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 commands/sandeepshekhar26/develop-anything/rungit clone --depth 1 https://github.com/sandeepshekhar26/develop-anythingWhat 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 | $0.00032 | $0.00461 |
| Opus 5 | $0.00016 | $0.00230 |
| Sonnet 5 | $0.00006 | $0.00092 |
| Haiku 4.5 | $0.00003 | $0.00046 |
Grade A, and why
run 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
Run auk end to end so the user gets rich, codebase-specific AI-context files from a single command. You (this agent) are the LLM that does the semantic pass — do not ask the user to copy-paste prompts anywhere.
1. Analyze (deterministic)
Run npx -y auk-develop generate --no-compile --emit-prompts in the project
root. This parses the code (tree-sitter), builds the import + call graph,
mines conventions, clusters files, writes .auk/rules.yaml and
.auk/graph.json, and emits .auk/prompts/enhance-rules-NN.md.
2. Deep pass (you)
Read every .auk/prompts/enhance-rules-NN.md. For each rule, open the cited
evidence files and rewrite the description to be specific to THIS codebase —
naming real directories, frameworks, types, and functions — plus a rationale
for why it exists and what breaks if violated. Be concrete and actionable.
Work through batches in parallel where practical. Write each
enhance-rules-NN.response.json to match the embedded schema, then run
npx -y auk-develop enhance --apply <file> for each.
3. Compile
Run npx -y auk-develop compile to regenerate CLAUDE.md, AGENTS.md,
.cursor/rules/*.mdc, .github/copilot-instructions.md, .windsurfrules,
.aider.conf.yml, and GEMINI.md from the enhanced rules.
4. Report
Tell the user how many rules were generated and enhanced, the project overview auk detected (stack, entrypoints, directory map), and remind them they can:
- run
auk verify(orauk verify --ci) to catch context rot over time, - run
auk graph --opento explore the dependency/call graph, - commit
.auk/rules.yaml,graph.json, and the compiled files.
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 · 39 lines · 32 tokens per session scan A 663f0c2e37e8
run is a command published in the GitHub repository sandeepshekhar26/develop-anything (17 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 461 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 2026-08-30.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.