Context Budget
Use when a coding session is burning context fast, output is drowning in noise, the model keeps forgetting earlier decisions, or the user asks to reduce tokens, stop the verbose narration, work in a huge repo, or make a long session survive. Covers what to read, what to summarize, what to write to disk, and when to start fresh.
What it does
# Context Budget Context is the scarcest resource in a long session. It gets spent on three things: files you read, output you generate, and narration nobody asked for. Only the first one is usually worth it. ## Core Behavior Treat the context window like a budget with a balance. Before any expensive action — reading a big file, running a chatty command, dumping a directory tree — ask what the cheapest way to get the same answer is. ## Spend Rules **Read narrowly.** `sed -n '120,190p' file.ts` beats reading a 2,000-line file to see one function. Grep for the symbol, then read the 40 lines around it. Read a whole file only when you are about to restructure it. **Search before reading.** `grep -rn "symbolName" --include="*.ts"` costs a few hundred tokens and tells you which of forty files matters. **Silence the noisy commands.** Pipe installs, builds, and test runs through a filter instead of dumping them whole: ```bash npm test 2>&1 | tail -30 npm run build 2>&1 | grep -E "error|Error|warning" | head -20 npm install --silent 2>&1 | tail -5 git diff --stat # not git diff, unless you need the hunks ``` A passing test suite needs one line of proof, not 400. **Write instead of remembering.** Anything that must survive — a plan, a decision, a list of files to touch — goes in a file on disk. Files are re-readable at a cost you choose; context is not. **Do not re-read what you just wrote.** If an edit succeeded, it succeeded. Re-reading to "verify" is pure spend. **Cut the narration.** No preamble, no recap of what was just shown, no bulleted summary of a diff the user can see. Say what changed and what is next, in a line or two. ## Where the Budget Actually Goes When a session bloats, it is almost always one of these: | Leak | Fix | |------|-----| | Whole-file reads for one function | grep, then a line-ranged read | | Full `npm install` / build logs | `\| tail -n` or grep for errors | | Directory listings of `node_modules`, `dist`, `.next` | prune them in the find/ls | | Re-reading files after editing | trust the edit result | | Long explanations of finished work | one line | | Pasting a file back to show a small change | show the diff hunk only | | Repeating the plan every turn | plan lives in a file | ## Compaction Points When roughly two-thirds of the window is gone, stop adding and start consolidating: 1. Write the current state to a plan or handoff file on disk. 2. State the single next action. 3. Tell the user a fresh session will be faster and more accurate than continuing. A fresh session that reads a good plan file outperforms a stuffed session every time. Do not treat starting over as failure — it is the intended move. ## Large Repos - Map before you dig: directory names and entry points first, implementation later. - Follow imports from the entry point rather than crawling folders alphabetically. - One subsystem at a time. Finish it, write down what you learned, move on. - Generated code, lockfiles, and snapshots are never worth reading. Exclude them by default. ## What Not to Cut Economy has a floor. Never skip: - Reading the actual code before changing it. - The error output when something failed — that is the one long dump worth having. - The user's own requirements, quoted. Being cheap about the wrong thing produces confident, wrong work. The goal is fewer tokens, not less evidence.
More agent workflow skills
Agent Eval Harness
Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
AI Coding Guardrails
Use when an AI coding agent is writing or refactoring production code and its known failure modes need to be held in check — over-engineering, silent scope creep, invented APIs, claiming work is done without checking, deleting tests to make them pass. Trigger when the user asks for careful, production-grade, or reviewed code, or when a previous agent run produced confident output that turned out wrong.
Claude Md Writer
Use to create or clean up the CLAUDE.md (or AGENTS.md) file that tells a coding agent how to work in a repo — commands, conventions, architecture, and the traps. Trigger when onboarding an agent to a codebase, when the agent keeps making the same repo-specific mistake, or when the user asks for project instructions, agent rules, or a context file.
Fresh Library Docs
Use before writing code against a library, SDK, API, or framework whose current version you are not certain of — especially fast-moving ones. Trigger when an import fails, a method does not exist, a config key is rejected, a deprecation warning appears, or the user says the code was written against an old version.