agentleFS
Ic3b3rgIc3b3rg/learn-skills/skills/assess/SKILL.md

assess

Skill9 starsChanged 5 months agoMITView on GitHub

Assesses demonstrated understanding with Bloom or SOLO levels, concrete gaps, and a next learning step. Use when the user requests a formative assessment, asks \"where am I at?\", or wants their current level checked after studying.

13 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use blueprint, verify and 1 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • WordPressSkill

    blueprint

    Use when the deliverable is WordPress Playground Blueprint JSON or a Blueprint bundle, including creating, editing, reviewing, validating schema keys, choosing steps/resources, and debugging Blueprint files. For only running or sharing a Playground environment, use wp-playground.

    Also in 3 projects that use this

  • Microsoft AzureSkill

    verify

    End-to-end app testing pipeline for browser-based web apps on cloud browsers via Azure Playwright Workspaces: verify the live app, author tests for working flows, heal failing tests, then re-run with video and produce one consolidated report. Three phases (app verification, test authoring, test healing) plus a scoped final sanity run; each phase emits a sub-report. Use when asked to "verify my app", "make sure my app works and has good test coverage", "do a full QA pass", "add tests where I'm missing coverage", "fix failing tests and add new ones", "smoke test before deploy", "QA my deployment", or any combined verify-and-test ask. Requires Azure CLI (`az login`) and a Playwright Workspace resource ID — bootstraps configs on first invocation. Browser-based web apps with Playwright only — not for unit/API tests, native mobile/desktop, load/perf, or full accessibility audits.

    Also in 4 projects that use this

  • MicrosoftSkill

    handoff

    Compact the current conversation into a handoff document for another agent to pick up.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 2 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 13 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    After the questions, the agent verifies answers against the artefact (code + docs), maps performance to a Bloom/SOLO level, lists concrete …

    line 23
  2. Very specific:

    Default to Bloom unless /start-learn recorded a SOLO preference, or the user states a preference now.

    line 35
  3. Distinctive:

    Read LEVELS.md for the full Bloom/SOLO mappings and the exact verdict block format.

    line 60
  4. Very specific:

    Every verification reads the real artefact.

    line 78
  5. Very specific:

    If CURRICULUM.md exists in cwd: read learning-records/ first and state prior progress (*"Last session: Apply (L3)"*).

    line 82

“Common” is measured: kept by at least 6 in 10 of the 13 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Assess

Speak to the user in their language; these instructions are in English for the agent.

A short, structured assessment that returns a discrete level on a named taxonomy (Bloom or SOLO), a concrete gap list, and a suggested next skill to close the most material gap. A monolithic 0–100 number is not an output of this skill, by design — see ADR 0002.

Quick start

User: /assess "event sourcing"
Agent: I'll use Bloom unless you prefer SOLO, with 4–6 questions of increasing demand.
       Close the code and docs and confirm before we begin.
User: Closed. Bloom is fine.
Agent: Your earlier baseline was 2/5. Answer without consulting sources:
       Q1 (Remember): What is stored in an event stream?

After the questions, the agent verifies answers against the artefact (code + docs), maps performance to a Bloom/SOLO level, lists concrete gaps, and recommends one next skill.

Workflows

Mode detection

Inspect the argument:

  • Code-mode — argument is a file, directory, or PR. Artefact = local code.
  • Topic-mode — argument is a topic name. Artefact = external docs + project's actual stack version.

Default scale

Default to Bloom unless /start-learn recorded a SOLO preference, or the user states a preference now. Both scales are first-class; the user picks.

Session checklist (copy into your reply and tick as you go)

Assessment progress:
- [ ] Mode detected (code / topic) and stated
- [ ] Scale chosen (Bloom default unless SOLO requested)
- [ ] Baseline recalled from /start-learn (or asked now)
- [ ] Source-closed rule confirmed with user
- [ ] Project read (topic-mode only)
- [ ] Question sequence follows the chosen scale in LEVELS.md
- [ ] Each answer verified against the artefact
- [ ] Questions asked in ascending cognitive demand
- [ ] Stopped after two consecutive fails, if reached
- [ ] No more than six questions asked
- [ ] Partial answers recorded as gaps toward the next level
- [ ] Level mapped (highest level fully passed)
- [ ] Gaps listed concretely with file:line or doc refs
- [ ] Suggested next skill identified by *dominant gap*, not by level
- [ ] Verdict block printed in the exact required format
- [ ] No numeric score produced (anti-pattern check)

Session protocol

Read LEVELS.md for the full Bloom/SOLO mappings and the exact verdict block format. Summary of the 7 steps:

  1. Frame — restate object, scale, sources-closed rule. Wait for confirmation.
  2. Recall baseline from /start-learn (or ask for it now: 1–5 self-rating).
  3. Project read (topic-mode only) — anchor on the version in package.json / lockfile / etc.
  4. Ask 4–6 questions of ascending cognitive demand — one per Bloom/SOLO level; stop after two consecutive fails.
  5. Verify each answer against the artefact — local code or authoritative documentation using available retrieval tools. Pass / partial / fail with cited evidence.
  6. Map performance to a level — highest level fully passed; a partial at L means the user is at L−1 with a gap toward L.
  7. Produce the verdict block in the exact format defined in LEVELS.md. Never a numeric score; never a rounded level.

--detailed adds a 4-axis rubric (Recall / Trace / Transfer / Teach-back, 1–3 each) as colour, not replacement.

Choosing the suggested next skill

Pick by dominant gap, not by lowest level: structural → /explain-and-check or /connect-to-what-you-know; factual → /quiz-me (capture with /flashcards); can't apply → /learn-by-doing; can't connect → /connect-to-what-you-know; solid, wants record → /linked-notes.

Source fidelity

Every verification reads the real artefact; every external claim carries file:line or a doc link. If an answer cannot be sourced, drop the question rather than score from memory.

Workspace mode

If CURRICULUM.md exists in cwd: read learning-records/ first and state prior progress ("Last session: Apply (L3)"). After verdict, write learning-records/NNNN-<topic>-assessment.md (frontmatter: date/topic/level; body: gap list; N = highest existing + 1). Absent: no records touched.

Anti-patterns

  • Don't produce a 1-100 score — a single number creates false precision, false closure ("78, good enough"), and applies borrowed confidence to the verdict itself; this is the failure mode the skill exists to refuse. See ADR 0002.
  • Don't round a level ("Apply-ish", "halfway between Apply and Analyze") — rounding reintroduces the false-precision problem on a smaller scale; levels are discrete because the diagnostic value is in the next level the user can't reach, not in fractional progress.
  • Don't skip the baseline — without it the verdict is a snapshot with no story, and the user has no way to tell if learning happened or stagnated.
  • Don't ask 12 questions — cap at 6; cognitive fatigue erodes the signal, and answers from a tired user describe their tiredness, not their understanding.
  • Don't choose the suggested-next from the lowest level reached — the dominant gap determines the next skill (a user at L3 with a factual gap needs /quiz-me, not /connect-to-what-you-know); routing by level is a shortcut that misses the texture.
  • Don't read docs / code with the user to "fairly verify" — joint reading collapses the assessment into a learning session, eliminating the diagnostic value; the user keeps the source closed and the agent judges.

Governing principles (this skill satisfies all five)

  1. Agent withholds — no hints, no partial credit narration during the assessment.
  2. Student speaks first — every level is verified on what the user produces.
  3. Artefact is the judge — verification reads the real code/docs.
  4. Source fidelity — version anchoring + citation to authoritative sources on every external claim. Before verifying, read SOURCES.md.
  5. Exit is a transfer test — the L3+ questions are transfer tests; the verdict is built on them.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.