Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/fergius-engineering/instincts/verify-against-codenpx skills add Fergius-Engineering/instincts --skill verify-against-codegit clone --depth 1 https://github.com/Fergius-Engineering/instinctsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00040 | $0.00475 |
| Opus 5 | $0.00020 | $0.00237 |
| Sonnet 5 | $0.00008 | $0.00095 |
| Haiku 4.5 | $0.00004 | $0.00047 |
Grade A, and why
verify-against-code scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
The rule
Before you state a fact about what the code or product does, go read the thing that proves it. Your memory, the user's framing, a plan that says "done", a table in the docs — all of these go stale, and stating one as fact when it's wrong costs more than the thirty seconds it takes to check.
Fires when
Answering "does it do X?", writing a doc or README line, writing a commit or PR body, shipping anything outward-facing (store copy, a caption, a before/after), agreeing with a claim the user just made about the code.
How to apply
For each claim, find the concrete artifact that proves it — the symbol, the field, the default value, the line — and read it. Cite what you read as the evidence, briefly.
"It doesn't actually do that" and "only partially" are valid, valuable answers. Say so plainly and surface the conflict instead of going along with the assumption.
Hold this bar even when nobody asked you to check. Especially when nobody asked.
Worked example
A user asks: "Our API trims whitespace from usernames before saving, right?" You remember writing something like that. The cheap move is "yes." The right move: open the save path and read it. You find username.toLowerCase() but no .trim(). The honest answer is "It lowercases, but it does not trim — leading spaces are saved as-is." That gap is exactly the bug the user was about to assume away. The thirty-second read changed a confident wrong answer into a real finding.
Red flags
| Thought | Reality |
|---|---|
| "I'm pretty sure it does that" | Sure isn't read. Open the file. |
| "The plan says it's done" | Plans go stale. The code is the truth. |
| "The user said it works that way" | The user can be wrong about their own system. Check. |
| "It's just a doc line, not code" | A wrong doc line is a wrong claim shipped to every reader. |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 34 lines · 40 tokens per session scan A d74f2be9f7d5
verify-against-code is a skill published in the GitHub repository Fergius-Engineering/instincts (2 stars, last pushed 7d ago), licensed MIT. It adds 40 tokens to every session and 475 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
rulesync
Generates and syncs AI rule configuration files (.cursorrules, CLAUDE.md, copilot-instructions.md) across 20+ coding tools from a single source. Use when syncing AI rules, running rulesync commands, importing or generating rule files, or managing shared AI coding configurations.
establishing-project-context
Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.
autoprompt
Explicit-only useful-first orchestration. Invoke /autoprompt to turn a mission into one executable roadmap, build dependency-safe lanes, and verify the result with independent reviewers. Never infer invocation from ordinary requests. Never resume from leftover artifacts without an explicit resume instruction.
office-hours
YC Office Hours — two modes. Startup mode: six forcing questions that expose demand reality, status quo, desperate specificity, narrowest wedge, observation, and future-fit. Builder mode: design thinking brainstorming for side projects, hackathons, learning, and open source. Saves a design doc. Use when asked to…
memstack-business-gdpr
Use this skill when the user says 'GDPR', 'data protection', 'privacy compliance', 'DPA', 'DSAR', 'data subject request', 'cookie consent', 'privacy audit', 'CCPA', or asks 'do I need GDPR for this repo'. Scans the repository to detect what personal data is collected, classifies sensitivity, determines whether GDPR…
echo
Use when the user references past sessions, asks 'what did we do', 'do you remember', 'last session', 'recall', or 'continue from'.