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 skills add charlesmu99/speccrew --skill speccrew-knowledge-bizs-api-graphgit clone --depth 1 https://github.com/charlesmu99/speccrewWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/charlesmu99/speccrew/speccrew-knowledge-bizs-api-graph)<a href="https://agentmods.dev/skills/charlesmu99/speccrew/speccrew-knowledge-bizs-api-graph"><img src="https://agentmods.dev/badge/skills/charlesmu99/speccrew/speccrew-knowledge-bizs-api-graph/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/charlesmu99/speccrew/speccrew-knowledge-bizs-api-graph"><img src="https://agentmods.dev/badge/skills/charlesmu99/speccrew/speccrew-knowledge-bizs-api-graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00047 | $0.00167 |
| Opus 5 | $0.00023 | $0.00084 |
| Sonnet 5 | $0.00009 | $0.00033 |
| Haiku 4.5 | $0.00005 | $0.00017 |
Grade A, and why
speccrew-knowledge-bizs-api-graph 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 10d 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
Trigger Scenarios
- "Construct graph data from API analysis results"
- "Generate knowledge graph nodes and edges for API feature"
- "Write graph JSON for API controller"
AgentFlow Definition
REQUIRED: Before executing this workflow, read the XML workflow specification: speccrew-workspace/docs/rules/agentflow-spec.md Then read and execute the XML workflow in SKILL.xml block-by-block as the authoritative execution plan.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 16 lines · 47 tokens per session scan A 166533671261
speccrew-knowledge-bizs-api-graph is a skill published in the GitHub repository charlesmu99/speccrew (13 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 167 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 skills, from other repositories
lijigang-skill
A Chinese-language approach to writing precise, highly structured prompts, sometimes using Lisp-like notation. It combines concise wording, philosophical questioning, and a process for defining roles, conditions, output formats, and revisions.
memstack-seo-ai-search-visibility
Use this skill when the user says 'AI search', 'AI visibility', 'ChatGPT ranking', 'Perplexity optimization', 'GEO', 'generative engine optimization', or needs to optimize content for AI-powered search engines and LLM citations. Do NOT use for traditional SEO audits or Google Ads.
llamafactory
Fine-tune LLMs with LlamaFactory — register datasets, train via YAML configs, merge LoRA adapters and serve the result.
ss-reference
Compile screenshots, URLs, Figma exports, or an existing UI into a project-local StyleSeed output grammar with evidence, tokens, confidence, anti-patterns, and a validation screen. Use when the user supplies a design reference that StyleSeed does not already model.
regex-vs-llm-structured-text
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
prompt-engineering
Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic…