context-surfing
Monitors context window health during large, long-running, multi-session, or explicitly…
Active runtime recovery for coding agents: when something breaks mid-task, diagnose the root cause, write a fix, VERIFY by re-running the broken thing, then file a `HEAL-` entry to `.learnings/HEALS.md` with proof. Use whenever a command, test, build, or lint fails or exits
$ npx -y skills add pskoett/pskoett-ai-skills --skill self-healing --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/self-healingContext preview
The summary Claude sees to decide when to auto-load this skill.
Active runtime recovery for coding agents: when something breaks mid-task, diagnose the root cause, write a fix, VERIFY by re-running the broken thing, then file a `HEAL-` entry to `.learnings/HEALS.md` with proof. Use whenever a command, test, build, or lint fails or exits
name: self-healing description: "Active runtime recovery for coding agents: when something breaks mid-task, diagnose the root cause, write a fix, VERIFY by re-running the broken thing, then file a `HEAL-` entry to `.learnings/HEALS.md` with proof. Use whenever a command, test, build, or lint fails or exits non-zero; on missing tooling, dependency/lockfile mismatch, wrong runtime version, venv or permission errors, port conflicts, dirty git state, or a missing `.env`; when the agent needs a helper or one-off script that doesn't exist yet; when an external API, tool, or MCP errors or rate-limits; or when a test flakes. Search `HEALS.md` by `Pattern-Key` first — most heals are recurrences, so increment `Recurrence-Count` instead of duplicating. Verify is mandatory: mark `pending-verify` honestly if sandboxed, `abandoned` if the fix can't be made to work. Pairs with `self-improvement` (which promotes recurring heals to durable memory) but owns the verify-before-persist discipline self-improvement doesn't."
Active runtime recovery for coding agents. When something breaks, run the loop: **diagnose → patch → verify → file**. Leave behind a reusable, verified artifact instead of a swept-under-the-rug failure.
The premise mirrors [browser-use/browser-harness](https://github.com/browser-use/browser-harness): *the harness improves itself every run*. An agent that hits a gap doesn't fail — it writes the fix during execution, verifies it works, and files the durable artifact for future runs. Coding tasks deserve the same loop.
When a coding agent hits a wall mid-task, the default failure modes are:
1. **Paper over it** — "let me try a different approach" — and lose the recovery 2. **Pretend the fix worked** — without re-running the broken thing 3. **Symptom-fix** — skip the test, swallow the error, retry until green
All three turn a one-time failure into a recurrence. The next agent on the same project hits the same wall.
This skill enforces one discipline: **verify before persist**. A patch isn't real until you've re-run the failing operation and watched it succeed. When it does, file the verified fix so the next run benefits.
These two skills are deliberately split. Run both — they feed each other but don't overlap.
| Aspect | `self-healing` (this skill) | `self-improvement` | | ----------- | -------------------------------------------------------------------- | ------------------------------------------------------------- | | **When** | During execution, failure is live | After the fact, at natural breakpoints | | **Verb** | Heal now — restore working state | Remember for later — accumulate knowledge | | **Outcome** | Verified patch + (optional) reusable artifact | Logged learning, correction, request | | **Verify** | **Mandatory** — no persist without proof | Not required | | **Files** | `.learnings/HEALS.md` + `.learnings/heals/<HEAL-ID>/` (lazy) | `.learnings/ERRORS.md`, `LEARNINGS.md`, `FEATURE_REQUESTS.md` | | **Trigger** | Failure observed mid-task | Correction, knowledge gap, feature request, recurrence |
**Boundary rule:** if you're capturing a fact, a correction, or a wish — that's `self-improvement`. If you're applying and verifying a fix to a live failure — that's `self-healing`.
● failure observed
│
● 1. DIAGNOSE capture context — command, error, env, what was attempted
│ search HEALS.md for the same Pattern-Key first
│ (most heals are recurrences; don't reinvent)
│
● 2. PATCH write the fix — script, helper, env tweak, alt command
│ artifacts → .learnings/heals/<HEAL-ID>/ (only if needed)
│
● 3. VERIFY re-run the failing op — must succeed
│ ↻ if still failing: refine and retry, cap at 3 attempts
│ ✗ if uncrackable: file Status: abandoned with notes
│
● 4. FILE write HEAL-YYYYMMDD-XXX to .learnings/HEALS.md
│ with Pattern-Key, status, verification proof
│
✓ working state restored, heal persisted
(conditional) PROMOTE if Pattern-Key recurrence ≥ 3 across distinct tasks,
append a Handoff block → self-improvement promotes to memoryIf you abandon a heal mid-loop, don't pretend it succeeded. File a `HEAL-` entry with `Status: abandoned` and notes on what didn't work. The next agent learns from the dead end too.
Self-healing fires on **active failures during execution** — the agent has just observed something not working and needs to make it work to continue. Five shapes:
Any invocation exits non-zero or produces wrong output. Don't acknowledge and retry verbatim — diagnose, patch, verify.
*Examples:* `npm install` errors when a `pnpm-lock.yaml` is present (switch tool); `pytest` fails with `ModuleNotFoundError` (activate the venv); `tsc` flags a stale type (regenerate the client); `eslint` reports a config error (install the missing parser).
The agent needs something that doesn't exist yet — a script, a helper, a wrapper, a glue function. Write it in the moment. This is the closest analog to browser-harness's `agent_helpers.py`.
*Examples:* dedupe a CSV by custom key (write a small Python helper); bootstrap 12 microservices the same way (write `scripts/bootstrap-all.sh`); bulk-rename branches matching a pattern (write a `gh`-based shell helper).
The local environment isn't what the project
A collection of skills for AI agents. Follows the Agent Skills specification and ships an Agent Plugins 1.0 portable package. This repository is my personal skill testing ground.
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