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/stevegjones/ai-first-sdlc-practices/traceability-rendernpx skills add SteveGJones/ai-first-sdlc-practices --skill traceability-rendergit clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesWrote 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/stevegjones/ai-first-sdlc-practices/traceability-render)<a href="https://agentmods.dev/skills/stevegjones/ai-first-sdlc-practices/traceability-render"><img src="https://agentmods.dev/badge/skills/stevegjones/ai-first-sdlc-practices/traceability-render.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.00329 |
| Opus 5 | $0.00022 | $0.00164 |
| Sonnet 5 | $0.00009 | $0.00066 |
| Haiku 4.5 | $0.00004 | $0.00033 |
Grade A, and why
traceability-render 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 6d 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
Skill: traceability-render
Use this skill when a reviewer or auditor needs a human-readable view of one module's traceability. Produces a markdown document with anchors that resolve in any markdown renderer, no Sphinx required.
Inputs
<module-id>— e.g.,P1.SP1.M1. If the project uses default decomposition, justM1is accepted.
Steps
-
Validate the module exists. Read
programs.yaml; confirm the module is declared. -
Load the registry. Read
library/_ids.mdandlibrary/_code-index.md. -
Filter to the module. Keep only IDs whose module assignment matches.
-
Render the module scope. Use
render_module_scope. -
Render the dependency graph. Walk source-file imports to derive the
actual_edges. Userender_module_dependency_graph. -
Concatenate scope + dependency graph into one document.
-
Write to
docs/traceability/<module-id>.md. Idempotent. -
Print the path of the rendered document.
Done criteria
- Document written.
- Contains REQs, DESs, TESTs, code, orphan code, dependency graph.
- Idempotent.
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.
- 6d ago First seen · 39 lines · 44 tokens per session scan A 6aba9b300ef5
traceability-render is a skill published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 27d ago), licensed MIT. It adds 44 tokens to every session and 329 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…