self-improvement

name

self-improvement

description

Codify lessons from a completed task on two channels at once — a draft pull request on the project's own content or tooling, plus a detailed anonymized issue on the seasoned-skills package — with every PR marked ready only by the user. Use when a task has met every other Definition of Done criterion, when a /goal goal is met but not yet marked complete, or when the user asks to derive lessons from recent work.

Self-Improvement

The last step of every task: mine what just happened for durable lessons and codify the ones that earn it. This is the most valuable and most dangerous part of the process — a good instruction improves every future session; a bad one biases every future session. Generalize with the big picture in mind, and codify only with conviction.

When to run

• After every other Definition of Done criterion has passed, before declaring the task done.
• For /goal goals: one pass over the whole effort when the goal is met, before marking it complete. Individual tasks inside a goal skip the per-task pass — their lessons are mined at goal completion, with the full arc in view.
• "No lessons worth codifying" is a valid and common outcome, especially for small tasks — state it explicitly and finish. Never manufacture a lesson to look productive.

Source ranking

Weigh evidence in this order:

1. User instructions, corrections, and feedback — the highest signal, outranking everything else. Anything the user had to say twice is something the system failed to absorb the first time: a top-priority candidate. A lesson that contradicts a stated user preference is wrong, no matter how much other evidence supports it.
2. Failures with a root cause — an incident traced to its mechanism. An untraced failure is not a lesson yet.
3. Wins — patterns that demonstrably worked and would not be rediscovered cheaply.

Mine the real record, not memory: the session ledger, the conversation (the user's messages above all), per-lane artifacts, PR review threads.

Where each source lives:

• The session ledger — in the scratchpad directory listed in the system prompt.
• The full conversation — a JSONL transcript under the Claude config directory at projects/<cwd with slashes replaced by dashes>/<session-id>.jsonl; the current session is the most recently modified file there, and compaction summaries cite the exact path. Long sessions grow to hundreds of megabytes — never read one wholesale. Extract layers with a script filtering by role: user messages and hook feedback first (highest signal, smallest volume), assistant narration only if needed, then slice for miners.
• Per-lane artifacts — subagent transcripts live beside the session transcript; lane ledgers live in the scratchpad.
• PR review threads — gh pr view <number> --comments and gh api repos/<owner>/<repo>/pulls/<number>/comments.

The codification bar

Codify a lesson only if all of these hold:

• Durable: it will matter beyond the task that taught it. Task-specific trivia dies with the task's ledger.
• Behavior-changing: a future agent reading it would act differently, and better.
• Earns its tokens: skills load into every relevant context; each sentence costs attention forever.

Always ask whether there is a deeper fix. A recurring trap is better eliminated in code — a setup script, a lint rule, a test harness — than warned about in prose. The best instruction is the one made unnecessary.

Reverify every staleness claim against the system of record at its current tip before codifying. A lane reporting a doc, target, or feature missing is evidence about that lane's base commit, not about the default branch — lanes based on an older branch can produce "stale reference" candidates that are false on current main.

The structural lens

Incident mining looks backward at what failed; it cannot see the gaps an effort's own success created. After ruling on the incident lessons, ask a second question: did this effort change the shape of future tasks — a new Definition of Done criterion, a new platform, a new product surface, or a workflow agents will now repeat? If it did, check that the skill surface matches the new shape. Facts documented in a README are findable when read but not discoverable at the moment of need, and a workflow future agents will repeat on every task deserves a trigger-discoverable skill, not just documentation.

Then ask a third: did the effort change a mechanism, command, or file that a skill in .claude/skills describes? Nothing type-checks a skill and no test reads one, so a skill's description becomes false the moment its mechanism changes, and a stale skill misleads future agents more confidently than no skill at all. Grep the skills for whatever the effort touched, and true up every statement the work made false before the pass closes. When the Definition of Done has grown since the calibrations in workflow-content/calibrations.md were measured, its numbers understate what a stage now costs — recalibrate them in this pass from the effort's own measured context windows.

Leanness and removal

Removing instructions is often more valuable than adding them. Skills bias agents: an instruction written for one context misfires in others, and an agent follows a wrong instruction more confidently than no instruction. On every pass, look for existing content that is stale, redundant across skills, or prescriptive enough to push an agent into wrong behavior — and propose its removal with the same rigor as an addition.

Judgment is never delegated

Subagents may mine a long effort's record and return candidates, but every codification ruling is made at the orchestrator tier, after personally reading the candidate and its evidence. Never codify on a miner's word alone.

Two channels

Every lesson that clears the bar travels on two channels at once — never a choice between them:

1. A pull request on the project. Solve the problem locally: land the lesson in the affected file — the project's own committed material, or, for a lesson about a generated skill, that skill's content file under workflow-content/. Never edit a generated skill itself: the generated text is rebuilt on every upgrade, and the edit dies with the next regeneration.
2. A detailed issue on the workflow package — seasonedcc/seasoned-skills — always an issue there, never a pull request. The issue carries the full scenario that suggested the improvement, anonymized to the same standard as everything else repository-visible, with suggested changes marked as exactly that: suggestions. The issues are the package's demand records; the package changes only through its own shaping process, never by applying a consumer's request verbatim.

The only exception is a lesson so obviously project-specific that it will never benefit another project — that one stays a local pull request alone. And when a fragment of the project's content turns out to be true of every project, that is a package demand like any other: file it as an issue so it can be shaped into the package's generic text, leaving the content file thinner.

Writing rules

• Follow the skill-management conventions: frontmatter, trigger-rich description, lean body.
• Use plain words a person would say out loud. If a teammate reading a sentence cold would have to ask what it means, it is not a lesson yet — rewrite it from the concrete case it came from.
• Write every instruction as a standalone statement of current policy. Never write a delta against history ("previously X, now Y") — the future reader has no such history.
• When a fact is an instance of a growing set, name the mechanism that produces it and how to enumerate it, not the instances — a hardcoded list is stale by the next addition, while a mechanism plus its grep stays true.
• Match the target file's voice and structure, and place a lesson in the one file where a future agent will look for it — never in two.
• Lessons about an application repository's code shape — organization, query boundaries, model/service responsibilities, test structure, naming — belong in that repository's own conventions document, delivered as a PR against that repository. Do not accrete equivalent rules into workflow skills.

Delivery

• One coherent PR per independent improvement; changes that belong together travel together.
• An improvement PR is almost always documentation-only, so it takes the documentation-only path from CLAUDE.md's Definition of Done: a self-improvement/<topic> branch with no provisioned worktree, the fast gates as the only local step, one review pass, then the PR. An improvement that changes code or tooling instead follows the full Definition of Done.
• Open every self-improvement PR as a draft, on every repository. Only the user marks it ready: a workflow change is doctrine about how a team works, and no change to the way of working reaches teammates before the user has read it. The rule carries no team-or-solo judgment to make — on a solo repository it costs nothing, since only the user could flip it anyway.
• NEVER merge these PRs. The user personally reviews and merges each one — codification is a user decision, and these PRs are exempt from the usual merge flow.
• Each PR body states the lesson, the evidence (what happened, where), and why it clears the codification bar.

Where lessons go

Project-empirical lessons about this skill land in workflow-content/self-improvement.md through a pull request on the project — never by editing this file, which is regenerated on every upgrade. A lesson that turns out to be true of every project travels as an issue on the workflow package instead.