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 pantheon-org/tekhne --skill pingit clone --depth 1 https://github.com/pantheon-org/tekhneWrote 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/pantheon-org/tekhne/pin)<a href="https://agentmods.dev/skills/pantheon-org/tekhne/pin"><img src="https://agentmods.dev/badge/skills/pantheon-org/tekhne/pin/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/pantheon-org/tekhne/pin"><img src="https://agentmods.dev/badge/skills/pantheon-org/tekhne/pin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 5 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00110 | $0.02859 |
| Opus 5.5 | $0.00044 | $0.01144 |
| Sonnet 5.5 | $0.00022 | $0.00572 |
| Haiku 4.5 | $0.00011 | $0.00286 |
Grade A, and why
pin 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 14d 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 — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pin — Session Decision Board
Persist decisions, questions, constraints, and corrections to a JSON file that survives context compaction. A companion hook injects the board into every tool call so the model never forgets.
Auto-Invoke Rules
After responding to any user message where a decision was made, a question was asked about a proposal, or a constraint was stated, IMMEDIATELY invoke /pin with the appropriate category. Do not ask permission — just pin it.
Examples of auto-invoke triggers:
- User: "yes go with bun" → respond normally, then
/pin ✅ use bun - User: "what about the latency impact?" → respond normally, then
/pin ❓ split services — latency impact? - User: "no skip auth for now" → respond normally, then
/pin ❌ auth layer — skip for MVP - User: "MVP only, max 3 files" → respond normally, then
/pin 📌 MVP only, max 3 files - User: "no I meant artisans not developers" → respond normally, then
/pin 🔧 target = artisans, not developers
Do NOT pin:
- Casual conversation, greetings
- Implementation details (code changes, file edits)
- Things already pinned (check board first)
Commands
| Command | Action |
|---|---|
/pin ✅ <text> |
Pin approved item |
/pin ❓ <text> |
Pin pending question |
/pin ❌ <text> |
Pin killed/rejected item |
/pin 📌 <text> |
Pin scope constraint |
/pin 🔧 <text> |
Pin correction |
/pin show or /pin |
Display current board |
/pin rm <n> |
Remove pin by number |
/pin clear |
Clear all pins |
/pin clear triage |
Clear ✅/❓ only, keep 📌/❌/🔧 |
State File
Path: a session-scoped .session-logs/<slug>/pins.json file under a configurable base directory.
Derive slug from CWD:
GIT_ROOT=$(git rev-parse --show-toplevel 2>/dev/null)
REL_PATH="${PWD#$GIT_ROOT/}"
SLUG=$(echo "$REL_PATH" | tr '/' '-')
PINS_DIR="$PRAXIS_DIR/.session-logs/$SLUG"
PINS_FILE="$PINS_DIR/pins.json"
Schema:
{
"items": [
{
"id": 1,
"type": "approved",
"emoji": "✅",
"content": "use bun everywhere",
"detail": "",
"ts": "2026-04-01T14:30:00Z"
}
],
"next_id": 2
}
What ships with it
34 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .audits/2026-04-11/analysis.md 1.2 KB
- .audits/2026-04-11/audit.json 480 B
- .audits/2026-04-11/remediation-plan.md 2.8 KB
- .audits/2026-10-02/Analysis.md 475 B
- .audits/2026-10-02/audit.json 537 B
- .audits/2026-10-02/Remediation.md 1.4 KB
- .audits/latest 10 B
- .tekhne/plugin.json 239 B
- CHANGELOG.md 323 B
- evals/instructions.json 4.4 KB
- evals/scenario-1/capability.txt 36 B
- evals/scenario-1/criteria.json 2.3 KB
- evals/scenario-1/task.md 158 B
- evals/scenario-2/capability.txt 34 B
- evals/scenario-2/criteria.json 2.2 KB
- evals/scenario-2/task.md 163 B
- evals/scenario-3/capability.txt 23 B
- evals/scenario-3/criteria.json 2.1 KB
- evals/scenario-3/task.md 158 B
- evals/scenario-4/capability.txt 27 B
- evals/scenario-4/criteria.json 2.1 KB
- evals/scenario-4/task.md 70 B
- evals/scenario-5/capability.txt 29 B
- evals/scenario-5/criteria.json 2.5 KB
- evals/scenario-5/task.md 72 B
- evals/scenario-6/capability.txt 103 B
- evals/scenario-6/criteria.json 1.9 KB
- evals/scenario-6/task.md 290 B
- evals/scenario-7/capability.txt 166 B
- evals/scenario-7/criteria.json 2.2 KB
- evals/scenario-7/task.md 520 B
- evals/summary.json 1.3 KB
- references/edge-cases.md 1.1 KB
- references/inspecting-the-board.md 1.3 KB
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.
- 14d ago Changed · -1 lines c74e80940a02
- 19d ago Changed · +73 lines 416e918967fe
- 1mo ago First seen · 210 lines · 110 tokens per session scan A ffb3f1239592
pin is a skill published in the GitHub repository pantheon-org/tekhne (10 stars, last pushed today), licensed MIT. It adds 110 tokens to every session and 2,859 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
comet-memory
A review step for deciding whether information should become durable personal memory. It can keep, update, forget, or skip memory candidates based on bounded evidence.
relevance-coarse-filter
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
recall-memory
Recall relevant long-term memories on demand. Given a topic or question, judges relevance from pre-loaded metadata, loads only relevant files, and returns a concise summary to the main agent.
catchup
Rebuild the human's lost context on a project from live state, in plain language: what needs them, what changed, what new words mean. Use when the human returns after a gap, says they can't follow the project anymore, asks what happened or what a term means, or before deciding what to do next when their mental model…
immune
Hybrid adaptive memory: Cheatsheet (positive patterns pre-generation) and Immune (negative patterns post-generation) with Hot/Cold tiered auto-learning. Triggers on: "scan for errors", "immune scan", "check output quality", "antibody scan". NOT for PR review (use pr-review) or repo audits (use repo-sentinel).