mcpbeat Sign in

Session Learn Agent Skill

Use when a completed work session should yield durable concepts, corrections, decisions, reusable patterns, and a traceable next action.

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
132
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/Mark393295827/third-brain-v7-skills --skill session-learn

The instruction itself

8 sections, as written by the author

Session Learn

<skill_contract>

<input>Completed session objective, actions, artifacts, verification receipts, errors, decisions, and destination paths.</input>

<output>Deduplicated durable deltas for concepts, entities, corrections, patterns, ideas, decisions, and gaps.</output>

<done>Each retained delta is formatted, linked, logged, provenance-preserving, and paired with a traceable next action when unresolved.</done>

<non_goals>Automatic-capture claims without a hook, raw transcript storage, conversational filler, or immutable-source edits.</non_goals>

Close a session by extracting only reusable deltas. Invocation must be explicit or performed by a verified external hook; this skill never claims it ran automatically.

Usage Template

Provide: session objective, actions, outputs, verification receipts, errors, decisions, and destination paths. Optional: existing notes for deduplication.

Workflow

<intake>

Establish the session boundary and compare intended versus observed result. Ignore conversational filler and separate execution evidence from retrospective interpretation.

</intake>

<unknowns_gate>

If the session outcome or evidence is unavailable, return INSUFFICIENT_EVIDENCE. Ask for a missing artifact only when it determines whether a lesson is valid; otherwise preserve it as an unresolved gap.

</unknowns_gate>

<execute>

Scan for seven signal types:

  • Concept — a stable mechanism worth linking.
  • Entity — a person, system, project, or tool requiring durable context.
  • Correction — a prior belief or procedure disproved by evidence.
  • Pattern — a reusable Trigger -> Execute -> Verify -> State sequence.
  • Idea — an untested possibility, explicitly provisional.
  • Decision — choice, rationale, alternatives, owner, and review condition.
  • Gap — an unknown with a probe or escalation path.

Apply Closure Protocol: Format the smallest durable note, Link it to source/project/concepts, and Log the write plus verification. Merge semantic duplicates; preserve source immutability and provenance.

</execute>

<evaluate>

For each candidate ask: Is it new, reusable, traceable, and decision-relevant? Reject session-specific trivia and unsupported generalizations. Confirm each write exists and links resolve before reporting closure.

</evaluate>

<state_contract>

Persist {run_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus session id, extracted candidates, accepted/rejected reasons, write paths, links, and closure receipt. Append corrections; do not erase superseded beliefs.

</state_contract>

Failure Protocol

  • INSUFFICIENT_EVIDENCE: artifacts cannot establish the lesson; preserve it as a gap.
  • BLOCKED_PERMISSION: the destination is read-only; return the proposed note and path without claiming a write.
  • VERIFY_FAILED: a write or link check fails; repair once or return the exact failure.
  • BUDGET_STOP: rank remaining candidates by reuse value and persist the queue.

Output Contract

Return status, result (accepted knowledge deltas), evidence (session receipts and write checks), unknowns, and next_action.

Edge Cases

  • The same concept already exists: update or link the durable note; do not create a title variant.
  • The session ended unsuccessfully: capture the falsified assumption, failure evidence, and recovery path rather than a false success pattern.

Success Metrics

  • Every accepted learning is traceable to session evidence.
  • Durable notes are deduplicated, linked, and verified after write.
  • At least one correction, decision, pattern, or gap improves future execution when present.

Quality Gates

  • [ ] Invocation provenance is explicit; no fake auto-trigger claim.
  • [ ] Facts, interpretations, and ideas are distinguishable.
  • [ ] Source material remains immutable.
  • [ ] Closure includes Format, Link, Log, and a receipt.

</skill_contract>

Other skills for the same job

different authors, same section of the catalogue
Declarative Agents
by github
vendor ×1

Complete development kit for Microsoft 365 Copilot declarative agents with three comprehensive workflows (basic, advanced, validation), TypeSpec support, and Microsoft 365 Agents Toolkit integration

1k tokens
Treatment Plans
by K-Dense-AI
×1

Format and structurally validate local treatment-plan documentation after clinical decisions have already been supplied and verified by authorized licensed professionals. Use for source traceability, clinician-authored intervention records, goals and checkpoints, shared-decision records, reconciliation handoffs, and release gates—not for clinical decision-making.

38k tokens scripts
Okx AI
by internet-court
×1

> provider/change budget/修改卖家/修改预算/draft/草稿/我的任务/my tasks/what am I working on/关闭/取消任务/决策列表/decision list/指定服务商/browse (sender.role = COUNTERPARTY, not you); (3) literal "Read the okx-ai skill" (or legacy "Read the okx-agent-task skill") in the envelope.

57k tokens
Prior Auth Review Skill
by anthropics
vendor ×1

Automate payer review of prior authorization (PA) requests. This skill should be used when users say "Review this PA request", "Process prior authorization for [procedure]", "Assess medical necessity", "Generate PA decision", or when processing clinical documentation for coverage policy validation and authorization decisions.

23k tokens
AI Agents Architect
by lingxling
×1

Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.

2k tokens
Autonomous Agents
by lingxling
×1

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.

7k tokens
Design Orchestration
by lingxling
×1

Orchestrates design workflows by routing work through brainstorming, multi-agent review, and execution readiness in the correct order.

959 tokens
Pitchcraft
by moshuying
×1

Structured persuasion for tech leads, PMs, and founders—not activity logs. Five scenarios (kickoff, status update, wrap-up, investor pitch, solution selling) on one 5-part framework (Hook→Context→Proposal→Evidence→Ask). AI prompts for missing materials and audience context; pre-submit checklist. Claude Code plugin; Cursor, Codex, and chat via prompts.

5k tokens

How to use it

Copy the folder

Take mark393295827/session-learn from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.