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 Bilal140202/the-lord-of-the-skills --skill elkidogz__technical-change-skillgit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-skillsWrote 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/bilal140202/the-lord-of-the-skills/elkidogz__technical-change-skill)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/elkidogz__technical-change-skill"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/elkidogz__technical-change-skill/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/bilal140202/the-lord-of-the-skills/elkidogz__technical-change-skill"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/elkidogz__technical-change-skill.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 | $0.00084 | $0.05353 |
| Opus 5 | $0.00042 | $0.02677 |
| Sonnet 5 | $0.00017 | $0.01071 |
| Haiku 4.5 | $0.00008 | $0.00535 |
Grade A, and why
tc 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 484 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/tc — Technical Change Tracker
Track every code change with structured JSON records and accessible HTML output. Ensures AI bot sessions can resume seamlessly when previous sessions expire or are abandoned. Designed for deployment across multiple projects.
First-Use Detection (MANDATORY — Every Session)
At the start of EVERY session, before doing any work:
- Check if
docs/TC/tc_config.jsonexists in the current working directory - If it EXISTS: follow the Session Start Protocol in the
/tc resumesection - If it does NOT exist: prompt the user:
TC tracking is not initialized in this project. Would you like to set it up? This enables structured change tracking, AI session handoff, and HTML documentation. Run
/tc initto get started. - Wait for the user's response. If they agree, run
/tc init. - If the user declines, continue without TC tracking for this session.
A global skill is installed at ~/.claude/skills/tc.md to ensure this check runs
in every project, even those that haven't been initialized yet.
Overview
Each Technical Change (TC) is a structured record that documents:
- What changed (files, code, configuration)
- Why it changed (motivation, scope, design decisions)
- Who changed it (human or AI bot session)
- When it changed (revision history with timestamps)
- How it was tested (test cases with evidence from logs)
- Where work stands (session handoff data for bot continuity)
Storage Location
Each project stores TCs at {project_root}/docs/TC/:
docs/TC/
├── tc_config.json # Project settings
├── tc_registry.json # Master index
├── index.html # Dashboard
├── records/
│ └── TC-001-MM-DD-YY-name/
│ ├── tc_record.json # System of record
│ └── tc_record.html # Human-readable
└── evidence/
└── TC-001/ # Log snippets, screenshots
TC Naming Convention
- Parent TC:
TC-NNN-MM-DD-YY-functionality-slug(e.g.,TC-001-04-03-26-user-authentication) - Sub-TC:
TC-NNN.AorTC-NNN.A.1(letter = revision, number = sub-revision) - NNN = sequential number, MM-DD-YY = creation date, slug = kebab-case functionality name
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.
- 8d ago First seen · 484 lines · 84 tokens per session scan A 87cdd3d085cf
tc is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (4 stars, last pushed 6d ago), licensed MIT. It adds 84 tokens to every session and 5,353 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
ainb-fleet
Fleet orchestration overview — the ainb fleet ... Rust subcommand namespace for driving every claude session on the host. Routes to the sub-skills (ainb-spawn / standup / broadcast / sequence / needs / daemon / atc). Invoke this for an at-a-glance map of what fleet can do; reach for the specific sub-skill for the verb…
ainb-fleet:fleet-needs
Workflow-backed Jarvis control panel. Runs the deterministic hangar workflow with verb=needs (discover → enrich → prioritize), renders the Jarvis HUD from its render-ready cards, fires AskUserQuestion per blocked session, and routes each answer back via tmux send-keys (broker fallback only). Requires the workflow gate…
serpsmith
Publish SEO articles reliably across AI-agent runtimes.
orchestrate
Pipeline orchestration: dispatch the highest-priority ready tasks/work units to agents, manage capacity, and coordinate the Todo to Done flow. Invoked as /agiflow:orchestrate. Uses listtasks, listactivetasksbyorg, listmembers, updatetask, getworkunitprogress.
nw-deliver
Orchestrates the full DELIVER wave end-to-end (roadmap > execute-all > finalize). Use when all prior waves are complete and the feature is ready for implementation.
plan
Breaks features/goals into phased plans with task lists, agent assignments, dependencies. Triggers: plan feature, implementation roadmap, break down task, project phases.