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/jongwony/epistemic-protocols/steernpx skills add jongwony/epistemic-protocols --skill steergit clone --depth 1 https://github.com/jongwony/epistemic-protocolsWrote 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/jongwony/epistemic-protocols/steer)<a href="https://agentmods.dev/skills/jongwony/epistemic-protocols/steer"><img src="https://agentmods.dev/badge/skills/jongwony/epistemic-protocols/steer.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 | $0.00039 | $0.10868 |
| Opus 5 | $0.00019 | $0.05434 |
| Sonnet 5 | $0.00008 | $0.02174 |
| Haiku 4.5 | $0.00004 | $0.01087 |
Grade A, and why
steer 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 today.
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 — 478 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Steer Skill
Project Profile Recalibration via Calibration Drift Audit — when the user wants to refresh the user-global or project-local project-profile rule layer based on observed Cognitive Partnership Move calibration drift in a target session, scan calibration moves for Constitution overapplication, Extension misapplication, and existing-profile drift, surface per-cluster evidence for user-constituted verdict, assemble a profile diff, and write an updated rule file with timestamped backup. Type: (CalibrationDriftOpaque, AI, INDUCE-with-inscription, SessionCalibrationMoves) → UpdatedProjectProfile.
Invoke directly with /steer when the user wants the project profile to track observed calibration practice rather than remain frozen at initialization time.
Definition
Steer (Periagoge specialization, "turning the helm"): A dialogical act of resolving the opacity of project profile drift into a recognized profile update, where AI scans target session calibration moves against the existing project-profile rule layer, classifies each move into drift clusters, presents per-cluster evidence for user-constituted verdict, assembles a diff over the six profile variables, and inscribes the approved diff back into the rule file — profile mutation belongs to the user's per-cluster verdict and final approval acts.
This skill is a Periagoge family extension. Generic Periagoge (/induce) crystallizes an unnamed essence from an instance set into a session-text abstraction. Steer narrows the instance axis to Cognitive Partnership Move calibration moves observed in a target session and extends the output axis with a writable rule inscription step — the inscribed project-profile.md becomes the new prejudgment baseline for the next invocation. This closing of the loop (audit → inscription → next invocation reads the inscribed baseline) is referred to as Circular Return in this skill: the inscribed artifact becomes the prejudgment input the next audit reads from, making the operation self-referential across sessions. The dialectical triangulation core is preserved (Phase 3 per-cluster verdicts and Phase 5 final approval realize the Confirm / Dismiss / Reorient pattern), but the write step crosses the preserves/mutates boundary that /induce itself respects.
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.
- today Changed · -1 lines 39431c037d84
- yesterday Changed 2a5fb8dcbc4b
- 5d ago First seen · 479 lines · 39 tokens per session scan A 89a65b4727e2
steer is a skill published in the GitHub repository jongwony/epistemic-protocols (160 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 10,868 once invoked, about $0.0002 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 2026-08-30.
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