Every system prompt in the repo: contradictions, dead instructions, bloat, and injection surface — with a consolidation plan.
Free & open · no signup · read-only — it ends by asking · nothing leaves your machine
You are working inside this repo. Mission: audit every prompt this product sends to a model — system prompts, templates, few-shot examples, inline instructions — for contradictions, rot, bloat, and injection risk.
This audits prompt quality. For the defensive guard architecture around the agent, run 35; to attack the product's AI like an adversary, run 118.
Read-only pass. Your only write is the report file.
Phase 2 sweeps the codebase through every one of these, citing file and line for each finding.
instruction A vs instruction B; prompt rules vs tool descriptions; system vs few-shot behavior
rules about features, formats, or tools that no longer exist
repeated emphasis, ALL-CAPS inflation, boilerplate the model demonstrably ignores; tokens paid on every call
"be helpful, be accurate" vs testable instructions with defined behavior
downstream parsing assumes structure the prompt never actually demands
few-shots contradicting current rules or demonstrating retired formats
user or retrieved content interpolated without delimiters, fencing, or role separation
the same rule copy-pasted across prompts, each copy drifting independently
One structured report at the repo root — or in reports/, if you keep one — the same shape every time, ready for a teammate — or the optional Studio — to act on.
Every Goal Prompt follows the same four steps, so results are consistent and repeatable — no matter which one you run.
Locate them all.
Cite the file and line for every finding.
Rank by blast radius.
Create PROMPTS.md at repo root.
Copy it and paste it into your agent inside the repo you want checked.
Install the goal plugin once — two commands — then just type /goal:prompt-instruction-audit.
/plugin marketplace add GhostlyGawd/goal-prompts/plugin install goal@goal-promptsOr install only this Goal Prompt as /goal-prompt-instruction-audit:
curl -fsSL https://goal-prompts.vercel.app/install | BRIEF=30 shLet an agent fetch it mid-conversation, or pull the raw Goal Prompt by URL.
https://goal-prompts.vercel.app/raw/30.mdNothing hidden — this is the whole Goal Prompt, verbatim. Read it in a minute, edit it, or copy it as-is.
# Goal: Prompt & Instruction Audit You are working inside this repo. Mission: audit every prompt this product sends to a model — system prompts, templates, few-shot examples, inline instructions — for contradictions, rot, bloat, and injection risk. This audits prompt quality. For the defensive guard architecture around the agent, run 35; to attack the product's AI like an adversary, run 118. Read-only pass. Your only write is the report file. ## Phase 1 — Find every prompt - Locate them all: prompt files, template strings, f-strings in code, few-shot examples, instructions concatenated at call sites. - For each: which model receives it, at what temperature, for what job, roughly how many tokens. - Are prompts versioned and diffable, or scattered string literals? ## Phase 2 — Audit through 8 lenses Cite the file and line for every finding. 1. **Contradictions** — instruction A vs instruction B; prompt rules vs tool descriptions; system vs few-shot behavior 2. **Dead instructions** — rules about features, formats, or tools that no longer exist 3. **Bloat** — repeated emphasis, ALL-CAPS inflation, boilerplate the model demonstrably ignores; tokens paid on every call 4. **Vague directives** — "be helpful, be accurate" vs testable instructions with defined behavior 5. **Format gaps** — downstream parsing assumes structure the prompt never actually demands 6. **Example rot** — few-shots contradicting current rules or demonstrating retired formats 7. **Injection surface** — user or retrieved content interpolated without delimiters, fencing, or role separation 8. **Sprawl** — the same rule copy-pasted across prompts, each copy drifting independently ## Phase 3 — Curate - Rank by blast radius: prompts on the hot path outrank one-off utilities - Every claim of "the model ignores this" needs a mechanism or trace evidence, not vibes ## Phase 4 — Report Create `PROMPTS.md` at repo root: 1. **Prompt inventory** — location · model · job · est. tokens · version-controlled? 2. **Findings** — each: issue · lens · evidence · fix · risk of changing it 3. **Consolidation plan** — shared rules extracted to one source, per-prompt deltas 4. **First rewrite** — the worst prompt, before/after sketch, and how to eval the change before shipping (see 34) Start the report with today's date. If `PROMPTS.md` already exists from a previous run, read it first and lead with what changed since. ## Rules - Prompt changes are behavior changes: every fix names its eval or rollback plan - Shorter and testable beats longer and reassuring - No prompts or agent instructions in this repo? Say so in a one-paragraph null report and stop — a null result is a valid finding. - If a `reports/` directory exists at the repo root, write the report there instead of the root. - Before asking, present the top findings as a ranked list in plain words - Report only — end by asking which rewrites to make
reports/ directory exists at the repo root, write the report there instead of the root.Curated neighbors — briefs that answer the adjacent question, worth running in the same session.
A defensive review of your agent's blast radius — injection paths, unvalidated outputs, over-broad permissions, ungated irreversible actions.
Attack the product's own AI like an adversary — make it ignore instructions, leak secrets, or misuse tools — and find where untrusted input can hijack it.
Your tools are an API whose consumer is a model — audit names, descriptions, error contracts, and guards on destructive actions.
How runs end — iteration caps, stop conditions, stuck-state detection, and the true cost of one pathological run.
Reconstruct what actually enters the model's window each step — stale payloads, token hogs, retrieval junk, and buried instructions.
The test-gap audit for nondeterminism — golden sets, regression evals, judge quality, and the drift between eval and production.
Runs inside these playbooks — curated sequences you can launch with one paste: