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 qwerfunch/cladding --skill plannergit clone --depth 1 https://github.com/qwerfunch/claddingWrote 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/qwerfunch/cladding/planner)<a href="https://agentmods.dev/skills/qwerfunch/cladding/planner"><img src="https://agentmods.dev/badge/skills/qwerfunch/cladding/planner/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/qwerfunch/cladding/planner"><img src="https://agentmods.dev/badge/skills/qwerfunch/cladding/planner.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 | $0.00053 | $0.01291 |
| Opus 5.5 | $0.00021 | $0.00516 |
| Sonnet 5.5 | $0.00011 | $0.00258 |
| Haiku 4.5 | $0.00005 | $0.00129 |
Grade A, and why
planner 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 1mo 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planner
The Planner is a selectable role brief (formerly librarian), not an agent cladding mandates spawning. It owns the Tier A spec SSoT — spec.yaml, spec/features/, and spec/scenarios/. See docs/ssot-model.md for the full model.
Sources (what you read, by Tier)
| Tier | Artifacts | Why you read it |
|---|---|---|
| A | spec.yaml, spec/features/<slug>-<hash8>.yaml, spec/scenarios/<slug>-<hash8>.yaml |
your write target |
| B | spec/architecture.yaml, spec/capabilities.yaml, docs/project-context.md |
cross-validate when editing A; e.g., new features[] binding in capabilities.yaml ↔ feature you just added |
You do NOT read Tier C (conventions — developer owns it) or Tier D (audit — observability owns it).
What you do
- Add
F-<hash8>features as<slug>-<hash8>.yaml. LegacyF-NNNand six-or-more-hex inputs stay readable; do not migrate them for spelling alone. - Author EARS-compliant
AC-<hash8>records; legacy sequential and six-or-more-hex ids remain readable. Every feature needs one. - For a load-bearing decision, record WHY in the AC's
notes(## Decision/## Why/## Trade-off); skip obvious ACs. Seedocs/ssot-model.md§ Capturing WHY. - Bind new features to existing scenarios via the scenario's
features[]array (see Scenarios policy below). - When adding user-facing features, update the matching capability's
features[]inspec/capabilities.yamlsoCAPABILITIES_FEATURE_MAPPINGstays clean. - Mark features as
archived(witharchived_at+archive_reason). - Walk
clad sync --propose-archivecandidates — STALE_SPECIFICATION emits suggestions; you confirm each before writing. - Treat
clad clarifyas answer collection, not design-impact resolution. When an onboarding artifact could change a registered design decision, record the design impact and ask a human to resolve it before treating the change as approved. - Split
spec.yamlinto per-feature spec files (spec/features/*.yaml) when the master crosses ~1k lines. - Edit
spec/architecture.yamlandspec/capabilities.yamlbetween scans — Tier B, edit-friendly; next scan diverts new body to.cladding/scan/*.proposal. - After every edit, validate with
clad syncand check withclad check --strict.
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.
- 1mo ago Changed · +5 lines fd765804f858
- 1mo ago First seen · 74 lines · 53 tokens per session scan A 2ca87ed1fe99
planner is a skill published in the GitHub repository qwerfunch/cladding (16 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 1,291 once invoked, about $0.0002 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 2026-08-30.
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