zakelfassi

Close implementation loops with target-bound independent evidence and a portable proof receipt. Use when an agent changes a real system and must earn a completion claim that survives the session.

First fixture skill for skdd add tests. Use when testing drop installation.

Second fixture skill for skdd add tests. Use when testing multi-skill drops.

Safely backfill historical data for a pipeline stage — scope the date range, run a dry-run, execute in chunks to avoid overwhelming the database, and verify row counts after each chunk. Use when historical data is missing, when a pipeline stage is added retroactively, or when asked to "backfill the {stage} table from {start_date}".

Add or extend data validation checks in the pipeline when data quality issues are detected — null-percentage thresholds, value ranges, referential integrity, and row-count sanity checks. Use when a pipeline breaks on bad data, when onboarding a new dataset, or when asked to "add a quality check for {column}".

Onboard a new raw dataset into the data platform — sniff the schema, generate a profiling notebook, create the ingestion job, and add an entry to the data dictionary. Use when adding a new data source, when a partner delivers a new CSV/Parquet drop, or when asked to "onboard the {name} dataset".

Record a model training run — log parameters, metrics, and artifact paths, then append a structured row to the experiments ledger. Use when a training run completes, when asked to "log this experiment", or when comparing runs to decide which model to promote.

Scaffold a new transform stage in the data pipeline — create the transformation script, schema contract, idempotency logic, and tests. Use when adding a new dbt model or pandas transform, when a new business metric needs a dedicated stage, or when asked to "add a {name} stage to the pipeline".

Interrogate a product for its core value — name the observable aha moment, measure time-to-aha, or honestly conclude there is none yet. Use when starting Growth Circle work on any repo, when someone asks "how do we grow this", when GROWTH.md is missing or its Aha section is empty, or before any activation/retention/distribution work is proposed. This is the gatekeeper skill — every other growth skill depends on its output.

Compile a product's earned, evidence-backed claims from GROWTH.md into a public VALUE.md — the proof-of-value surface that agent-mediated discovery reads — with attestation levels, expiry dates, and honest retractions. Use when creating or refreshing a VALUE.md, when marketing copy needs grounding in evidence, when preparing a product to be evaluated by assistants and their principals' agents, or when a published claim has gone stale.

Run the growth experiment cadence — propose ranked by keystone impact, pre-register before launch, evaluate by the committed rule, keep/kill without relitigating, log everything. Enforces the virality gate (get-users-to-get-users proposals are parked until activation and retention run green) and the alienation test (auto-kill anything a fully-informed user would resent). Use when the growth cadence comes due (weekly/biweekly), when someone proposes "a growth hack", or when experiment results need an honest verdict.

Use SkDD to forge project-specialized adaptations of the Growth Circle skills — local skills that carry this product's keystone, event names, query paths, contexts, and cadence baked in, so future sessions (and cheaper models) run the circle without rediscovering it. Use when the generic pack has run against a project a few times and keeps re-deriving the same context, when handing growth work to scheduled or lighter-weight agents, or when a project's circle has stabilized enough to be worth freezing as skills.

Make the growth circle self-running — schedule its recurring checks (loop cadence, keystone sunset, first-light freshness, AEO probes, trust-ledger review) using whatever scheduling primitive the current harness offers, and record every schedule in GROWTH.md so it survives harness changes. Use when the circle's gates are open but nothing recurs, when a keystone sunset or instrumentation check exists only as a date nobody watches, after run-the-loop establishes a cadence, or when growth work keeps happening "whenever someone remembers".

Turn growth-team values into working artifacts — role scorecards, interview kits with listen-fors — and map which rings of the growth circle run on agents versus humans, with explicit approval boundaries. Use when hiring a first growth person, when staffing growth work across a human/agent team, when a growth hire is failing and nobody can say why, or when deciding what an autonomous agent may and may not do to users.

Detect when a playbook that works in one context is failing in another — market, language, platform, segment, or agent-mediated channel — and run the 99/1 reset protocol with native-informed deviations. Use when the same keystone reads very differently across segments, when entering a new market or platform, when metrics are flat somewhere the product "should" work, or when deciding how the product shows up in assistant recommendations.

Derive a product's keystone metric — who × does-what × N times × window — by working backwards from engaged users, then stress-test it for causation and gameability before anyone reorganizes around it. Use when a team asks "what's our North Star", when activation work has no target, when an existing keystone was adopted from a blog post rather than cohort evidence, or when GROWTH.md has an Aha but an empty Keystone.

Validate both the standard Forgeloop HUD and the spectator-facing Director Mode against the same canonical loopback state. Use when changing elixir/priv/static/ui/*, when adding stream/broadcast presentation features, or when you need to prove the HUD spectacle still reflects repo-local truth.

Create reviewable, heavy-atom git commits from current changes (working tree by default; optionally staged/unstaged/range), then push a branch and open a PR. Use when you need to split mixed changes into a small number of cohesive commits (not tiny commits) and ship via GitHub PR.

Build with rp-cli context builder plan → implement

Deep investigation with rp-cli commands: tools gather evidence, follow-up reasoning synthesizes selected context

Export a ChatGPT-ready Question / Plan / Review prompt using rp-cli

Refactoring assistant using rp-cli to analyze and improve code organization

Reminder to use rp-cli

Code review workflow using rp-cli git tool and context_builder

Create self-contained HTML presentation decks and companion downloadable guides for content creators, educators, technical talks, YouTube lives, workshops, explainers, launch narratives, and media-rich essays. Use when the user wants a presentable deck, speaker notes, run-of-show, demo plan, PDF/guide output, image-generation plan, or screenshot-backed visual narrative rather than a flat markdown answer.

Create self-contained HTML artifacts from agent or repo context, including operator briefs, build plans, implementation maps, PR/release packets, incident timelines, decision briefs, reports, explainers, diagrams, prototypes, and lightweight editors. Use when the user asks to turn dense text, code evidence, plans, reviews, or status into browser-ready HTML instead of a markdown wall.

Build and verify flightrec (fmt --all --check → clippy --all-targets -D warnings → test → build --release). Use before opening a PR or cutting a release.

Scaffold a new reusable skill (SKILL.md + optional scripts/references). Use when asked to create a skill, when a task would benefit from a reusable procedural guide, or when you notice repeated workflow friction.