LangBot Skill Authoring

LangBot Skill Authoring is a LangBot plugin for turning run traces, QA evidence, troubleshooting notes, and operator feedback into reviewable SKILL.md drafts.

It is not a replacement for LangBot's Skill runtime. LangBot already owns the runtime surface: Box-managed SKILL.md packages, activate, register_skill, Skill CRUD, and sandbox mounts. This plugin adds the missing authoring loop:

source evidence -> candidate -> risk report -> review -> export/publish package

For personal-assistant scenarios, the plugin also has a gated one-click mode:

source evidence or completed turn
  -> auto candidate -> risk report -> policy gate -> optional auto review/export

This mode is controlled by the auto_deposition_enabled master switch and is off by default.

Runtime Boundary

This plugin does not change how LangBot discovers, activates, mounts, or runs a Skill. Runtime usage remains owned by LangBot core:

Agents can create a Skill package under /workspace and call the built-in register_skill tool.
Operators can manage Skills through LangBot's existing /api/v1/skills surface.
Activated Skills still use LangBot's existing sandbox and permission checks.

The missing layer is the product workflow before registration: deciding what is worth keeping, generating a structured draft, scanning for secrets and environment coupling, recording review/eval evidence, and exporting a package that can then be registered through the normal runtime path.

Auto Deposition Mode

Auto deposition is intended for a personal assistant that should learn from a user-approved conversation or repeated workflow with one action. When enabled, the Page One-click Deposit action and the skill_auto_deposit tool will:

Create a candidate from the provided evidence.
Generate a structured Skill draft.
Run deterministic risk scanning.
Apply the configured risk policy.
Automatically record an approval and export the package when allowed.

The mode still does not directly publish into the runtime registry. The result contains a register_skill_hint so the assistant can write the package under /workspace/<skill-name> and call LangBot's built-in register_skill tool.

The plugin can also learn passively from completed LangBot turns. When both auto_deposition_enabled and post_response_candidate_enabled are enabled, an EventListener watches NormalMessageResponded, reads the current user_message_text, assistant response_text, function names, and available query vars, then creates a candidate only when deterministic confidence is high or the user explicitly says things like "沉淀一下", "记住这个流程", or "make this a skill".

Post-response extraction is conservative:

default off, even when the plugin is installed
private/personal-assistant sessions only by default
candidate-only by default
optional auto-export still obeys auto_deposition_policy
no automatic runtime register_skill
no automatic LongTermMemory write

Post-response config:

post_response_candidate_enabled: enables candidate creation after normal replies, default false.
post_response_auto_export: approve and export low-risk candidates automatically, default false.
post_response_private_only: restrict to private chats, default true.
post_response_min_confidence: deterministic threshold, default 0.72.
post_response_max_source_chars: maximum copied turn text, default 6000.
post_response_explicit_only: require an explicit deposition phrase, default false.

The response includes explicit disclosures:

Risks: over-generalizing one-off workflows, preserving private context, leaking secrets or local paths, and carrying prompt-injection text forward.
Cost: source size, plugin storage writes, optional package export writes, runtime changes, and whether LLM calls were used. The current deterministic MVP uses zero LLM calls.

Config:

auto_deposition_enabled: master switch, default false.
auto_deposition_policy: pass_only, allow_warn, or allow_blocked.
auto_deposition_reviewer: reviewer label for automatic approval records.
post_response_candidate_enabled: post-response candidate extraction, default false.
post_response_auto_export: automatic post-response review/export, default false.
retention_deprecate_score: lifecycle score below which deprecation is recommended.
retention_archive_score: lifecycle score below which archival is recommended.

Lifecycle And Retention

Hermes-style learning needs provenance and recoverable forgetting, not just writing. This plugin tracks a lightweight lifecycle for deposited Skill candidates:

candidate -> active -> deprecated -> archived
                  \-> superseded

Every candidate now carries provenance metadata:

origin: manual, auto_deposition, agent_review, imported, runtime_registered, etc.
protected: protected candidates cannot be deprecated, archived, or superseded unless an explicit operator action passes force=true.
auto_curation_eligible: only agent/auto-created candidates are eligible for automatic lifecycle recommendations to be applied later.

Lifecycle events can be recorded through the skill_lifecycle_manage tool or the Page API:

positive signals: used, success, positive_feedback, eval_pass
negative signals: failure, negative_feedback, eval_fail, stale, security_issue, memory_conflict, superseded

The retention evaluator computes a score and recommends keep, deprecate, archive, or superseded. Reports include auto_apply_allowed so a future curator can distinguish "safe to apply automatically" from "human review needed". Applying an action only changes this plugin's governance record; deleting or hiding a runtime Skill still needs LangBot's existing Skill management path or a future admin proxy.

Exported packages include:

SKILL.md
references/source-excerpt.md
references/risk-report.json
references/provenance.json
references/learning-decision.json
references/support-files.json

This mirrors the Hermes pattern of keeping session-specific detail in support files instead of flattening every one-off run into a narrow Skill.

LongTermMemory Coordination

Use LongTermMemory and Skill Authoring as separate asset layers:

LongTermMemory L1: stable profile facts and preferences.
LongTermMemory L2: situational memories, decisions, events, and correction history.
Skill Authoring: reusable procedures that need tools, sequencing, guardrails, and verification.

The skill_lifecycle_manage tool supports memory_plan for a candidate. It classifies whether the source should primarily become a Skill, an L1 profile update, an L2 episodic memory, or require manual review. When both apply, keep the executable workflow as a Skill and store only a compact source or usage summary in L2 memory.

The response includes a machine-readable learning-decision/v1 object and optional LongTermMemory tool suggestions. This plugin does not directly call LongTermMemory; the agent or a future host-level workflow can apply the suggested update_profile or remember call after user review.

When LongTermMemory is installed, its _ltm_context query var is preserved as provenance summary for post-response candidates when available. That gives the reviewer session/speaker context without coupling the two plugins or duplicating memory writes.

Hermes Parity Status

Implemented in this plugin:

gated one-click deposition with risk/cost disclosure
LangBot-native post-response candidate extraction from NormalMessageResponded
provenance, protection, and auto-curation eligibility
lifecycle scoring with recoverable archive/deprecate/supersede records
export packages with support-file manifests
shared learning decision output for LongTermMemory coordination

Not implemented inside this plugin:

Hermes' post-turn background fork that reviews the full conversation
prompt-cache-aware auxiliary model routing
complete tool-result trace review beyond the current event/query vars
direct runtime Skill archive/delete/restore
automatic umbrella consolidation across all installed runtime Skills

Those need stable LangBot host APIs for richer run traces, cross-plugin calls, runtime Skill provenance, and recoverable runtime archive/restore. They are not required for the current LangBot-native candidate loop.

The pipeline refactor follow-up plan is documented in docs/pipeline-host-integration-plan.md.

What Works In This MVP

Page backend API for candidates, reviews, validation, and export.
Admin-facing Page UI for drafting and reviewing candidates.
LLM-callable tools:
- skill_auto_deposit
- skill_lifecycle_manage
- skill_candidate_create
- skill_candidate_risk_check
- skill_candidate_export
Deterministic SKILL.md generation with source refs and risk notes.
Local plugin storage when running inside LangBot, with an in-memory fallback for tests and offline development.

Repository Layout

manifest.yaml
main.py
components/
  event_listener/
  pages/authoring/
  tools/
skill_authoring/
tests/

Development

From this repository:

python -m pytest

Build with the LangBot plugin SDK CLI when available:

lbp build

Inside a LangBot run, an Agent can still create the final runtime Skill using the built-in register_skill tool after writing the exported package under /workspace. The Page backend can also be extended to call /api/v1/skills with an admin API key when a deployment wants direct publishing from the review screen.

Skill Authoring

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