Zach prompting
Calibrated decision measurement skills for AI agents.
npx -y skills add ThatGuySam/skills --skill zach-promptingAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 26 days oldThe repository was created 26 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Improves and refactors prompts, system instructions, skills, agent definitions, tool descriptions, CLAUDE.md, and AGENTS.md files by preserving intent while removing redundancy, contradictions, over-prescription, and obsolete scaffolding. Use when reviewing, rewriting, shortening, migrating, or debugging an instruction artifact; defining outcomes, success criteria, evidence, permissions, tools, output, stop rules, autonomy, or long-run behavior; or adapting prompts to GPT-5.6 or Claude Fable 5.
SKILL.md
6.4 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it
Zach Prompting
Goal
Produce the smallest justified revision that makes an instruction artifact easier for an agent to follow without weakening its outcome, evidence, safety, permission, output, or validation contract.
Treat brevity as a means, not the success metric. Call a change an improvement only when its behavior is verified on representative work.
Select the mode
- Review: Inspect and report findings. Do not edit when the user asked only for assessment.
- Rewrite: Return the revised artifact or apply an in-scope edit when the user asked for a change.
- Debug: Use real failures or traces to find a likely instruction-level cause, then propose or make a surgical change.
- Migrate: Establish the current model, runtime, prompt, tools, reasoning or effort setting, and eval baseline. Change one variable group at a time.
Infer the mode from the request. Ask only when choosing incorrectly would cause an unauthorized edit or materially different result.
Rewrite the contract
- Recover intent. Read the whole target plus governing instructions. Identify the audience, runtime, model, request type, and the larger purpose the artifact serves.
- Map what must survive. Record the user-visible outcome, success criteria, true invariants, evidence rules, authorization boundary, relevant tools, required output, validation, and stop conditions. Preserve explicit user values.
- Find behavioral friction. Locate contradictions, repeated rules, obsolete scaffolding, behavior-neutral examples, irrelevant tools, vague tone labels, unneeded absolutes, keyword shortcuts, hidden approval rules, unsupported claims, and missing completion criteria.
- Make a narrow revision. Lead with the outcome. Keep one rule per behavior. Use absolutes only for invariants; use decision rules for judgment. State safe autonomy and approval boundaries once. Add detail only where it changes behavior.
- Check the result. Compare the revision against the preserved contract. Run available representative evals or validation. For consequential or complex changes, prefer a fresh-context verifier over self-critique.
Do not force a small prompt into a large template. For complex artifacts, use this order when it helps: context, goal, success criteria, constraints, tools, output, stop rules.
Apply these editing rules
Outcomes and completion
- Describe the destination and completion bar before prescribing process.
- Retain required calculations, evidence, citations, fields, and validation even when shortening.
- Define when to retry, use a fallback, ask for missing input, abstain, or finish.
- End autonomous work only when complete or blocked on input only the user can provide.
Boundaries and autonomy
- Distinguish inspect/report requests from change/build requests.
- Permit safe, reversible, in-scope actions needed for an authorized change.
- Require a pause for destructive or irreversible actions, external writes not already authorized, purchases, real scope expansion, or user-only information.
- Keep research, design, implementation, review, and external coordination from silently bleeding into one another.
- Respect ownership and instruction hierarchy. For vendored, generated, protected, or out-of-scope artifacts, return a patch or bridge instead of modifying the source.
Evidence and progress
- Define which claims need support and what sufficient evidence means.
- Treat missing evidence as unknown, not as proof of absence.
- Separate retrieved fact, user-provided fact, inference, and creative wording.
- Ground progress and completion claims in current tool results. Report failed, skipped, unverified, and completed checks plainly.
- Never invent names, dates, metrics, outcomes, capabilities, citations, or test results to strengthen an artifact.
Tools and delegation
- Keep only task-relevant tools. State what each tool does, when to use it, important outputs, and meaningful failure behavior.
- Make prerequisites explicit. Parallelize independent reads; sequence dependent work; synthesize before acting.
- Use programmatic or batch tool calling only for bounded deterministic reduction. Keep approval, citations, semantic judgment, and final validation in direct model control.
- Delegate independent work when it improves speed or verification, but give each agent a bounded deliverable and enough source context to succeed.
Reader-facing communication
- Define tone with observable choices instead of labels such as “friendly” or “professional.”
- Lead final responses with the outcome. Preserve material facts, decisions, caveats, and next actions; remove repetition and optional background first.
- Prefer clear sentences over fragments, jargon, or dense shorthand.
- Do not ask the model to expose, reproduce, or transcribe private chain-of-thought. Request conclusions, evidence, assumptions, and concise rationale instead.
Keep vendor guidance conditional
Read references/vendor-guidance.md when the request names GPT-5.6, Claude Fable 5, model migration, reasoning or effort settings, long-run harness behavior, or source-backed rationale.
Do not copy model-specific settings into a general artifact unless the named runtime uses them. Prefer shared behavioral rules in portable skills and keep provider controls in scoped sections.
Return the result
Match the requested mode and native artifact format. Include only what helps the user evaluate the change:
- the revised artifact, applied diff, or review findings;
- a compact change map: preserved, removed, clarified, and conditionalized;
- unresolved conflicts or missing context;
- validation performed and observable results; and
- status:
validated,awaiting-evals,no-change-needed, orblocked.
Do not claim validated without real checks. If no eval can run, label the revision awaiting-evals and provide the smallest representative test set with explicit pass conditions.
Gives 0 of the 12 instructions most memory context skills give in ~1.2k tokens
Counted across 674 of the 847 authors here whose files we hold, read 2026-08-06
- inform the user when setup is completein 21 of 674, across 6 files
- confirm the draft with the user before writingin 21 of 674, across 6 files
- update the agent skills block in place if it existsin 21 of 674, across 6 files
- present findings to the userin 20 of 674, across 5 files
- write the three docs files from seed templatesin 20 of 674, across 5 files
- ask the user about each decision one at a timein 19 of 674, across 4 files
- edit CLAUDE.md if it existsin 18 of 674, across 3 files
- explore current repo statein 18 of 674, across 3 files
- do not overwrite user edits to surrounding sectionsin 18 of 674, across 3 files
- back up the original file before overwritingin 16 of 674, across 8 files
- keep the memory index under 200 linesin 15 of 674
- Provide actionable steps and verificationin 13 of 674, across 2 files
Said here and by no other author read
- Infer the working mode from the request
- Read the whole target plus governing instructions
- Map what must survive before editing
- Keep one rule per behavior
- Run available representative evals or validation
- Describe the outcome and completion bar before process
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.