implicit invocation, review agents/openai.yaml (allow_implicit_invocation) and your host policy before enabling automatic routing. Overall the skill is internally consistent with its stated purpose.

Emotion Skill

Emotion Skill is a small runtime router for coding agents.

It reads the latest user turn, recent dialogue, retries, delay pressure, optional host state, and optional feedback from the last routing decision. It then returns a compact host contract that tells the agent how to work this turn.

The core rule: internal user-state signals must become positive execution instructions. Production hosts should pass guidance.system_prompt_addendum, response_constraints, and routing into the model. Raw affect fields stay internal unless audit mode is explicitly enabled.

Use It When

• A bug fix has failed more than once.
• The user asks for evidence, exact checks, logs, or root cause.
• The user says to touch only specific files or avoid config drift.
• A tool call, session, queue, or heartbeat path has gone silent.
• The user says the work is good and the agent should close out.
• The agent needs to choose between collect, steer, and interrupt modes.

What It Returns

Use the host command for real integration:

python scripts/emotion_engine.py host --message "Show me the basis before changing more files." --pretty

Default host shape:

{
  "mode": "skeptical",
  "route_reasons": ["repeat_failure_pressure", "evidence_requested"],
  "response_constraints": ["show_basis_first", "name_verification_steps"],
  "guidance": {
    "system_prompt_addendum": "The user wants evidence before more changes. Start with a verification point, command, or log excerpt, then give the conclusion and next step.",
    "tone": "evidence_first"
  },
  "routing": {
    "reply_style": "evidence_then_act",
    "verification_level": "high",
    "queue_mode": "collect",
    "prefer_main_thread": true,
    "progress_update_interval_sec": 20
  }
}

Default output deliberately keeps raw labels and raw emotion_vector out of the host prompt path. This keeps the skill from amplifying negative state words inside the model context.

Host Fields

• guidance.system_prompt_addendum: positive instruction text for the host LLM.
• guidance.tone: compact tone target such as evidence_first, careful_and_bounded, or guarded_closeout.
• response_constraints: compact reply guardrails.
• route_reasons: enum-like routing codes for logs and telemetry.
• routing.reply_style: response posture.
• routing.verification_level: checking depth.
• routing.queue_mode: collect, steer, or interrupt.
• routing.prefer_main_thread: keep the work on the main turn when user trust or clarity needs it.
• routing.progress_update_interval_sec: progress cadence for long-running work.
• satisfaction_lock: closeout guard after success.
• interaction_state: positive host-facing axes: clarity, trust, engagement.
• state.state_delta: action-named shifts such as needs_concrete_unblock, needs_evidence_first, or needs_alignment_check.
• memory.should_persist: host-side persistence recommendation.

Input Contract

Smallest valid payload:

{
  "message": "latest user message"
}

Production payload:

{
  "message": "Only touch the parser file and show the failing path first.",
  "history": [
    {"role": "user", "text": "earlier user turn"},
    {"role": "assistant", "text": "earlier assistant turn"}
  ],
  "runtime": {
    "response_delay_seconds": 20,
    "unresolved_turns": 3,
    "bug_retries": 2,
    "same_issue_mentions": 2,
    "queue_depth": 1,
    "background_tasks_running": 1,
    "last_routing_outcome": {
      "mode_was": "skeptical",
      "user_followed_up_with": "still broken"
    }
  },
  "last_state": {
    "vector": {},
    "emotion_vector": {},
    "ttl_seconds": 1200
  },
  "calibration_state": {},
  "user_profile": {}
}

Malformed JSON, missing files, and top-level arrays return exit code 2 with a single-line error.

Raw Affect Audit Mode

For audit and calibration only:

{
  "host_capabilities": {
    "include_raw_emotion": true
  }
}

This adds diagnostics.internal.labels, diagnostics.internal.emotion_vector, raw state_delta, and mode_scores. Keep these fields out of normal LLM prompts.

Runtime Commands

Command	Purpose
host	compact production contract
run	full diagnostics
screen	deterministic first pass
confirm	final state and weight schedule
predict	risk, stall, patience, and semantic-pass budget
route	routing only
guide	short-probe guidance
overlay	overlay prompt inspection
posthoc	review-pass and calibration inspection

Persistence Boundary

The core engine is stateless. It returns JSON, makes no network calls, and writes only when --output is provided.

The minimal host adapter writes three host-owned JSON files under --store-dir when persistence is enabled:

• user_profile.json
• last_state.json
• calibration_state.json

Use --no-persist for read-only previews. Use --ignore-bad-store to skip corrupt store files and continue from empty values.

Integration Pattern

1. Run host when a user turn arrives.
2. Put guidance.system_prompt_addendum before the model's task instructions.
3. Put overlay_prompt near the runtime metadata.
4. Feed response_constraints into reply planning.
5. Feed routing into queue, heartbeat, progress cadence, and subtask policy.
6. Apply satisfaction_lock after success.
7. Persist memory.proposed_calibration_state only in host-owned storage.

Published Bundle

ClawHub publish now ships the runtime-facing subset only:

• SKILL.md
• README.md
• README.zh-CN.md
• CHANGELOG.md
• agents/openai.yaml
• scripts/emotion_engine.py
• scripts/minimal_host_adapter.py
• scripts/download_smoke.py
• demo/local_history_event.json
• references/examples.md
• references/model-prompts.md
• references/emotion-value-model.md
• references/emotion-policy-matrix.md
• references/integration-openclaw-hermes.md

The full GitHub repo keeps the heavier regression, audit, and calibration assets.

Validation

Published-bundle smoke:

python scripts/download_smoke.py

Full GitHub validation:

python scripts/alignment_test.py
python scripts/ablation_test.py
python scripts/smoke_test.py --seed 20260424 --strict
python scripts/independent_audit.py
python scripts/marketplace_tag_audit.py
python scripts/feature_gate_audit.py
python scripts/bundle_manifest_check.py

Current local regression results:

• alignment: 70/70
• ablation: 333/333
• strict smoke: ok
• independent audit: ok
• download smoke: ok
• bundle manifest: ok

Good Fit

Use it for coding-agent orchestration, repository debugging, scoped edits, verification-first replies, and closeout behavior after success.

Use a different skill for general emotional memory, roleplay, personal journaling, or long-term personality simulation.