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/ramboz/jig/reframenpx skills add ramboz/jig --skill reframegit clone --depth 1 https://github.com/ramboz/jigWhat 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 | $0.00175 | $0.04893 |
| Opus 5 | $0.00088 | $0.02447 |
| Sonnet 5 | $0.00035 | $0.00979 |
| Haiku 4.5 | $0.00017 | $0.00489 |
Grade A, and why
reframe 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 — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spec 067 introduces this skill; ADR-0024 decides its shape. Reframe is a lightweight correction capability over the lifecycle spine (ADR-0023 §4), not a new gated lifecycle member: it has no
transitionand no state machine of its own — its arc (recognize → decide dispositions → execute) orchestrates the ADR and spec lifecycles it spawns, and every gate it relies on (the keystone ADR's frame-critiqueaccept; the review passes on the retrofit specs) belongs to those lifecycles. Shipping as its own skill is a user-facing-surface choice — a deliberate, named operation a user reaches for — not a lifecycle one.Like
/jig:clarifyand/jig:explain, reframe is a judgment skill — it ships no.pyhelper. The determinism it needs (reserve the keystone ADR, reserve retrofit specs) it borrows from the existingadr.py/workflow.pyscaffolding; the corpus read and the drafting are model judgment. Its testable surface is structural (this SKILL.md's contract: the keystone-ADR-via-adr.py newflow, the disposition vocabulary, the coverage-floor shape, the deferral language, registration). The quality of the corpus read and the honesty of a given coverage floor are judgment, exercised by this prompt and by the keystone ADR's frame-critiqueacceptgate — not something a unit test can assert (the accepted gap for every judgment-only jig skill).
What this skill does
jig keeps work consistent with prior decisions: every spec and ADR is a durable record, and new work reads the accepted corpus as authoritative. That is jig's strength — and its blind spot when a load-bearing premise changes from outside the system. A team drops a new design (or contract, or vision) into the repo intending to retrofit onto it; but the corpus already encodes the old premise as settled truth, so the new artifact enters as an inert file with no authority and the consistency machinery faithfully carries the dead premise forward — the agent patches at the edges instead of re-baselining.
A load-bearing reference is any authoritative external input the corpus is
premised on: a design system, a test-infrastructure choice, a vendor / API
contract, a compliance regime, a target platform, or a product-positioning /
strategic-vision shift. A reframe re-anchors the corpus to one that has
moved. /jig:reframe, given the new reference:
- Reads the accepted corpus against the reference (the corpus read).
- Drafts the keystone reframe-ADR via
adr.py new— elevating the new reference from an inert file to an accepted decision, superseding the old premise, and carrying the re-baselining manifest. - Assigns every affected artifact a disposition in that manifest (no
TBD), each routing to an operation that already exists. - States a two-level coverage floor — what corpus was scanned, at what
granularity, and what was not — so a partial read is visible at the ADR's
acceptgate rather than silently carrying a dead premise forward under fresh authority.
A competent session, handed the accepted keystone ADR + the manifest, then executes the dispositions through the existing ADR and spec lifecycles. The skill drafts; the session executes. This is a best-effort correction floor: it makes re-baselining expressible and human-gated — it does not auto-solve enumeration completeness (parked, see the coverage floor) and it is not a silent-drift detector (that half is the 067-03 noticing nudge + parked detection).
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 · 340 lines · 175 tokens per session scan A d1debcf8d611
reframe is a skill published in the GitHub repository ramboz/jig (6 stars, last pushed 2d ago), licensed MIT. It adds 175 tokens to every session and 4,893 once invoked, about $0.0009 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 2026-08-31.
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