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/everyone-needs-a-copilot/claude-copilot/tanpx skills add Everyone-Needs-A-Copilot/claude-copilot --skill tagit clone --depth 1 https://github.com/Everyone-Needs-A-Copilot/claude-copilotWrote 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/everyone-needs-a-copilot/claude-copilot/ta)<a href="https://agentmods.dev/skills/everyone-needs-a-copilot/claude-copilot/ta"><img src="https://agentmods.dev/badge/skills/everyone-needs-a-copilot/claude-copilot/ta.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 | $0.00699 |
| Opus 5 | $0.00022 | $0.00349 |
| Sonnet 5 | $0.00009 | $0.00140 |
| Haiku 4.5 | $0.00004 | $0.00070 |
Grade A, and why
ta 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 yesterday.
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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Architect
Use this skill before non-trivial implementation.
Operating Lens
- Understand the existing system before proposing changes.
- Prefer the simplest viable design that matches local patterns.
- Make tradeoffs explicit.
- Design for failure modes and maintainability.
- Use
tcfor substantial PRDs, tasks, and architecture work products. - Verify installed third-party API surfaces with Live Docs before planning around them.
Success Criteria
- Existing architecture and constraints are understood before recommendations.
- Tradeoffs and rejected alternatives are documented.
- Implementation tasks include explicit test requirements.
- Security, operations, data, and failure modes are called out when relevant.
- Substantial plans are stored in
tc, not loose markdown. - Third-party API assumptions are checked with
cc docswhen available.
Workflow
-
Read
08-taste/INDEX.mdfrom the nearestpaths.knowledge_repoentry that has one — resolved tensions from this owner's own feedback, personal tier only, empty until earned. Apply the reasoning, not the example; when a rule does not fit, say so rather than forcing it. -
Check task context with
tc task get <taskId> --jsonwhen a task exists. -
Hydrate config with
eval "$($HOME/.local/bin/cc env)"whenccis available. -
Search memory for prior architecture decisions.
-
Read the request, relevant project docs, decision instruments, and surrounding code.
-
Run
cc docs get <pkg> --topic <area> --jsonbefore planning against installed third-party APIs. -
Define scope, non-goals, constraints, and risks.
-
Compare viable approaches and choose one.
-
Break work into concrete implementation and verification units with test expectations.
-
Store architecture decisions and task plans as
tcwork products. -
Route to
$mefor implementation and$qafor verification.
Iteration Loop
For non-trivial plans, iterate until the design has clear boundaries, no unresolved critical dependency, and testable implementation tasks. If an external decision blocks the plan, mark the task blocked or store a blocker work product.
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.
- yesterday First seen · 77 lines · 44 tokens per session scan A a7b228ce3273
ta is a skill published in the GitHub repository Everyone-Needs-A-Copilot/claude-copilot (13 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 699 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 2026-09-04.
Other skills, from other repositories
ship-first
Final task completion protocol: report → user-note → deploy → smoke test → close? → guide? → routing.db → propagate → STATUS → sessions. Invoked from fast-track and pipeline flows after task execution, not directly by the user. Use when: invoked after the execute step of a fast-track or pipeline task.
ui-ai-first
Final audit of a large task before closure — finds which operations are available only via code / curl / SQL and decides per each: automate with a skill or AI agent (A) or create a UI task (B). Protects against invisible usability debt. Walks through each implemented block: reads task.md + reports + guides + code →…
cadence-first
Meta-orchestrator: assigns skill-chain cadence per block (Tier 1/2/3) via Q1-Q6 rules. Reads target taskblocks + task.md, writes cadence-decisions-{R}.md artifact. Standalone (executor invokes) or Batch (generator hand-off). Use when: deciding cadence for a batch of blocks, generator hand-off from decomposition skills.
decision-first
Makes an architectural / project / scope decision using a 5-part model INSTEAD of asking the user. Structure: 🎯 Decision / Why / 🛡 Security / 📈 Scalability / Alternatives / Plain-language analogy. 1 question = 1 atomic artifact. Use when: the agent is about to ask an architectural / scope question…
library-first
Mandatory protocol before executing any fast-track task. Analyzes the task, builds a table: what we do / where it comes from / how many lines of code. Principle: maximum reuse of existing libraries and components, minimum new code. Waits for explicit user approval — does nothing until confirmed. Use when: fast-track…
fixture-new
Creates a parity fixture — the frozen scenario plus the contract its output must satisfy. Asks which skill and case, what shape the run must produce, and writes input.md and expect.yml. Ends by proving the new fixture actually fails on an empty directory. A fixture that passes when nothing ran is worse than no…