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 varunk130/ai-gtm-skill-library --skill context-anchorgit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote 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/varunk130/ai-gtm-skill-library/context-anchor)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/context-anchor"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/context-anchor/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/varunk130/ai-gtm-skill-library/context-anchor"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/context-anchor.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 | $0.00081 | $0.00758 |
| Opus 5.5 | $0.00032 | $0.00303 |
| Sonnet 5.5 | $0.00016 | $0.00152 |
| Haiku 4.5 | $0.00008 | $0.00076 |
Grade A, and why
context-anchor 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 26d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Anchor (CANVAS Framework)
Most GTM skills share one gap: each one re-asks who the buyer is, what the product does, and why it wins, and gets a slightly different answer every time. Context Anchor captures those answers once in a versioned file, then keeps the file honest with a freshness and completeness score so downstream skills never run on stale positioning.
Core Principle
One source of truth, dated and scored. A context file nobody has touched in a quarter quietly spreads outdated positioning, because every skill downstream repeats it.
The CANVAS Framework
| Letter | Section | The Question |
|---|---|---|
| C | Claim | What do we say we do better, in one sentence a buyer would repeat? |
| A | Audience | Who buys, who uses, who signs? (ICP firmographics + personas) |
| N | Need | Which job or pain triggers a purchase right now? |
| V | Voice | How we sound: words we use, words we never use, reading level |
| A | Alternatives | What else the buyer considers, including doing nothing |
| S | Substantiation | Which numbers, logos, and quotes back the claim? |
Process
- Interview - ask one CANVAS section at a time; accept "unknown" rather than guessing
- Draft - write
GTM_CONTEXT.mdwith one H2 per CANVAS section and alast_revieweddate per section - Score completeness - each section scores 0 (missing), 1 (asserted), or 2 (asserted + evidence); completeness = total / 12
- Score freshness - per section,
freshness = max(0, 1 - days_since_review / 90) - Gate - context health = completeness × average freshness; below 0.6, downstream skills should warn before running
- Hand off - list which sections each paired skill reads so owners know what to update
Output
Save to GTM_CONTEXT.md at the repo root, plus outputs/context-anchor-health-[YYYY-MM-DD].md
| Artifact | Description |
|---|---|
| GTM_CONTEXT.md | Six CANVAS sections, each with an owner and last_reviewed date |
| Health table | Completeness (0-2) and freshness (0-1) per section, plus overall health |
| Refresh list | Sections below 0.5 freshness or missing evidence, with the owner to ping |
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.
- 26d ago First seen · 55 lines · 81 tokens per session scan A 043bd54ac762
context-anchor is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 12d ago), licensed MIT. It adds 81 tokens to every session and 758 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-20.
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