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/ackeskin/contexture/discovernpx skills add AcKeskin/contexture --skill discovergit clone --depth 1 https://github.com/AcKeskin/contextureWrote 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/ackeskin/contexture/discover)<a href="https://agentmods.dev/skills/ackeskin/contexture/discover"><img src="https://agentmods.dev/badge/skills/ackeskin/contexture/discover.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 | $0.00051 | $0.04358 |
| Opus 5 | $0.00026 | $0.02179 |
| Sonnet 5 | $0.00010 | $0.00872 |
| Haiku 4.5 | $0.00005 | $0.00436 |
Grade A, and why
discover 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 2d 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 — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
discover
Unified retrieval layer. Aggregates stored memory fragments (what we've decided/learned) and codebase context (what the code actually looks like) into the current session.
When to run
- User types
/discover(explicit trigger). - User says "load context," "prep yourself," "what do you know about X."
- Another skill invokes this one (prep, review).
- Do not auto-fire at session start — explicit invocation only.
Inputs
- Task text. The most recent user message, plus any arguments passed after
/discover. - Project root.
$CLAUDE_PROJECT_DIRif set, otherwise the current working directory. - Optional caller filters (when invoked by another skill, not by a user):
scopes,kind,relevance_phases. See "Query interface" below.
Architecture — MCP-primary, skill-fallback
Retrieval is MCP-primary: memory + recap + codemap retrieval is owned by the project-memory MCP engine, out of context. This skill is a thin client over it, with a static always-on floor when the engine is unreachable.
Two responsibilities stay in this skill, NOT the MCP:
- Rules-overlay resolution (§4a) — the rules-overlay tier/patch/disable subsystem is a separate module; v1 keeps it skill-resident.
- The fallback — when the MCP is down, surface the static floor (§0b). The skill does not re-implement the full ranking as its primary path.
0. Primary path — query the MCP engine
Call mcp__project-memory__discover with the task-derived query:
mcp__project-memory__discover({
cwd: <project root>, // the engine resolves the layered tier
include_codemap: true,
... // remaining params per "Query interface" below
})
The engine returns ranked memory fragments (with 📛 warning prefixes, [related_to] / ⚡ contradicts flags, scope/relevance metadata), plus a ## Codemap block, plus any ⚠️ shadowed-tier / case-split warnings. Surface that result directly (§9 shaping is mostly done by the engine). Then run §4a (rules-overlay) and merge: overlay-resolved rules are appended to the engine's result list and ranked by the same signals the engine reports (scope match, then kind priority, then description keyword overlap with the task); ties break toward the overlay rule, since a tier override outranks a shipped default. The merged list honours the caller's top_n as a single combined cap — overlay rules never bypass the cap. Deliver then renders the merged list's bodies exactly as in the degraded path (§9a applies to engine-returned bodies too).
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.
- 2d ago First seen · 277 lines · 51 tokens per session scan A 850d72514d53
discover is a skill published in the GitHub repository AcKeskin/contexture (2 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 4,358 once invoked, about $0.0003 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
log-session
Append a structured entry to the project's session log (docs/LOGS.md): what was done this session, files touched, decisions taken, and the next step — so the next session (or another person) can pick up the thread without re-reading git history. Run it before /clear, before closing Claude Code, or at any natural…
context-scaffold
Analyzes an existing project and generates context files (AGENTS.md, context directory, cascading structure) pre-populated with discovered information. Run once to bootstrap, then customize.
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AgentDB Memory Patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
V3 Memory Unification
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context-upgrade
Guides transition from your current context level to the next one -- minimal to full, full to cascading, or adding skills and hooks layers. Preserves existing content.