Requirements Traceability logo

Requirements Traceability

Organization
l-gevity
requirements-traceability

Maintains bidirectional traceability between canonical requirements and implementation, verification, decision, operational, and outcome evidence. Use when planning, implementing, reviewing, or closing work that must prove which requirement authorized a change, where it is implemented, which executed evidence verifies it, whether a linked outcome hypothesis is supported by current measurement, what remains unmapped, or whether references have gone stale. Do not use to invent requirement or hypothesis meaning, normalize the requirement graph, decide implementation readiness, or issue a functionality-worth verdict.

Overview

Publisherl-gevity
Repositoryl-gevity-skills
Skill namerequirements-traceability
Stars
72
Forks
13
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by l-gevity on GitHub. Read the source before you install it.

Installation

Install the Requirements Traceability AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/l-gevity/l-gevity-skills/tree/main/.agents/skills/requirements-traceability
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/l-gevity/l-gevity-skills.git /tmp/l-gevity-skills
mkdir -p .claude/skills
cp -r /tmp/l-gevity-skills/.agents/skills/requirements-traceability .claude/skills/requirements-traceability
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Requirements Traceability in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Requirements Traceability on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Requirements Traceability is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Requirements Traceability

Keep requirement intent connected to implementation and executed evidence without turning code, tests, or a trace registry into a second requirements source.

Core Directives

  1. Canonical requirements keep meaning. Trace links identify evidence; they never authorize new product, policy, legal, or domain behavior.
  2. Trace both directions. Check requirement to evidence and changed artifact to requirement or an explicit non-requirement rationale.
  3. Implementation is not verification. A code anchor or test definition proves coverage exists; only an executed passing result proves verification.
  4. Place anchors naturally. Prefer stable IDs in contracts, tests, ADRs, operations evidence, and issue closeout over a duplicate central registry.
  5. Fail on stale references. Unknown IDs, removed criteria, ambiguous aliases, and unverifiable success claims are blocking trace defects.
  6. Completion is not outcome evidence. Acceptance and implementation evidence may verify a capability; only a linked, representative measurement can assess its downstream outcome hypothesis.
  7. Every representation gap names its expiry condition. A declared asymmetry without the condition that ends it becomes permanent by accident.

Boundary

Use this skill after a passing implementation-readiness decision when work enters architecture, implementation, verification, review, or closeout.

Use requirements-grounding to establish meaning and authority, requirements-topology to own IDs, relationships, lineage, schemas, and generated views, and implementation-readiness to decide whether work may start. Apply the project's requirements profile for repository paths, ID formats, evidence stores, commands, and classifications.

This skill owns live evidence relationships and outcome-evidence state. requirements-grounding owns hypothesis meaning, thresholds, guardrails, and revisit intent. functionality-complexity-tradeoff owns the resulting functionality-worth verdict. This skill does not add another stage or letter to A.L.C.H.E.M.Y.

Minimum Inputs

Require:

  • canonical requirement and acceptance-criterion IDs for completion tracing;
  • canonical outcome-hypothesis IDs and versions when outcome evidence is in scope;
  • the passing readiness decision and admitted increment;
  • for aspect requirements, the governed set at subsystem granularity from the accepted placement decision or the architecture-as-code registry;
  • the changed implementation, contract, decision, or operations artifacts;
  • the project's accepted anchor forms and executable verification sources;
  • commit, build, run, or environment identity when operational evidence is offered as proof.

If canonical IDs or criterion references are unstable, return to requirements-topology. If the admitted increment or acceptance conditions are unclear, return to implementation-readiness.

Completion Evidence States

Classify every criterion independently:

StateRequired evidenceWhat it does not prove
unmappedNo accepted implementation or verification anchorWhether work is planned elsewhere
implementedStable implementation anchor or executable test definitionThat the behavior passed
verifiedImplemented anchor plus a passing test or accepted operational result for this revisionContinued correctness after later changes
blockedNamed missing dependency, decision, environment, or evidence ownerThat the requirement may be silently skipped
not-applicableExplicit scoped rationale approved by the requirement ownerA general waiver for other increments or actors

A passed test tag without a matching test definition is not verification. A test definition without a result is implementation evidence only. Operational evidence without revision and run identity is an observation, not reproducible proof.

Outcome Evidence States

Classify each linked empirical hypothesis independently for its exact version:

StateRequired evidenceWhat it does not prove
unmeasuredNo accepted representative observation yetThat the expected impact is absent
supportedCurrent evidence covers the defined cohort and window, meets the primary threshold, and satisfies the guardrailsThat implementation remains complete or the causal explanation is universally true
rejectedCurrent evidence misses the primary threshold or violates a required guardrailThat the underlying actor problem never existed
inconclusiveEvidence exists but cannot support or reject the hypothesis because of power, exposure, attribution, quality, or guardrail gapsThat value is zero or that more measurement will resolve the ambiguity
staleEarlier evidence was invalidated by its freshness rule or a material change to the cohort, capability, measure, environment, or hypothesisA current worth decision

Use the hypothesis's declared threshold, window, cohort, and guardrails. Never rewrite them to fit observed data. If a causal claim is material but the evidence cannot distinguish the capability's effect from confounders, classify it inconclusive, not supported.

When Grounding records outcome hypotheses as not applicable for an authoritative obligation, carry its scope and reason into the trace summary but do not create an outcome-evidence record. Completion evidence remains required.

Use this linked record:

text
Outcome evidence: <hypothesis slug>
Hypothesis version: <canonical source and version>
Applies to requirements: <requirement slugs>
Observation identity: <dataset, query, study, experiment, or run reference>
Cohort and exposure: <who was measured and evidence they used the capability>
Measurement window: <start/end or bounded period>
Baseline: <value and source, or unavailable + limitation>
Observed result: <primary measure result>
Threshold evaluation: met | missed | not-evaluable
Guardrail results: <each guardrail result or missing>
Comparison or attribution: <method, or unavailable + limitation>
Freshness: current | stale — <rule or invalidating change>
Evidence state: unmeasured | supported | rejected | inconclusive | stale
Confidence: low | medium | high
Owner: <evidence owner>
Next measurement or revisit: <trigger, date, or none + reason>

The evidence state is a projection over an immutable hypothesis version, not a rewrite of canonical meaning. A new threshold, cohort, measure, or causal claim creates a new hypothesis version and invalidates dependent assessments.

Workflow

  1. Resolve the canonical requirement set, version, and admitted increment.
  2. Inventory changed or planned artifacts at public boundaries: contracts, commands, events, exports, routes, domain rules, migrations, domain and data model views, tests, ADRs, runbooks, and deployment evidence.
  3. Reuse stable requirement and criterion IDs; resolve aliases only through the canonical lineage model.
  4. Place the smallest useful anchor in the artifact a maintainer will inspect first.
  5. Map each criterion to implementation and verification evidence separately.
  6. When linked outcome hypotheses are in scope, map each hypothesis version to its measurement plan and current observation; evaluate exposure, threshold, guardrails, attribution limits, and freshness separately from completion.
  7. Reverse-check each non-trivial changed artifact for a requirement ID or an explicit platform, operations, technical-debt, spike, or product-gap or outcome-evidence rationale.
  8. Classify gaps, stale references, and stale outcome assessments.
  9. Run the repository's blocking trace check and the closest behavior tests.
  10. Emit the trace decision and unresolved owners before closeout.

Anchor Placement

ArtifactPreferred stable anchor
Contract or public APIRequirement ID in schema metadata, operation metadata, or contract test
Domain behaviorNamed rule/function plus a criterion-tagged behavior test
UI or workflowStable route/action/state identifier plus acceptance test
Data or migrationSchema/migration identifier plus compatibility or migration test
Domain or data model viewStable entity/node identifier plus the requirement IDs that authorize it
ADR or architecture recordRequirement IDs in the decision context
AspectRequirement ID on the mechanism subsystem plus the coverage check that enumerates the governed set
OperationsRunbook/check identifier plus revision, environment, run, and outcome
Issue or change recordCanonical IDs, admitted increment, evidence links, and named gaps

Do not add long requirement prose to source files when the ID and a nearby contract or test already preserve the relationship.

Formal Completion Records

Formalize completion in the project's natural issue or change record. Do not rewrite canonical requirement meaning, approval, or readiness state merely to signal that delivery finished. Completion evidence, release state, and outcome evidence remain independent.

Close the record only when it preserves:

  • the bounded canonical requirement and criterion IDs;
  • the completion boundary, such as acceptance, release, or production;
  • the exact revision, artifact, environment, and executed run evidence;
  • implementation and verification anchors;
  • evidence freshness when the operational proof can expire; and
  • named gaps, deferrals, and owners without implying a broader completion state.

Use repository markers according to the repository's existing convention:

  • an immutable checkpoint or tag is optional when the exact source snapshot matters; its name must not imply release, production, or outcome proof that the evidence does not establish;
  • a milestone groups multiple work items toward a shared objective; do not invent one for a single closeout unless the project explicitly uses that convention; and
  • keep unresolved release or production work separate from an accepted increment.
text
Completion record:
- Subject: <capability or bounded increment>
- Decision: TRACEABLE | PARTIAL | BLOCKED
- Canonical scope: <requirement and criterion IDs>
- Completion boundary: <acceptance | release | production | other>
- Revision/artifact/environment: <immutable identities>
- Implementation: <stable anchors>
- Executed evidence: <run identities and results>
- Evidence freshness: <current through date, stale, or not applicable>
- Gaps/deferred: <gap, owner, and follow-up record>
- Outcome status: <linked states or not assessed>
- Repository marker: <change record; optional checkpoint; aggregate milestone>

Gap Taxonomy

GapMeaning
missing-requirementWork may be justified but has no canonical requirement or accepted rationale
missing-implementationRequirement exists but no implementation anchor covers it
missing-testImplementation exists but no executable verification covers it
aspect-uncoveredThe aspect's coverage check is absent, unexecuted, or reports a governed subsystem without the mechanism for the current revision
stale-referenceAnchor targets an unknown, replaced, or removed ID or criterion
scope-deferredWork is intentionally later and cites an accepted deferral
decision-blockedAn unresolved product, policy, platform, or ownership decision blocks proof
evidence-unreproducibleClaimed evidence lacks revision, run, environment, or result identity
outcome-unmeasuredA decision-relevant hypothesis has no accepted observation
outcome-staleOutcome evidence no longer satisfies its freshness rule
outcome-not-evaluableThreshold, exposure, window, guardrail, or attribution evidence is insufficient
representation-aggregatedOne artifact carries several canonical concepts because they share a write or release unit
representation-derivedA concept exists only as a computation; the computation's source is named
representation-projectedA concept is rebuilt from another artifact's records rather than stored

CI Enforcement

Put deterministic trace checks in the earliest capable blocking gate:

  • validate anchor syntax while authoring when editor tooling can do so;
  • reject unknown IDs, criteria, aliases, and stale references in static CI;
  • reject a representation gap that names no expiry condition;
  • reject a deprecated-criterion record that names no reviewed implementation anchors or no accountability reference;
  • list lapsing criteria beside pending ones, and reject a lapsing record that names no expiry condition;
  • require criterion-tagged tests to exist before accepting test-result tags;
  • reject an aspect whose coverage check is absent, unexecuted, or reports a governed subsystem without the mechanism for the current revision;
  • ingest executed test results before promoting implemented to verified;
  • require operational evidence to carry revision and run identity;
  • reject unknown hypothesis IDs and outcome assessments that omit the hypothesis version, observation identity, window, threshold evaluation, or freshness;
  • invalidate current outcome assessments when their hypothesis version or declared freshness inputs change;
  • report coverage counts, but never convert a percentage target into invented requirement meaning.

Keep fast local checks for feedback and a full-repository CI backstop for unbypassable coverage. Automation may validate links, record shape, and explicit freshness; it cannot manufacture causal validity from structurally complete fields. Route gate placement through defect-shift-left.

Output Contract

text
Subject:             <change, increment, release, or requirement scope>
Decision:            TRACEABLE | PARTIAL | BLOCKED
Canonical source:    <requirement set and version>
Requirement IDs:     <IDs covered>
Implemented:         <criteria count and anchors>
Verified:            <criteria count and executed evidence>
Outcome hypotheses:  <IDs + unmeasured/supported/rejected/inconclusive/stale/not-applicable>
Outcome evidence:    <current measurement links, or none>
Reverse-trace gaps:  <changed artifacts without requirement/rationale>
Stale references:    <none or exact references>
Representation gaps: <class, artifact, and expiry condition, or none>
Other gaps:          <gap taxonomy entries and owners>
Next action:         <implement, test, decide, repair reference, or close>
Verification:        <trace check and tests run, or Not run + reason>

For implementation or review closeout, append a compact note:

text
Trace:
- Source: <requirement set, issue, or admitted increment>
- IDs: <requirement and criterion IDs>
- Implementation: <stable artifacts>
- Evidence: <executed tests or operational results>
- Outcomes: <hypothesis IDs, evidence states, freshness, and observation links>
- Gaps/deferred: <none or named gaps; representation gaps carry expiry>

Guardrails

  • Do not mark a requirement verified because code, a route shell, a fixture, or a test definition exists.
  • Do not tag a test with a criterion it does not assert; when a criterion's wording or mechanism changes, re-verify every test tagged to it.
  • Do not use issue text as canonical when it conflicts with requirements.
  • Do not hide unrelated work under a convenient requirement ID.
  • Do not duplicate every trace edge in a giant registry and nearby artifacts.
  • Do not use completion, deployment, adoption, or telemetry presence alone as evidence that an outcome hypothesis is supported.
  • Do not change a hypothesis threshold, cohort, window, or guardrail in an evidence record; return meaning changes to requirements-grounding.
  • Do not issue BUILD, KEEP, SIMPLIFY, DROP, or removal decisions; hand current outcome evidence to functionality-complexity-tradeoff.
  • Do not recycle IDs; follow topology lineage after splits, merges, and replacements.
  • If grounding, topology, or readiness changes materially, invalidate or refresh affected trace decisions.

See also

  • requirements-grounding — requirement meaning, source authority, and evidence.
  • functionality-complexity-tradeoff — worth decisions using current outcome evidence.
  • requirements-topology — stable IDs, lineage, graph semantics, and repository gates.
  • implementation-readiness — admitted implementation increment and verification obligations.
  • defect-shift-left — earliest blocking placement for trace checks.

Frequently asked questions

What does the Requirements Traceability AI skill do?

Maintains bidirectional traceability between canonical requirements and implementation, verification, decision, operational, and outcome evidence. Use when planning, implementing, reviewing, or closing work that must prove which requirement authorized a change, where it is implemented, which executed evidence verifies it, whether a linked outcome hypothesis is supported by current measurement, what remains unmapped, or whether references have gone stale. Do not use to invent requirement or hypothesis meaning, normalize the requirement graph, decide implementation readiness, or issue a funct...

Why use Requirements Traceability on TypingMind?

Because you install it once and use it with any model. Requirements Traceability is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Requirements Traceability in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/l-gevity/l-gevity-skills/tree/main/.agents/skills/requirements-traceability. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Requirements Traceability?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Requirements Traceability?

As many as you like. As long as a model supports skills, you can use Requirements Traceability with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Requirements Traceability AI skill free?

Yes. It is published on GitHub by l-gevity under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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