Borrowing it
Nothing to install: this file belongs to ashishpatill/tell-proof. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ashishpatill/tell-proof/master/.cursor/skills/agency-run-learn/SKILL.mdgit clone --depth 1 https://github.com/ashishpatill/tell-proofWrote 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/ashishpatill/tell-proof/agency-run-learn)<a href="https://agentmods.dev/skills/ashishpatill/tell-proof/agency-run-learn"><img src="https://agentmods.dev/badge/skills/ashishpatill/tell-proof/agency-run-learn/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/ashishpatill/tell-proof/agency-run-learn"><img src="https://agentmods.dev/badge/skills/ashishpatill/tell-proof/agency-run-learn.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 | $0.00036 | $0.00427 |
| Opus 5 | $0.00018 | $0.00214 |
| Sonnet 5 | $0.00007 | $0.00085 |
| Haiku 4.5 | $0.00004 | $0.00043 |
Grade A, and why
agency-run-learn 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 10d 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.
What it actually says
agency-run-learn
Developer / maintainer loop. Each agency:run on a workstation with a design-data
pointer ends with learn → agency-engine-memory.json + LEARNINGS.md (+ write-back).
Not for Ashish's browser. His preferences use tell-user-session-learn.
| Loop | Skill | Where |
|---|---|---|
| Dev corpus + pipeline | agency-run-learn |
research/*, design-data checkout |
| User sessions | tell-user-session-learn |
localStorage tell:user-design-profile |
Automatic on pnpm agency:run and --mark-pass 4-ship. Opt out: AGENCY_SKIP_LEARN=1.
Dev corpus itself requires research/design-data.local.json or TELL_DESIGN_DATA + TELL_DEV_CORPUS=1.
Hard rules
- Learn is automatic on agency runs — do not schedule a separate agent step.
- Never enable design-data pull/write-back on public demos (
VERCEL/TELL_PUBLIC_DEMO). - Do not weaken gates to make a run green.
- No third-party hosts in Tell commits.
- Do not store end-user profiles in the design-data repo.
Commands
pnpm agency:run -- --query "<requirement>" --fresh # learn automatic
pnpm agency:learn -- --run-id <id> # re-learn only
AGENCY_SKIP_LEARN=1 pnpm agency:run -- --query "…" # dry smoke
Related
research/design-data.README.md— developer corpus wiringtell-user-session-learn— per-user product learningagency-quality-site— phased craft + autonomous run
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.
- 10d ago First seen · 42 lines · 36 tokens per session scan A d3359d84f5bc
agency-run-learn is a skill published in the GitHub repository ashishpatill/tell-proof (1 stars, last pushed 15d ago), licensed MIT. It adds 36 tokens to every session and 427 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 2026-08-31.
Other skills, from other repositories
init-workspace-documentation
Skill "init-workspace-documentation" from griddynamics/rosetta, covering agent memory.md, agent memory, preventive rules, what worked and what failed.
ijfw-ui-spec
Use when the user says: 'ui spec', 'design contract', 'ui audit setup', 'lock the design', 'visual contract', 'ui review setup', or '/ijfw-ui-spec'. Produces UI-SPEC.md as the visual design contract before any frontend or visual-artifact build, and dispatches ijfw-ui-auditor as the final 6-pillar gate.
repo-context-ledger
Record every behavior-changing feature addition, fix, and adjustment as durable, evidence-based repository knowledge, then use that ledger to continue accurately across AI windows, tools, Git collaboration, and pull requests. Use the deterministic runtime to route bounded context, isolate private drafts, publish…
layered-context
L0/L1/L2 three-layer context loading protocol — reduces token consumption during /resume boot.
semantic-search
Zero-dependency TF-IDF search across MUSE memory, roles, and skills. Use when user wants to find information across their project context.
a2wave-memory
Progressively recall and maintain a2wave cross-session memory through a compact startup catalog, bounded topics, and searchable history.